250 Days Complete Roadmap for Data Science After Learning Python

Directory:

Days n - 0: Learn Python (SQL is also recommended). ↓

Days 1 - 10: Learn Data Analysis Libraries NumPy and Pandas [Easy]. ↓

Days 11 - 20: Learn Data Visualization Libraries (Matplotlib, Seaborn, Plotly, etc) [Easy]. ↓

Days 21 - 30: Learn Machine Learning (ML) and Basic algorithms (Scikit-Learn) [Medium]. ↓

Days 31 - 40: Learn Deep Learning and Neural Networks (Keras) [Medium]. ↓

Days 41 - 50: Learn Advanced Machine Learning (ML) (Scikit-Learn) [Hard]. ↓

Days 51 - 60: Learn Natural Language Processing (NLP) (Scikit-Learn, NLTK, Spacy, etc) [Hard]. ↓

Days 61 - 70: Learn Time Series Analysis (TSA) (Pandas, statsmodels) [Hard]. ↓

Days 71 - 80: Learn Advanced Deep Learning (DL) (Keras, Tensorflow) [Medium]. ↓

Days 81 - 90: Learn Data Visualization Tools (PowerBI, Tableau) [Easy]. ↓

Days 91 - 100: Learn Linux and Shell Commands [Easy]. ↓

Days 101 - 110: Explore Different Databases (MySQL, PostgreSQL, PyMongo, etc) [Medium]. ↓

Days 111 - 130: Learn Big Data Tools "Hadoop Ecosystem" (Hadoop, Spark, Hive, etc) [Hard]. ↓

Days 131 - 150: Learn AWS, Microsoft Azure, or GCP (Based on your choice, we'll be using AWS) [Hard]. ↓

Days 151 - 160: Learn Extract, Transform, and Load (ETL) (Apache NiFI, AWS Glue) [Medium]. ↓

Days 161 - 180: Learn Development Frameworks (Flask and Django) [Medium]. ↓

Days 181 - 190: Learn Version Control (Git and GitHub) [Easy]. ↓

Days 191 - 210: Learn Containerization and Orchestration (Docker and Kubernetes) [Medium]. ↓

Days 211 - 220: Learn Advanced Natural Language Processing (NLP) (Tensorflow, Gensim, LIME, etc) [Hard]. ↓

Days 221 - 230: Learn Reinforcement Learning (RL) [Medium]. ↓

Days 231 - 240: Learn Computer Vision (Tensorflow, OpenCV, COLMAP, Scikit-Image, etc) [Medium]. ↓

Days 241 - 250: Learn CyberSecurity and Privacy [Medium]. ↓

Days 251 - n: You are an Industry Standard Data Scientist. ↓

Days n - 0: Learn Python (SQL is also recommended).

First, it is needed to learn Python and then follow this roadmap. SQL is also a valuable skill to have since databases play a crucial role in this roadmap.

Days 1 - 10: Learn Data Analysis Libraries NumPy and Pandas [Easy].

Day 1: Introduction to NumPy

Understand what NumPy is and why it's important for scientific computing and data analysis.
Learn about NumPy arrays and their properties, such as shape, size, and data type.
Practice creating and manipulating NumPy arrays using indexing, slicing, and broadcasting.

Day 2: Basic Operations with NumPy Arrays

Learn about basic mathematical operations with NumPy arrays, such as addition, subtraction, multiplication, and division.
Understand how to perform element-wise operations and matrix operations with NumPy arrays.
Practice implementing basic operations with NumPy arrays.

Day 3: Advanced Operations with NumPy Arrays

Learn about advanced operations with NumPy arrays, such as reshaping, stacking, and splitting arrays.
Understand how to perform linear algebra operations with NumPy arrays, such as matrix inversion, eigenvalues, and eigenvectors.
Practice implementing advanced operations with NumPy arrays.

Day 4: Introduction to Pandas

Understand what Pandas is and why it's important for data analysis.
Learn about Pandas data structures, such as Series and DataFrame, and their properties.
Practice creating and manipulating Pandas data structures.

Day 5: Data Manipulation with Pandas

Learn how to select, filter, and transform data in Pandas.
Understand how to handle missing data and duplicates in Pandas.
Practice manipulating data in Pandas.

Day 6: Data Aggregation and Grouping with Pandas

Learn how to group data in Pandas and perform aggregation functions, such as sum, mean, and count.
Understand how to apply multiple functions to grouped data and customize the output.
Practice aggregating and grouping data in Pandas.

Day 7: Time Series Analysis with Pandas

Learn how to work with time series data in Pandas, including creating time series data structures, resampling, and shifting.
Understand how to perform time-based calculations and aggregations.
Practice analyzing time series data using Pandas.

Day 8: Data Visualization with Pandas

Learn how to create basic visualizations with Pandas, including line plots, scatter plots, and histograms.
Understand how to customize visualizations and add labels, titles, and legends.
Practice creating visualizations with Pandas.

Day 9: Advanced Data Analysis with Pandas

Learn how to perform advanced data analysis tasks in Pandas, such as merging, joining, and pivoting data.
Understand how to apply statistical functions to data and perform hypothesis testing.
Practice analyzing data using Pandas.

Day 10: Real-world Applications with NumPy and Pandas

Apply your knowledge of NumPy and Pandas to real-world data analysis problems.
Practice importing and cleaning real-world data sets using Pandas.
Analyze and visualize the data using NumPy and Pandas, and draw insights from the results.

Days 11 - 20: Learn Data Visualization Libraries (Matplotlib, Seaborn, Plotly, etc) [Easy].

Day 11: Matplotlib Concepts and Techniques

Understanding the Figure and Axes objects in Matplotlib.
Data visualization with Matplotlib.
Using Matplotlib's animation and interactive plotting capabilities.
Customization of Matplotlib plots.
Tips and tricks for optimizing Matplotlib performance.

Day 12: Advanced Matplotlib Plotting Techniques

Advanced data visualization techniques with Matplotlib.
Creating complex plots with Matplotlib, such as contour and 3D plots.
Customizing the appearance of Matplotlib plots.
Techniques for working with large datasets with Matplotlib.
Advanced tips and tricks for improving performance with Matplotlib.

Day 13: Seaborn Concepts and Techniques

Using Seaborn for exploratory data analysis.
Working with different types of data using Seaborn.
Customization of Seaborn plots.
Creating complex visualizations with Seaborn.
Best practices for designing effective Seaborn visualizations.

Day 14: Advanced Seaborn Plotting Techniques

Techniques for visualizing complex datasets with Seaborn.
Creating visualizations with Seaborn, such as heatmaps and clustermaps.
Customizing Seaborn plots with advanced options.
Best practices for creating publication-quality Seaborn visualizations.
Working with large datasets in Seaborn.

Day 15: Plotting with Other Visualization Libraries

Introduction to other plotting libraries, such as Plotly and Bokeh.
Creating complex visualizations with these libraries.
Comparing and contrasting these libraries with Matplotlib and Seaborn.
Techniques for optimizing performance with other visualization libraries.
Using Plotly and Bokeh for interactive data visualization.

Day 16: Plotting with Plotly

Introduction to the Plotly library and its capabilities.
Creating visualizations with Plotly, such as scatter plots and bubble charts.
Customizing Plotly visualizations with advanced options.
Techniques for working with large datasets in Plotly.
Best practices for creating publication-quality Plotly visualizations.

Day 17: Plotting with Bokeh

Introduction to the Bokeh library and its capabilities.
Creating visualizations with Bokeh, such as interactive scatter plots and geographic maps.
Customizing Bokeh visualizations with advanced options.
Techniques for working with large datasets in Bokeh.
Best practices for creating publication-quality Bokeh visualizations.

Day 18: Plotting with Altair

Introduction to the Altair library and its capabilities.
Creating visualizations with Altair, such as layered visualizations and facet grids.
Customizing Altair visualizations with advanced options.
Techniques for working with large datasets in Altair.
Best practices for creating publication-quality Altair visualizations.

Day 19: Plotting with ggplot

Introduction to the ggplot library and its capabilities.
Creating visualizations with ggplot, such as layered visualizations and theme customization.
Customizing ggplot visualizations with advanced options.
Techniques for working with large datasets in ggplot.
Best practices for creating publication-quality ggplot visualizations.

Day 20: Data Visualization Workflows

Integrating advanced data visualization techniques with machine learning workflows.
Building end-to-end data visualization pipelines with Matplotlib, Seaborn, Plotly, Bokeh, Altair, or ggplot.
Advanced techniques for working with large and complex datasets in data visualization.
Best practices for scaling data visualization workflows to production environments.

Days 21 - 30: Learn Machine Learning (ML) and Basic algorithms (Scikit-Learn) [Medium].

Day 21: Introduction to Machine Learning

Understand the basics of machine learning and its applications.
Learn about the types of machine learning: supervised, unsupervised, and reinforcement learning.
Get an overview of the machine learning workflow and key terminology.

Day 22: Understand Scikit-Learn Framework and its syntax

Introduction to Scikit-Learn and why it is used for.
See Scikit-Learn syntax.

Day 23: Linear & Logistic Regression

Learn about the most basic type of supervised learning: linear regression.
Understand how to train a model to make predictions based on input features.
Practice implementing linear regression using Python's Scikit-Learn library.
Learn about another type of supervised learning: logistic regression.
Understand how to train a model to classify input data into different categories.
Practice implementing logistic regression using Scikit-Learn.

Day 24: k-Nearest Neighbors (k-NN)

Learn about the k-NN algorithm, which can be used for both classification and regression problems.
Understand how to choose the value of k and measure the performance of the model.
Practice implementing k-NN using Scikit-Learn.

Day 25: Decision Trees

Learn about decision trees, a type of algorithm used for classification and regression.
Understand how decision trees make predictions based on simple rules.
Practice implementing decision trees using Scikit-Learn.

Day 26: Random Forests

Learn about random forests, an ensemble learning method that combines multiple decision trees.
Understand how random forests can improve the accuracy and stability of the model.
Practice implementing random forests using Scikit-Learn

Day 27: Clustering

Learn about unsupervised learning and clustering algorithms.
Understand how to group similar data points together based on some similarity metric.
Practice implementing clustering algorithms using Scikit-Learn.

Day 28: Principal Component Analysis (PCA)

Learn about dimensionality reduction and the PCA algorithm.
Understand how PCA can be used to reduce the number of features in a dataset while preserving the most important information.
Practice implementing PCA using Scikit-Learn.

Day 29: Support Vector Machines (SVM)

Learn about SVM, a powerful algorithm used for both classification and regression.
Understand how SVM finds the best hyperplane that separates different classes of data.
Practice implementing SVM using Scikit-Learn.

Day 30: Neural Networks and Keras Library (Take a look, we are just going to discuss this in detail)

Learn about artificial neural networks, a popular machine-learning method inspired by the structure of the human brain.
Understand how neural networks can be used for a variety of tasks like image recognition and natural language processing.
Understand the Keras library and practice implementing neural networks using Keras.

Days 31 - 40: Learn Deep Learning and Neural Networks (Keras) [Medium].

Day 31: Introduction to Deep Learning and Neural Networks

Understand what Deep Learning is and how it differs from Machine Learning.
Learn about Neural Networks, their structure, and how they work.
Implement a simple Neural Network using Python and Keras.

Day 32: Single & Multilayer Perceptrons (MLPs)

Understand the architecture of MLPs.
Learn about activation functions, loss functions, and optimization techniques used in MLPs.
Understand the MNIST dataset.
Implement an MLP using Keras to classify images in the MNIST dataset.

Day 33: Convolutional Neural Networks (CNNs)

Understand the architecture of CNNs.
Learn about convolutional layers, pooling layers, and activation functions used in CNNs.
Implement a CNN using Keras to classify images in the CIFAR-10 dataset.

Day 34: Recurrent Neural Networks (RNNs)

Understand the architecture of RNNs.
Learn about different types of RNNs and their applications.
Implement an RNN using Keras to perform sentiment analysis on movie reviews.

Day 35: Autoencoders and Generative Models

Understand the architecture of Autoencoders and Generative Models.
Learn about different types of Autoencoders and Generative Models and their applications.
Implement an Autoencoder using Keras to perform image denoising.

Day 36: Transfer Learning and Fine-tuning

Understand the concept of Transfer Learning and Fine-tuning.
Learn how to use pre-trained models for transfer learning.
Implement transfer learning and fine-tuning using Keras on the VGG16 model for image classification.

Day 37: Object Detection and Segmentation

Understand the concept of Object Detection and Segmentation.
Learn about different architectures used for Object Detection and Segmentation.
Implement Object Detection and Segmentation using Keras on the Mask R-CNN model.

Day 38: Time Series Analysis (TSA) with Deep Learning (Take a look, we'll cover this in detail)

Understand the concept of Time Series Analysis and how it is different from other types of data.
Learn about different architectures used for Time Series Analysis.
Implement Time Series Analysis using Keras on a dataset of stock prices.

Day 39: Reinforcement Learning (RL) with Deep Learning (Take a look, we'll cover this in detail)

Understand the concept of Reinforcement Learning and how it differs from other types of Machine Learning.
Learn about different architectures used for Reinforcement Learning.
Implement Reinforcement Learning using Keras on the OpenAI Gym toolkit.

Day 40: Advanced Deep Learning Techniques (Take a look, we'll cover this in detail)

Understand advanced techniques used in Deep Learning, such as Attention Mechanisms, Adversarial Learning, and Reinforcement Learning.
Learn about recent advances in the field of Deep Learning

Days 41 - 50: Learn Advanced Machine Learning (ML) (Scikit-Learn) [Hard].

Day 41: Advanced Regression Techniques

Learn about advanced regression techniques like Ridge Regression, Lasso Regression, and ElasticNet Regression.
Understand how these techniques can be used to overcome overfitting and improve the performance of linear regression models.
Practice implementing these techniques using Scikit-Learn.

Day 42: Gradient Boosting

Learn about gradient boosting, an ensemble learning method that combines multiple weak models to make more accurate predictions.
Understand how gradient boosting can be used for both regression and classification problems.
Practice implementing gradient boosting using Scikit-Learn.
Implement an MLP using Keras to classify images in the MNIST dataset.

Day 43: Support Vector Machines (SVM) with Kernel Tricks

Learn about kernel tricks, a method for transforming data into a higher-dimensional space to make it more separable.
Understand how kernel tricks can be used to improve the performance of SVM models.
Practice implementing SVM with kernel tricks using Scikit-Learn.

Day 44: Naive Bayes

Learn about Naive Bayes, a probabilistic algorithm used for classification.
Understand how Naive Bayes makes predictions based on Bayes' theorem and assumes that features are independent of each other.
Practice implementing Naive Bayes using Scikit-Learn.

Day 45: Ensemble Methods

Learn about ensemble methods like bagging, boosting, and stacking.
Understand how these methods can be used to improve the accuracy and stability of machine learning models.
Practice implementing ensemble methods using Scikit-Learn.

Day 46: Clustering with Gaussian Mixture Models (GMM)

Learn about Gaussian Mixture Models, a type of clustering algorithm that models data as a mixture of Gaussian distributions.
Understand how GMM can be used to cluster data when the boundaries between clusters are not well-defined.
Practice implementing GMM using Scikit-Learn.

Day 47: Dimensionality Reduction with t-SNE

Learn about t-SNE, a technique for visualizing high-dimensional data in a lower-dimensional space.
Understand how t-SNE can be used to identify patterns and relationships in complex datasets.
Practice implementing t-SNE using Scikit-Learn.

Day 48: Decision Trees with Bagging and Boosting

Learn about how decision trees can be combined with ensemble methods like bagging and boosting to improve their performance.
Understand how bagging and boosting can reduce the variance and bias of decision trees, respectively.
Practice implementing decision trees with bagging and boosting using Scikit-Learn.

Day 49: Autoencoders

Learn about autoencoders, a type of neural network used for unsupervised learning and dimensionality reduction.
Understand how autoencoders can be used to compress data into a lower-dimensional space while preserving important information.
Practice implementing autoencoders using Keras.

Day 50: Convolutional Neural Networks (CNN)

Learn about convolutional neural networks, a type of neural network used for image recognition and computer vision tasks.
Understand how CNNs use convolutional layers to extract features from images and pooling layers to reduce their dimensionality.
Practice implementing CNNs using Keras.

Days 51 - 60: Learn Natural Language Processing (NLP) (Scikit-Learn, NLTK, Spacy, etc) [Hard].

Day 51: Introduction to NLP

Learn the basics of NLP, including what it is and why it's important.
Understand the different applications of NLP, such as sentiment analysis, text classification, and machine translation.
Explore some common NLP tasks and tools, such as tokenization, part-of-speech tagging, and named entity recognition.

Day 52: Regular Expressions and Text Preprocessing

Learn about regular expressions, a powerful tool for manipulating text data.
Understand how regular expressions can be used for tasks such as pattern matching, string substitution, and data validation.
Explore some common text preprocessing techniques, such as lowercasing, stemming, and stopword removal.

Day 53: Text Vectorization with Bag-of-Words (BoW)

Learn about bag-of-words (BoW), a common technique for representing text data as numerical vectors.
Understand how BoW works, including the processes of tokenization, counting, and normalization.
Practice implementing BoW using Python libraries such as Scikit-Learn and NLTK.

Day 54: Text Vectorization with TF-IDF

Learn about the term frequency-inverse document frequency (TF-IDF), another popular technique for text vectorization.
Understand how TF-IDF works to give higher weights to words that are more informative and less frequent.
Practice implementing TF-IDF using Python libraries such as Scikit-Learn and NLTK.

Day 55: Sentiment Analysis

Learn about sentiment analysis, a common NLP task that involves identifying the sentiment (positive, negative, or neutral) of a piece of text.
Understand how sentiment analysis can be performed using various techniques, such as rule-based methods, lexicon-based methods, and machine learning.
Practice implementing sentiment analysis using Python libraries such as TextBlob and Scikit-Learn.

Day 56: Text Classification

Learn about text classification, a task that involves assigning predefined categories or labels to pieces of text.
Understand how text classification can be performed using various techniques, such as Naive Bayes, logistic regression, and support vector machines.
Practice implementing text classification using Python libraries such as Scikit-Learn and NLTK.

Day 57: Named Entity Recognition (NER)

Learn about named entity recognition (NER), a task that involves identifying and classifying named entities such as people, organizations, and locations in text.
Understand how NER can be performed using various techniques, such as rule-based methods, statistical models, and neural networks.
Practice implementing NER using Python libraries such as NLTK and Spacy.

Day 58: Topic Modeling

Learn about topic modeling, a technique for discovering latent topics in a collection of text documents.
Understand how topic modeling can be performed using various techniques, such as Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF).
Practice implementing topic modeling using Python libraries such as Gensim and Scikit-Learn.

Day 59: Word Embeddings

Learn about word embeddings, a technique for representing words as dense numerical vectors in a high-dimensional space.
Understand how word embeddings can capture semantic and syntactic relationships between words and enable various NLP tasks such as semantic similarity and word analogy.
Practice implementing word embeddings using Python libraries such as Word2Vec and GloVe.

Day 60: Practical Applications of NLP

Explore some practical applications of NLP in the industry, such as chatbots, virtual assistants, and customer support.
Understand how NLP can be integrated into various software applications and systems, such as web scraping, data analytics, and natural language generation.
Practice implementing a complete NLP pipeline for a real-world problem using Python libraries such as Scikit-Learn, NLTK, and others.

Days 61 - 70: Learn Time Series Analysis (TSA) (Pandas, statsmodels) [Hard].

Day 61: Introduction to Time Series Analysis (TSA)

Learn the basics of time series analysis, including the definition of time series data, common types of time series data, and different methods for time series analysis.
Understand the difference between stationary and non-stationary time series, and how to identify them.
Practice loading and visualizing time series data using Python libraries such as Pandas and Matplotlib.

Day 62: Time Series Components

Learn about the different components of a time series, such as trend, seasonality, and noise.
Understand how to decompose a time series into its components and analyze them separately.
Practice decomposing time series using Python libraries such as statsmodels and Pandas.

Day 63: Autocorrelation and Partial Autocorrelation

Learn about autocorrelation and partial autocorrelation functions, which are used to identify the presence of correlation in time series data.
Understand how to interpret autocorrelation and partial autocorrelation plots, and how to use them to select appropriate models for TSA.
Practice analyzing autocorrelation and partial autocorrelation using Python libraries such as statsmodels and Pandas.

Day 64: Moving Averages and Exponential Smoothing

Learn about moving averages and exponential smoothing, which are simple methods for forecasting time series data.
Understand how to implement moving averages and exponential smoothing models in Python using libraries such as statsmodels and Pandas.
Practice forecasting time series data using moving averages and exponential smoothing models.

Day 65: ARIMA Models

Learn about ARIMA models, which are widely used for time series forecasting.
Understand how to select appropriate values for the P, D, and Q parameters in an ARIMA model, and how to interpret the output of an ARIMA model.
Practice implementing ARIMA models using Python libraries such as statsmodels and Pandas.

Day 66: Seasonal ARIMA Models

Learn about seasonal ARIMA models, which are used for time series forecasting when there is a seasonal component to the data.
Understand how to select appropriate values for the P, D, and Q parameters in a seasonal ARIMA model, and how to interpret the output of a seasonal ARIMA model.
Practice implementing seasonal ARIMA models using Python libraries such as statsmodels and Pandas.

Day 67: Vector Autoregression (VAR) Models

Learn about vector autoregression (VAR) models, which are used for multivariate TSA and forecasting.
Understand how to implement VAR models in Python using the statsmodels library, and how to interpret the output of a VAR model.
Practice analyzing and forecasting multivariate time series data using VAR models.

Day 68: Vector Error Correction (VEC) Models

Learn about vector error correction (VEC) models, which are used for cointegrated TSA and forecasting.
Understand how to implement VEC models in Python using the statsmodels library, and how to interpret the output of a VEC model.
Practice analyzing and forecasting cointegrated time series data using VEC models.

Day 69: Long Short-Term Memory (LSTM) Networks

Learn about long short-term memory (LSTM) networks, which are a type of recurrent neural network used for time series forecasting.
Understand how LSTM networks can be used to capture long-term dependencies in time series data, and how to implement LSTM networks in Python using the Keras library.
Practice building and training LSTM networks for time series forecasting.

Day 70: Deep Learning (DL) for Time Series Analysis (TSA)

Learn about other deep learning approaches for TSA, such as convolutional neural networks (CNNs) and autoencoders.
Understand how these models can be used for time series forecasting, anomaly detection, and pattern recognition tasks in various industries, including finance, healthcare, and energy.
Practice implementing these models using popular deep learning frameworks like TensorFlow and PyTorch, and learn how to tune hyperparameters and evaluate model performance.
Explore real-world applications of deep learning for TSA and discuss best practices for implementing these models in production environments.

Days 71 - 80: Learn Advanced Deep Learning (DL) (Keras, Tensorflow) [Medium].

Day 71: Introduction to Advanced Deep Learning Techniques

Learn about advanced deep learning techniques such as Generative Adversarial Networks (GANs), Reinforcement Learning, and Transfer Learning.
Understand the importance and applications of these techniques in the field of deep learning.

Day 72: Generative Adversarial Networks (GANs)

Understand the architecture of GANs and how they are used for image generation, video generation, and text generation.
Learn about different loss functions and optimization techniques used in GANs.
Implement a GAN using Keras to generate images.

Day 73: Transfer Learning and Fine-tuning for Advanced Applications

Learn about advanced transfer learning techniques such as domain adaptation and one-shot learning.
Understand how to fine-tune pre-trained models for specific applications.
Implement transfer learning and fine-tuning using Keras on the InceptionV3 model for image classification.

Day 74: Attention Mechanisms in Deep Learning

Understand the concept of attention mechanisms in deep learning and their applications.
Learn about different types of attention mechanisms such as self-attention and multi-head attention.
Implement an attention-based model using Keras for natural language processing (NLP).

Day 75: Advanced Optimization Techniques for Deep Learning

Learn about advanced optimization techniques such as second-order optimization and adaptive learning rates.
Understand the importance of these techniques in training deep neural networks.
Implement these optimization techniques using TensorFlow on a dataset of handwritten digit recognition.
Implement these optimization techniques using TensorFlow on a dataset of handwritten digit recognition.

Day 76: Advanced Architectures for Image Classification

Learn about advanced convolutional neural network architectures such as ResNet, Inception, and DenseNet.
Understand the differences between these architectures and how they can be used for image classification tasks.
Implement these architectures using Keras on the CIFAR-100 dataset.

Day 77: Advanced Architectures for Natural Language Processing (NLP)

Learn about advanced recurrent neural network architectures such as LSTM, GRU, and Attention-based models.
Understand how these architectures can be used for text classification, sentiment analysis, and machine translation.
Implement these architectures using Keras on a dataset of movie reviews.

Day 78: Advanced Techniques for Reinforcement Learning

Learn about advanced techniques in reinforcement learning such as actor-critic methods, policy gradients, and value-based methods.
Understand how these techniques can be used for complex tasks such as game playing and robotics.
Implement these techniques using TensorFlow on the OpenAI Gym toolkit.

Day 79: Advanced Techniques for Time Series Analysis (TSA)

Learn about advanced techniques for time series analysis such as Long Short-Term Memory (LSTM) networks, WaveNet, and Temporal Convolutional Networks (TCNs).
Understand how these techniques can be used for time series forecasting and anomaly detection.
Implement these techniques using Keras on a dataset of energy consumption.

Day 80: Advanced Adversarial Learning

Learn about advanced adversarial learning techniques such as adversarial training, adversarial examples, and adversarial autoencoders.
Understand the importance and applications of these techniques in the field of deep learning.
Implement these techniques using TensorFlow on the MNIST dataset.

Days 81 - 90: Learn Data Visualization Tools (PowerBI, Tableau) [Easy].

Day 81: Introduction to Power BI

Understand the basics of Power BI and its advantages for data science.
Learn how to download and install Power BI Desktop.
Explore the Power BI interface and its components, such as data sources, visuals, and reports.
Practice loading data into Power BI and creating basic visualizations.

Day 82: Power BI Data Modeling

Learn how to build a data model in Power BI.
Understand the difference between DirectQuery and Import modes.
Practice transforming and shaping data using the Query Editor.
Explore the relationship view and how to create relationships between tables.

Day 83: Power BI DAX Functions

Learn about Data Analysis Expressions (DAX) functions in Power BI.
Understand how to create calculated columns and measures using DAX.
Practice using common DAX functions, such as CALCULATE, FILTER, and SUMX.
Explore advanced DAX concepts, such as time intelligence and context.

Day 84: Power BI Advanced Visualizations

Learn how to create advanced visualizations in Power BI, such as scatter charts, funnel charts, and custom visuals.
Understand how to use conditional formatting and data bars to highlight data.
Practice creating interactive visualizations using drill-down and drill-through features.
Explore Power BI's AI capabilities, such as Q&A and Quick Insights.

Day 85: Introduction to Tableau

Understand the basics of Tableau and its advantages for data science.
Learn how to download and install Tableau Desktop.
Explore the Tableau interface and its components, such as worksheets, dashboards, and stories.
Practice loading data into Tableau and creating basic visualizations.

Day 86: Tableau Data Modeling

Learn how to build a data model in Tableau.
Understand the difference between live and extract data connections.
Practice transforming and shaping data using Tableau's data preparation features.
Explore the relationship view and how to create joins and blends between tables.

Day 87: Tableau Calculations and LOD Expressions

Learn about calculations and Level of Detail (LOD) expressions in Tableau.
Understand how to create calculated fields and table calculations using Tableau's formula language.
Practice using common functions, such as IF, MIN, MAX, and WINDOW.
Explore advanced concepts, such as LOD expressions and context filters.

Day 88: Tableau Advanced Visualizations

Learn how to create advanced visualizations in Tableau, such as heat maps, treemaps, and box-and-whisker plots.
Understand how to use groups, sets, and parameters to customize visualizations.
Practice creating interactive visualizations using actions and filters.
Explore Tableau's mapping capabilities and how to create geographic visualizations.

Day 89: Integration with Other Tools

Learn how to integrate PowerBI and Tableau with other tools, such as Excel, Python, and R.
Understand how to use Power Query and Tableau Prep for data preparation and cleaning.
Practice integrating PowerBI and Tableau with other tools and platforms.

Day 90: Project and Case Study

Apply the skills and knowledge learned throughout the course to a real-life project or case study.
Practice creating a comprehensive data analysis and visualization solution using PowerBI and/or Tableau.

Days 91 - 100: Learn Linux and Shell Commands [Easy].

Day 91: Introduction to Linux

Understand the basics of the Linux operating system and its advantages for data science.
Understand a real-life use case with a project example of the Linux Operating System with Data Science
Learn about the different distributions of Linux, such as Ubuntu, CentOS, and Debian.
Install a Linux distribution on a virtual machine or as a dual boot on your computer.

Day 92: Linux File System

Learn about the Linux file system structure and how it is organized.
Understand the differences between files and directories.
Use commands like cd, ls, pwd, mkdir, and rmdir to navigate and manipulate the file system.

Day 93: Linux Shell

Understand the different types of shells available in Linux, such as Bash, Korn, and C shell.
Practice basic shell commands like echo, cat, head, tail, grep, and awk.

Day 94: Linux Text Editors

Learn about the different text editors available in Linux, such as Vi, Vim, and Nano.
Understand how to open, edit, save, and exit files using these text editors.
Practice basic commands like moving the cursor, searching, and replacing text.

Day 95: Linux Networking

Learn about networking in Linux.
Understand the different types of network configurations, such as IP addresses, DNS, and firewalls.
Practice basic networking commands like ping, traceroute, netstat, and ifconfig.

Day 96: Linux Packages and Libraries

Learn about Linux packages and libraries.
Understand the differences between packages and libraries.
Learn how to install, update, and remove packages and libraries using command line tools like apt-get and yum.

Day 97: Linux Shell Scripting

Learn about shell scripting in Linux.
Understand the different scripting languages available in Linux, such as Bash and Python.
Practice basic scripting commands like variables, loops, and conditional statements in a Linux environment.

Day 98: Linux Permissions and Security

Learn about Linux permissions and security.
Understand the different types of permissions available in Linux, such as read, write, and execute.
Practice basic commands like chmod and chown to manage permissions and ownership of files and directories.

Day 99: Linux System Administration

Learn about What is Linux system administration.
Understand the different system administration tasks, such as user management, process management, and system monitoring.
Practice basic administration commands like adduser, kill, top, and df.

Day 100: Linux for Data Science Tools

Learn about the different Linux tools available for data science, such as R, Python, Jupyter Notebooks, and Hadoop.
Understand how to install and configure these tools on Linux.
Practice using these tools to perform data analysis and machine learning tasks on Linux.

Days 101 - 110: Explore Different Databases (MySQL, PostgreSQL, PyMongo, etc) [Medium].

Day 101: Introduction to Databases

Overview of databases and their importance in data science.
Understanding different types of databases (relational, non-relational, graph, etc.).
Data modeling and schema design.
Query optimization techniques.
An introduction to ACID properties.

Day 102: MySQL

Introduction to MySQL and its features.
Installing MySQL on your local machine.
Creating databases and tables in MySQL.
Performing basic CRUD operations using SQL queries.
Working with joins, subqueries, and aggregate functions.
Importing and exporting data from MySQL.

Day 103: PostgreSQL

Introduction to PostgreSQL and its features.
Installing PostgreSQL on your local machine.
Creating databases and tables in PostgreSQL.
Performing basic CRUD operations using SQL queries.
Working with joins, subqueries, and aggregate functions.
Postgres-specific data types and operators.

Day 104: Oracle

Introduction to Oracle and its features.
Installing Oracle on your local machine.
Creating databases and tables in Oracle.
Performing basic CRUD operations using SQL queries.
Working with joins, subqueries, and aggregate functions.
Oracle-specific features and functions.

Day 105: MongoDB

Introduction to MongoDB and its features.
Installing MongoDB on your local machine.
Creating databases and collections in MongoDB.
Performing basic CRUD operations using MongoDB queries.
Understanding the BSON format.
Working with MongoDB Aggregation Framework.

Day 106: PyMongo

Introduction to PyMongo and its features.
Installing PyMongo on your local machine.
Creating databases and collections in MongoDB using PyMongo.
Performing basic CRUD operations using PyMongo.
Understanding the BSON format in PyMongo.
Working with MongoDB Aggregation Framework using PyMongo.
Querying MongoDB data using PyMongo.
Creating indexes in MongoDB using PyMongo.

Day 107: Cassandra

Introduction to Cassandra and its features.
Installing Cassandra on your local machine.
Creating keyspaces and tables in Cassandra.
Performing basic CRUD operations using CQL queries.
Understanding Cassandra's data model and architecture.
Working with consistency levels in Cassandra.

Day 108: Neo4j

Introduction to Neo4j and its features.
Installing Neo4j on your local machine.
Creating nodes, relationships, and properties in Neo4j.
Querying the graph database using Cypher language.
Understanding Neo4j data model and architecture.
Working with indexes and constraints in Neo4j.

Day 109: AWS Databases (We'll cover AWS in detail)

Overview of different databases available on AWS.
Creating and managing RDS instances (MySQL, PostgreSQL, Oracle, SQL Server, Aurora).
Setting up DynamoDB tables.
Working with Redshift data warehouse.
Overview of other NoSQL databases available on AWS (DocumentDB, Cassandra, etc.).

Day 110: Usage of Databases in Data Science

Understanding Data Acquisition and Storage Requirements for Different Data Types and Sources.
Choosing Appropriate Database Technologies Based on Data Size, Complexity, and Analysis Requirements.
Integration of Databases with Data Science Workflows and Tools.
Data Cleaning, Transformation, and Processing using Databases.
Query Optimization Techniques for Large-Scale Data Analysis.
Introduction to Database Architectures for Big Data Processing, such as Hadoop and Spark.
Case Studies of Successful Data Science Projects that Utilized Databases.

Days 111 - 130: Learn Big Data Tools "Hadoop Ecosystem" (Hadoop, Spark, Hive, etc) [Hard].

Day 111: Introduction to Hadoop Ecosystem

Introduction to Hadoop Ecosystem and its components.
Hadoop Distributed File System (HDFS) architecture and commands.
Overview of MapReduce and its role in the Hadoop Ecosystem.
Introduction to YARN and its role in Hadoop Ecosystem.
Hadoop Cluster setup using Hortonworks Data Platform (HDP) Sandbox.
Accessing HDP Sandbox using Web UI (Ambari) and CLI (Command Line Interface).

Day 112: Hadoop Distributed File System (HDFS)

HDFS architecture and components.
HDFS commands for file system operations (creating directories, moving files, deleting files, etc.).
HDFS commands for data operations (copying files to/from the local file system, listing files, etc.).
HDFS permissions and access control.
Practice uploading data to HDFS using CLI commands and Web UI (Ambari).

Day 113: MapReduce Basics

Introduction to MapReduce framework and its role in Hadoop Ecosystem.
MapReduce phases (map, shuffle, reduce) and their functionalities.
Anatomy of a MapReduce job.
Writing a simple MapReduce job using Python.
Practice running the MapReduce job on a small dataset using CLI commands and Web UI (Ambari).

Day 114: Pig

Introduction to Pig and its role in Hadoop Ecosystem.
Pig Latin language and its syntax.
Pig data types and operators.
Writing Pig scripts for data processing and analysis.
Practice running Pig scripts on a small dataset using CLI commands and Web UI (Ambari).

Day 115: Hive and HiveQL

Introduction to Hive and its role in Hadoop Ecosystem.
HiveQL language and its syntax.
Creating and querying tables in Hive.
Hive data types and operators.
Practice creating and querying tables in Hive using CLI commands and Web UI (Ambari).

Day 116: HBase

Introduction to HBase and its role in Hadoop Ecosystem.
HBase architecture and components.
HBase data model and its schema design.
Creating and querying tables in HBase.
Practice creating and querying tables in HBase using CLI commands and Web UI (Ambari).

Day 117: Sqoop

Introduction to Sqoop and its role in Hadoop Ecosystem.
Sqoop commands for importing and exporting data between Hadoop and other relational databases.
Importing data from a MySQL database to HDFS using Sqoop.
Exporting data from HDFS to a MySQL database using Sqoop.
Practice importing and exporting data using Sqoop CLI commands and Web UI (Ambari).

Day 118: Flume

Introduction to Flume and its role in Hadoop Ecosystem.
Flume architecture and components.
Flume agents and sources.
Writing Flume configuration files.
Practice collecting and transferring log data using Flume CLI commands and Web UI (Ambari).

Day 119: Oozie

Introduction to Oozie and its role in Hadoop Ecosystem.
Oozie workflow and its components.
Creating and running Oozie workflows using XML configuration files.
Practice creating and running Oozie workflows for data processing using CLI commands and Web UI (Ambari).

Day 120: Spark

Introduction to Apache Spark and its role in Hadoop Ecosystem.
Spark architecture and components.
Spark RDD (Resilient Distributed Datasets) and its operations.
Writing a simple Spark program using Python.
Practice running Spark programs on a small dataset using CLI commands and Web UI (Ambari).

Day 121: Kafka

Introduction to Apache Kafka and its role in Hadoop Ecosystem.
Kafka architecture and components.
Kafka producers and consumers.
Writing Kafka configuration files.
Practice collecting and processing streaming data using Kafka CLI commands and Web UI (Ambari).

Day 122: NiFi

Introduction to Apache NiFi and its role in Hadoop Ecosystem.
NiFi architecture and components.
NiFi processors and controllers.
Writing NiFi configuration files.
Practice collecting, transforming, and routing data using NiFi CLI commands and Web UI (Ambari).

Day 123: Flink

Introduction to Apache Flink and its role in Hadoop Ecosystem.
Flink architecture and components.
Flink data streams and batch processing.
Writing Flink programs using Java or Scala. (PyFlink for Python)
Practice running Flink programs on a small dataset using CLI commands and Web UI (Ambari).

Day 124: Beam

Introduction to Apache Beam and its role in Hadoop Ecosystem.
Beam architecture and components.
Beam programming model and its API.
Writing Beam programs using Java or Python.
Practice running Beam programs on a small dataset using CLI commands and Web UI (Ambari).

Day 125: Storm

Introduction to Apache Storm and its role in Hadoop Ecosystem.
Storm architecture and components.
Storm spouts and bolts.
Writing Storm topologies using Java or Python.
Practice running Storm topologies on a small dataset using CLI commands and Web UI (Ambari).

Day 126: Kylin

Introduction to Apache Kylin and its role in Hadoop Ecosystem.
Kylin architecture and components.
Kylin cube and its design.
Creating and querying Kylin cubes using SQL-like queries.
Practice creating and querying Kylin cubes using CLI commands and Web UI (Ambari).

Day 127: Atlas

Introduction to Apache Atlas and its role in Hadoop Ecosystem.
Atlas architecture and components.
Atlas entities and relationships.
Writing Atlas metadata using REST API.
Practice managing metadata using Atlas CLI commands and Web UI (Ambari).

Day 128: Zeppelin

Introduction to Apache Zeppelin and its role in Hadoop Ecosystem.
Zeppelin architecture and components.
Zeppelin notebooks and its visualization features.
Writing Zeppelin notebooks using Scala or Python.
Practice running Zeppelin notebooks on a small dataset using CLI commands and Web UI (Ambari).

Day 129: Ranger

Introduction to Apache Ranger and its role in Hadoop Ecosystem.
Ranger architecture and components.
Ranger policies and its authorization features.
Writing Ranger policies using REST API.
Practice managing policies using Ranger CLI commands and Web UI (Ambari).

Day 130: Hadoop Security

Introduction to Hadoop Security and its components.
Kerberos authentication and authorization.
Hadoop Secure Mode configuration.
Practice configuring and managing Hadoop Security using CLI commands and Web UI (Ambari).

Days 131 - 150: Learn AWS, Microsoft Azure, or GCP (Based on your choice, we'll be using AWS) [Hard].

Day 131: Introduction to AWS

Overview of cloud computing and AWS services.
AWS global infrastructure.
AWS console, command line interface (CLI), and software development kits (SDKs).
IAM (Identity and Access Management) and security best practices.
Setting up an AWS account and configuring AWS CLI.
Practice launching an EC2 (Elastic Compute Cloud) instance using CLI and web console.

Day 132: S3 (Simple Storage Service)

Introduction to S3 and its features.
S3 storage classes and pricing.
S3 buckets and objects.
S3 security and access control.
Practice creating an S3 bucket, uploading files, and configuring permissions.

Day 133: EC2 (Elastic Compute Cloud)

EC2 instances and instance types.
EC2 security groups and networking.
EBS (Elastic Block Store) volumes and snapshots.
Elastic IP addresses and elastic network interfaces.
Practice launching and configuring an EC2 instance, attaching an EBS volume, and assigning an elastic IP address.

Day 134: RDS (Relational Database Service)

Introduction to RDS and its features.
RDS database engines (MySQL, PostgreSQL, Oracle, etc.).
RDS instance types and sizing.
RDS backups, snapshots, and replication.
Practice creating and configuring an RDS instance, connecting to it, and importing data.

Day 135: DynamoDB

Introduction to DynamoDB and its features.
DynamoDB tables, items, and attributes.
DynamoDB capacity modes and pricing.
DynamoDB indexes (primary, secondary, and global).
Practice creating a DynamoDB table, inserting and querying data, and creating indexes.

Day 136: Redshift

Introduction to Redshift and its features.
Redshift clusters and node types.
Redshift data warehousing and SQL.
Redshift performance tuning and best practices.
Practice launching a Redshift cluster, loading data, and running SQL queries.

Day 137: Data Pipeline

Introduction to AWS Data Pipeline and its features.
Data Pipeline components (data sources, activities, and destinations).
Data Pipeline scheduling and orchestration.
Data Pipeline security and access control.
Practice creating and configuring a simple data pipeline.

Day 138: EMR (Elastic MapReduce)

Introduction to EMR and its features.
EMR cluster types (Hadoop, Spark, Presto, etc.).
EMR security and access control.
EMR storage options (S3, HDFS, HBase, etc.).
Practice launching and configuring an EMR cluster, running a Hadoop or Spark job.

Day 139: Lambda

Introduction to AWS Lambda and its features.
Lambda functions and event sources.
Lambda runtimes and programming languages.
Lambda security and access control.
Practice creating and deploying a simple Lambda function.

Day 140: Athena

Introduction to Athena and its features.
Athena database and table structures.
Athena data sources (S3, JDBC, etc.).
Athena queries and result sets.
Practice running queries in Athena and analyzing results.

Day 141: CloudFormation

Introduction to AWS CloudFormation and its features.
CloudFormation templates and stacks.
CloudFormation resources and parameters.
Practice creating and deploying a CloudFormation stack to launch an EC2 instance and associated resources.

Day 142: Kinesis

Introduction to Kinesis and its features.
Kinesis data streams and data analytics.
Kinesis Firehose and data delivery.
Kinesis Video Streams and real-time video processing.
Practice creating and processing data streams using Kinesis CLI commands and Web UI.

Day 143: Elastic Beanstalk

Introduction to Elastic Beanstalk and its features.
Elastic Beanstalk environments and application versions.
Elastic Beanstalk platforms and languages.
Elastic Beanstalk monitoring and scaling.
Practice deploying an application using Elastic Beanstalk CLI and web console.

Day 144: API Gateway

Introduction to AWS API Gateway and its features.
API Gateway deployment and management.
API Gateway security and authorization.
API Gateway integrations and endpoints.
Practice creating and testing an API Gateway REST API.

Day 145: CloudWatch

Introduction to AWS CloudWatch and its features.
CloudWatch metrics and alarms.
CloudWatch logs and log groups.
CloudWatch dashboards and visualizations.
Practice monitoring and analyzing system metrics using CloudWatch CLI and web console.

Day 146: SNS (Simple Notification Service)

Introduction to SNS and its features.
SNS topics and subscriptions.
SNS message filtering and delivery.
SNS mobile push notifications.
Practice creating and sending notifications using SNS CLI and web console.

Day 147: SES (Simple Email Service)

Introduction to SES and its features.
SES email sending and receiving.
SES email templates and content.
SES bounce and complaint handling.
Practice sending and receiving emails using SES CLI and web console.

Day 148: Step Functions

Introduction to AWS Step Functions and its features.
Step Functions state machines and workflows.
Step Functions AWS service integrations.
Step Functions error handling and retries.
Practice creating and testing a simple Step Functions workflow.

Day 149: Glue (We'll cover ETL in detail)

Introduction to AWS Glue and its features.
Glue ETL (Extract, Transform, Load) jobs.
Glue data catalog and schema discovery.
Glue crawlers and classifiers.
Practice creating and running a Glue ETL job to transform data from S3 to RDS.

Day 150: SageMaker

Introduction to AWS SageMaker and its features.
SageMaker training and hosting.
SageMaker algorithms and models.
SageMaker notebooks and model tuning.
Practice training and deploying a machine learning model using SageMaker CLI and web console.

Days 151 - 160: Learn Extract, Transform, and Load (ETL) (Apache NiFI, AWS Glue) [Medium].

Day 151: Introduction to ETL and Data Warehousing

Overview of ETL process and its importance in data warehousing.
Understanding of different data warehouse architectures: star schema, snowflake schema, and data vault.
Familiar with popular ETL tools and technologies: Apache NiFi, Apache Airflow, Talend, Informatica, AWS Glue, etc.

Day 152: Data Extraction Techniques

Understanding the different types of data sources: flat files, databases, APIs, web scraping, etc.
Extracting data from flat files: CSV, Excel, JSON, XML, etc.
Extracting data from relational databases: MySQL, PostgreSQL, Oracle, SQL Server, etc.
Hands-on practice with a data extraction pipeline using Apache NiFi or AWS Glue.

Day 153: Data Transformation Techniques

Understanding the different data transformation techniques: data cleaning, data validation, data normalization, data aggregation, data enrichment, etc.
Transforming data using SQL queries: Joins, Group By, Aggregations, etc.
Transforming data using Python: Pandas, NumPy, etc.
Hands-on practice with a data transformation pipeline using Apache NiFi or AWS Glue.

Day 154: Data Loading Techniques

Understanding the different data loading techniques: full load, incremental load, delta load, etc.
Understanding the different data loading destinations: databases, data lakes, data warehouses, etc.
Loading data into relational databases: MySQL, PostgreSQL, Oracle, etc.
Hands-on practice with a data loading pipeline using Apache NiFi or AWS Glue.

Day 155: ETL Performance Tuning

Understanding the different ETL performance bottlenecks: network latency, disk I/O, CPU, memory, etc.
Understanding the different ETL performance tuning techniques: partitioning, indexing, caching, parallel processing, etc.
Hands-on practice with a performance tuning pipeline using Apache NiFi or AWS Glue.

Day 156: ETL Monitoring and Alerting

Understanding the importance of ETL monitoring and alerting
Understanding the different ETL monitoring and alerting techniques: log monitoring, dashboarding, threshold-based alerting, anomaly detection, etc.
Hands-on practice with an ETL monitoring and alerting pipeline using Apache NiFi or AWS Glue.

Day 157: Cloud-based ETL Solutions

Understanding the benefits of cloud-based ETL solutions
Familiar with different cloud-based ETL solutions: AWS Glue, Google Cloud Dataflow, Azure Data Factory, etc.
Hands-on practice with a cloud-based ETL pipeline using AWS Glue or any other cloud-based ETL solution.

Day 158: Advanced ETL Techniques

Understanding advanced ETL techniques such as change data capture, data replication, data deduplication, and data integration.
Familiar with advanced ETL tools such as Apache Kafka, Apache Spark, and Apache Flink.
Hands-on practice with an advanced ETL pipeline using Apache NiFi, AWS Glue, or any other ETL tool.

Day 159: ETL Optimization and Scalability

Understanding ETL optimization and scalability techniques such as distributed processing, data partitioning, data sharding, and data compression.
Familiar with ETL optimization and scalability tools such as Hadoop, Spark, and NoSQL databases.
Hands-on practice with an optimized and scalable ETL pipeline using Apache NiFi, AWS Glue, or any other ETL tool.

Day 160: Data Quality and Governance

Understanding the importance of data quality and governance in ETL.
Understanding the different data quality techniques: data profiling, data standardization, data matching, data enrichment, etc.
Understanding the different data governance techniques: data lineage, data privacy, data security, etc.
Hands-on practice with data quality and governance.

Days 161 - 180: Learn Development Frameworks (Flask and Django) [Medium].

Day 161: Introduction to Flask

Understanding the principles and architecture of Flask for building data-driven web applications.
Installing and configuring Flask and its extensions in a virtual environment for data science applications.
Creating a basic Flask application with database and file upload functionality.

Day 162: Flask Templates and Visualizations

Advanced visualization techniques using libraries like Matplotlib, Seaborn, and Plotly.
Creating interactive and dynamic charts and graphs with JavaScript libraries like D3.js and Highcharts.
Using templates to create custom dashboards and user interfaces with advanced CSS and JavaScript.

Day 163: Flask Forms and Data Validation

Building complex forms for data input and manipulation using Flask-WTF and WTForms.
Implementing custom form fields for advanced data input, such as date pickers, file uploads, and autocomplete.
Validating form data using regular expressions, custom validators, and error handling.

Day 164: Flask Database Integration and Data Modeling

Connecting Flask to a database using SQLAlchemy and Flask-SQLAlchemy.
Creating database models and relationships for data storage and retrieval.
Querying and manipulating data with SQLAlchemy, including filtering, sorting, and pagination.

Day 165: Flask API Development and Documentation

Developing RESTful APIs using Flask-RESTful and Flask-API.
Creating endpoints for HTTP methods for data extraction, manipulation, and visualization.
Documenting API endpoints using Swagger and ReDoc.

Day 166: Flask Authentication and Authorization

Implementing user authentication and authorization with Flask-Login, Flask-Security, and JWT.
Protecting views and routes with access control for data privacy.
Handling authentication errors and redirects with custom error handlers.

Day 167: Flask Deployment and Scalability for Data Science

Understanding the benefits of cloud-based ETL solutions
Familiar with different cloud-based ETL solutions: AWS Glue, Google Cloud Dataflow, Azure Data Factory, etc.
Hands-on practice with a cloud-based ETL pipeline using AWS Glue or any other cloud-based ETL solution.

Day 168: Flask Advanced Topics for Data Science

Building custom Flask extensions and plugins for data processing and analysis, such as Flask-Uploads, Flask-Migrate, and Flask-Excel.
Implementing machine learning and data analysis workflows using Flask and libraries like Scikit-Learn, Pandas, and TensorFlow.
Integrating Flask with other data science tools and platforms, such as Apache Spark, Hadoop, and Jupyter Notebook.

Day 169: Django Fundamentals

Understanding the core concepts of Django, including models, views, templates, and URL routing.
Working with advanced model fields such as ForeignKey, ManyToManyField, and OneToOneField.
Using Django's ORM to work with databases and perform advanced database queries.

Day 170: Django Forms and Validation

Creating and validating forms in Django.
Working with advanced form fields such as ModelChoiceField, ModelMultipleChoiceField, and FileField.
Customizing form validation and error messages.

Day 171: Django Authentication and Authorization

Implementing user authentication and authorization in Django.
Working with advanced authentication features such as password reset and two-factor authentication.
Using Django's built-in permission system and creating custom permissions.

Day 172: Django Rest Framework

Understanding the basics of building RESTful APIs with Django Rest Framework (DRF).
Creating serializers to convert model instances to JSON format.
Implementing CRUD operations for RESTful APIs using DRF.

Day 173: Advanced DRF

Working with more advanced DRF features, such as pagination, filtering, and nested serializers.
Implementing token-based authentication and authorization for DRF APIs.
Creating custom DRF views and serializers.

Day 174: Django Testing

Understanding the importance of testing in Django development.
Writing and running unit tests for Django applications.
Using tools such as Pytest and Selenium for testing Django applications.

Day 175: Django Deployment

Preparing a Django application for deployment to a production environment.
Understanding the basics of server infrastructure, including load balancing and scaling.
Deploying a Django application to a cloud platform such as AWS, Azure, or Google Cloud.

Day 176: Django Security

Understanding common security vulnerabilities in Django applications.
Implementing security features such as SSL, CSRF protection, and SQL injection prevention.
Writing secure code in Django to prevent attacks such as cross-site scripting and injection attacks.

Day 177: Django Performance Optimization

Analyzing and optimizing Django application performance.
Implementing caching strategies to improve performance.
Working with advanced database optimization techniques such as indexing and query optimization.

Day 178: Django Signals and Middleware

Understanding Django signals and their use cases.
Creating custom signals to trigger actions within a Django application.
Implementing custom middleware to modify requests and responses in a Django application.

Day 179: Django Internationalization and Localization

Understanding the importance of internationalization and localization in Django applications.
Implementing multilingual support in Django applications.
Working with translation tools such as gettext and Rosetta.

Day 180: Advanced Django Topics

Exploring advanced Django topics such as content management systems (CMS), search engine optimization (SEO), and real-time web applications.
Working with third-party Django packages and libraries to extend Django functionality.
Best practices for Django development, including code organization and project architecture.

Days 181 - 190: Learn Version Control (Git and GitHub) [Easy].

Day 181: Introduction to Version Control and Git

Understanding the concept of version control and its importance in software development.
Getting familiar with Git and its basic functionalities such as initializing a repository, making commits, and branching.
Setting up Git on your local machine.

Day 182: GitHub Basics

Understanding the basics of GitHub, such as repositories, issues, and pull requests.
Learning how to clone, fork, and contribute to open-source projects on GitHub.
Getting familiar with GitHub's web interface and how to use it to manage your repositories.

Day 183: GitHub Intermediate

Learning more advanced GitHub features such as GitHub Actions and GitHub Pages.
Understanding how to use GitHub for continuous integration and deployment (CI/CD).
Using GitHub to host and publish static websites.

Day 184: Git Intermediate

Learning more advanced Git commands, such as merging, rebasing, and resolving conflicts.
Understanding Git's object model and how it works under the hood.
Using Git to collaborate with others on a project.

Day 185: Git Best Practices

Understanding best practices for using Git, such as committing frequently, writing descriptive commit messages, and keeping your commit history clean.
Learning how to use Git hooks to automate tasks in your development workflow.
Understanding how to use Git with different IDEs and editors.

Day 186: Advanced Git Topics

Learning advanced Git topics such as Git rebase, Git stash, and Git bisect.
Understanding how to use Git to work with large files and binary data.
Learning how to use Git submodules and Git subtrees for managing dependencies.

Day 187: GitHub Collaboration

Learning how to use GitHub to collaborate with other developers on a project.
Understanding how to use GitHub's pull request review tools to improve code quality.
Learning how to manage issues and merge requests on GitHub.

Day 188: Git Branching Strategies

Understanding different branching strategies such as GitFlow, Trunk-based Development, and Feature Branching.
Learning how to use Git tags for versioning and release management.
Understanding semantic versioning.
Understanding how to use Git with different types of projects, such as web applications, mobile applications, and libraries.

Day 189: GitHub Advanced Topics

Learning advanced GitHub topics such as GitHub Enterprise, GitHub APIs, and GitHub CLI.
Understanding how to use GitHub for project management, such as kanban boards and milestones.
Learning how to use GitHub to host and manage documentation.

Day 190: Git and GitHub Best Practices

Understanding best practices for using Git and GitHub together, such as creating meaningful commit messages, using pull requests for code review, and automating your development workflow with CI/CD.
Learning how to troubleshoot common Git and GitHub issues.
Contributing to open-source projects on GitHub.

Days 191 - 210: Learn Containerization and Orchestration (Docker and Kubernetes) [Medium].

Day 191: Introduction to Docker

Understanding the fundamentals of containerization and the history of Docker.
Installing Docker on your local machine.
Installing Docker on a remote server or cloud provider.
Running your first Docker container.
Running and managing containers using the Docker CLI and API.

Day 192: Docker Images

Creating and customizing Docker images.
Creating optimized and secure Docker images for production environments.
Understanding Dockerfile syntax.
Using Docker Hub to search and share images.
Configuring Docker images for compatibility with specific operating systems and hardware.
Using advanced Dockerfile features such as multi-stage builds and ARGs.

Day 193: Docker Networking

Understanding Docker's networking model.
Configuring network settings for your containers.
Using Docker Compose to define multi-container applications.
Implementing load balancing and service discovery with Docker Swarm and Consul.
Securing Docker networks with TLS encryption and mutual authentication.

Day 194: Docker Volume

Creating and using volumes to persist data.
Backing up and restoring Docker volumes.
Understanding different types of storage in Docker and how to manage them.
Implementing storage features such as block-level storage and distributed file systems.
Configuring Docker storage drivers for performance and data safety.

Day 195: Docker Security

Understanding Docker security risks and best practices.
Configuring access controls for your containers.
Implementing security features such as rootless containers and seccomp profiles.
Scanning Docker images for vulnerabilities.
Integrating Docker security scanning tools with CI/CD pipelines.
Hardening Docker hosts and securing Docker registries with authentication and authorization.

Day 196: Docker Compose

Understanding how Docker Compose works.
Creating and managing multi-container applications with Compose.
Using environment variables and configuration files in Compose.

Day 197: Docker Performance and Optimization

Use resource limits and cgroups to optimize container resource usage, such as CPU, memory, and I/O utilization.
Minimize kernel overhead and tune the kernel for container workloads to improve performance.
Use Docker stats and third-party tools such as Prometheus to measure and analyze container performance.
Use Docker Swarm to implement autoscaling and self-healing features for your container workloads.
Configure container networking for performance and reliability, and optimize container storage for performance and reliability.

Day 198: Docker Best Practices

Learning about tools and resources for managing Docker at scale.
Reviewing best practices for containerizing applications and working with Docker at scale.
Implementing advanced Docker use cases such as edge computing and IoT.
Integrating Docker with other technologies such as serverless computing and blockchain.

Day 199: Introduction to Kubernetes

Overview of Kubernetes architecture and components.
Understanding Kubernetes objects, such as pods, services, and deployments.
Deploying a simple docker application on Kubernetes.

Day 200: Kubernetes Networking

Understanding Kubernetes networking and service discovery.
Configuring Kubernetes networking using CNI plugins.
Deploying a multi-tier docker application with network policies.

Day 201: Kubernetes Storage

Understanding Kubernetes storage options, such as volumes and persistent volumes.
Configuring storage classes and persistent volume claims.
Deploying a stateful application with persistent storage on Kubernetes.

Day 202: Kubernetes Security

Understanding Kubernetes security features, such as RBAC and network policies.
Configuring Kubernetes security with TLS certificates and secrets management.
Deploying a secure docker application on Kubernetes.

Day 203: Kubernetes Operators

Understanding the concept of Kubernetes operators.
Creating a custom Kubernetes operator using the Operator SDK.
Deploying and managing a complex application with a custom operator.

Day 204: Kubernetes API

Understanding the Kubernetes API and how to interact with it.
Using Kubernetes API clients and tools to manage Kubernetes resources.
Developing custom Kubernetes controllers using the API.

Day 205: Kubernetes Observability

Understanding Kubernetes observability with monitoring and logging tools.
Configuring and deploying Prometheus and Grafana on Kubernetes.
Monitoring Kubernetes resources and application performance with Prometheus and Grafana.

Day 206: Kubernetes Advanced Scheduling

Understanding Kubernetes advanced scheduling features, such as node affinity and taints/tolerations.
Configuring Kubernetes scheduling policies for optimal resource usage.
Deploying a workload with advanced scheduling requirements on Kubernetes.

Day 207: Kubernetes High Availability

Understanding Kubernetes high availability and cluster management.
Setting up Kubernetes clusters with multiple control plane nodes.
Deploying a highly available application on Kubernetes.

Day 208: Kubernetes Federation

Understanding Kubernetes federation and how to use it to manage multiple clusters.
Configuring Kubernetes federation using kubefed.
Deploying and managing a federated application on Kubernetes.

Day 209: Kubernetes Custom Resource Definitions (CRDs)

Understanding Kubernetes custom resource definitions and how to create them.
Developing a custom CRD and controller using the API and Operator SDK.
Deploying and managing a custom resource on Kubernetes.

Day 210: Kubernetes Advanced Networking

Understanding Kubernetes advanced networking features, such as ingress controllers and service meshes.
Configuring and deploying an ingress controller and service mesh on Kubernetes.
Managing application traffic and security with advanced networking features on Kubernetes.

Days 211 - 220: Learn Advanced Natural Language Processing (NLP) (Tensorflow, Gensim, LIME, etc) [Hard].

Day 211: Neural Networks for NLP

Learn about neural networks and their applications in NLP, including text classification, sentiment analysis, and machine translation.
Explore popular neural network architectures for NLP, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
Practice implementing neural networks for NLP using Python libraries such as PyTorch and TensorFlow.

Day 212: Attention Mechanisms

Learn about attention mechanisms, a powerful tool for improving the performance of neural networks in NLP tasks.
Understand how attention mechanisms work, including self-attention and cross-attention.
Explore popular attention-based models for NLP, such as Transformer and BERT.
Practice implementing attention mechanisms for NLP using Python libraries such as PyTorch and TensorFlow.

Day 213: Sequence Modeling

Learn about sequence modeling, a technique for modeling sequences of data such as text and speech.
Understand how sequence modeling can be performed using recurrent neural networks (RNNs) and their variants such as LSTM and GRU.
Explore popular sequence modeling applications in NLP, such as language modeling, text generation, and machine translation.
Practice implementing sequence modeling for NLP using Python libraries such as PyTorch and TensorFlow.

Day 214: Transfer Learning in NLP

Learn about transfer learning, a technique for reusing pre-trained models to improve the performance of NLP models on new tasks and domains.
Understand how transfer learning can be performed using pre-trained language models such as GPT and BERT.
Explore popular transfer learning applications in NLP, such as fine-tuning and zero-shot learning.
Practice implementing transfer learning for NLP using Python libraries such as Hugging Face Transformers.

Day 215: Unsupervised Learning in NLP

Learn about unsupervised learning, a technique for learning from unlabelled data in NLP tasks.
Understand how unsupervised learning can be performed using methods such as clustering, dimensionality reduction, and generative models.
Explore popular unsupervised learning applications in NLP, such as topic modeling and word embeddings.
Practice implementing unsupervised learning for NLP using Python libraries such as Scikit-Learn and Gensim.

Day 216: Deep Reinforcement Learning for NLP (We'll just cover RL)

Learn about deep reinforcement learning, a technique for learning to make decisions in complex environments by interacting with them.
Understand how deep reinforcement learning can be applied to NLP tasks, such as dialog systems and recommendation systems.
Explore popular deep reinforcement learning algorithms for NLP, such as deep Q-networks (DQNs) and policy gradients.
Practice implementing deep reinforcement learning for NLP using Python libraries such as PyTorch and TensorFlow.

Day 217: Multimodal NLP

Learn about multimodal NLP, a field that deals with processing and understanding text data in conjunction with other modalities such as images, audio, and video.
Understand how multimodal NLP can be performed using methods such as visual question answering (VQA) and audio captioning.
Explore popular multimodal NLP applications, such as image captioning and speech recognition.
Practice implementing multimodal NLP using Python libraries such as PyTorch and TensorFlow.

Day 218: Cross-Lingual NLP

Learn about cross-lingual NLP, a field that deals with processing and understanding text data in multiple languages.
Understand how cross-lingual NLP can be performed using methods such as machine translation and cross-lingual word embeddings.
Explore popular cross-lingual NLP applications, such as cross-lingual document classification and cross-lingual information retrieval.
Practice implementing cross-lingual NLP using Python libraries such as Hugging Face Transformers.

Day 219: Adversarial Attacks and Defenses in NLP

Learn about adversarial attacks, a technique for creating inputs that are designed to fool a neural network model into making incorrect predictions.
Understand how adversarial attacks can be performed on NLP models, and explore methods for defending against them.
Explore popular adversarial attacks and defenses in NLP, such as text perturbations and defensive distillation.
Practice implementing adversarial attacks and defenses in NLP using Python libraries such as cleverhans and ART.

Day 220: Explainability and Interpretability in NLP

Learn about explainability and interpretability, techniques for understanding and explaining the decisions made by a neural network model.
Understand how explainability and interpretability can be performed on NLP models, and explore methods for visualizing and analyzing model behavior.
Explore popular explainability and interpretability techniques in NLP, such as saliency maps and attention visualization.
Practice implementing explainability and interpretability in NLP using Python libraries such as Captum and LIME.

Days 221 - 230: Learn Reinforcement Learning (RL) [Medium].

Day 221: Introduction to Reinforcement Learning

Learn about Reinforcement Learning (RL) and its applications. Understand how it differs from other machine learning techniques, such as supervised and unsupervised learning.
Explore the components of an RL problem, such as the agent, environment, state, action, reward, and policy.
Understand the basic concepts of RL, including the Markov Decision Process (MDP), value functions, and Bellman equations.

Day 222: Q-Learning and SARSA

Explore the two most popular RL algorithms, Q-learning, and SARSA.
Understand how Q-learning and SARSA work, including the use of the Q-value function to determine the optimal policy and the update rules for the Q-value function.
Learn about the differences between Q-learning and SARSA, such as the exploration-exploitation trade-off and the on-policy vs. off-policy learning.

Day 223: Policy Gradient Methods

Explore policy gradient methods, an alternative to Q-learning and SARSA, where the agent learns a parameterized policy directly.
Understand how policy gradient methods work, including the use of gradient ascent to optimize the policy parameters and the use of a baseline to reduce variance.
Learn about popular policy gradient methods such as REINFORCE, actor-critic, and Proximal Policy Optimization (PPO).

Day 224: DQN and DDPG

Understand how RL can be combined with deep neural networks to solve complex problems.
Explore the two most popular deep RL algorithms, Deep Q-Network (DQN), and Deep Deterministic Policy Gradient (DDPG).
Understand how DQN uses a deep neural network to approximate the Q-value function, and how DDPG learns a parameterized policy directly.

Day 225: Multi-Agent RL

Learn about Multi-Agent Reinforcement Learning (MARL), where multiple agents interact with the environment and learn to optimize their individual policies.
Understand the challenges of MARL, such as the curse of dimensionality and the need for coordination among agents.
Explore popular MARL algorithms such as Independent Q-learning (IQL), centralized training with decentralized execution (CTDE), and actor-critic with centralized critics (AC3).

Day 226: RL in Robotics

Explore the applications of RL in robotics, where RL can be used to learn optimal control policies for robotic systems.
Understand the challenges of RL in robotics, such as the need for safety and the need for efficient exploration.
Learn about popular RL algorithms for robotics, such as Trust Region Policy Optimization (TRPO) and Soft Actor-Critic (SAC).

Day 227: RL in Natural Language Processing (NLP)

Learn about the applications of RL in Natural Language Processing (NLP), where RL can be used to learn optimal policies for tasks such as text classification, language modeling, and dialog systems.
Explore popular RL algorithms for NLP, such as REINFORCE and Actor-Critic.
Understand the challenges of RL in NLP, such as the need for efficient exploration and the need to handle the high dimensionality of the input space.

Day 228: RL in Game AI

Explore the applications of RL in Game AI, where RL can be used to learn optimal policies for game-playing agents.
Learn about popular RL algorithms for game AI, such as AlphaGo and AlphaZero.
Understand the challenges of RL in Game AI, such as the need to handle the partial observability of the game state and the need to handle the stochasticity of the environment.

Day 229: Exploration and Exploitation in RL

Understand the exploration-exploitation trade-off in RL, where the agent needs to balance between exploring new actions and exploiting the current knowledge to maximize the reward.
Learn about different exploration strategies, such as Epsilon-Greedy, Upper Confidence Bound (UCB), and Thompson Sampling.
Explore advanced exploration strategies, such as Optimistic Initial Values, Boltzmann Exploration, and Bootstrapped DQN.

Day 230: RL in Continuous Control

Learn about the challenges of RL in continuous control problems, where the action space is continuous and infinite.
Explore popular algorithms for continuous control, such as Trust Region Policy Optimization (TRPO), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC).
Understand how these algorithms handle the continuous action space using techniques such as policy gradient methods, actor-critic methods, and value function approximation.
Learn about advanced topics in continuous control, such as hierarchical RL, imitation learning, and inverse RL.

Days 231 - 240: Learn Computer Vision (Tensorflow, OpenCV, COLMAP, Scikit-Image, etc) [Medium].

Day 231: Introduction to Computer Vision

Learn about the basics of computer vision, its applications, and its history.
Understand the difference between image processing and computer vision.
Explore the computer vision pipeline and various techniques involved in each stage.

Day 232: Image Segmentation

Learn about the various image segmentation techniques, including thresholding, clustering, and watershed segmentation.
Understand the difference between supervised and unsupervised segmentation methods.
Implement segmentation techniques using Python libraries such as OpenCV and scikit-image.

Day 233: Object Detection and Recognition

Learn about the different object detection and recognition techniques, including feature-based methods, deep learning-based methods, and hybrid methods.
Understand the various object detection frameworks such as YOLO, SSD, and Faster R-CNN.
Implement object detection and recognition using Python libraries such as OpenCV and TensorFlow.

Day 234: Image Registration and Alignment

Learn about the different image registration and alignment techniques, including point-based registration, feature-based registration, and intensity-based registration.
Understand the difference between rigid and non-rigid registration methods.
Implement image registration and alignment techniques using Python libraries such as OpenCV and SimpleITK.

Day 235: Image Restoration and Enhancement

Learn about the different image restoration and enhancement techniques, including deblurring, denoising, and super-resolution.
Understand the various image enhancement methods such as contrast stretching, histogram equalization, and adaptive histogram equalization.
Implement image restoration and enhancement techniques using Python libraries such as OpenCV and scikit-image.

Day 236: Deep Learning for Computer Vision

Learn about the basics of deep learning for computer vision, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).
Understand the various deep learning frameworks such as TensorFlow and PyTorch.
Implement deep learning models for computer vision tasks such as image classification, segmentation, and object detection.

Day 237: 3D Computer Vision

Learn about 3D computer vision techniques, including stereo vision, structure from motion (SFM), and visual SLAM.
Understand the difference between monocular and multi-camera 3D computer vision methods.
Implement 3D computer vision techniques using Python libraries such as OpenCV and COLMAP.

Day 238: Face Recognition and Biometrics

Learn about face recognition and biometrics techniques, including feature extraction, template matching, and deep learning-based methods.
Understand the various face recognition frameworks such as OpenFace, FaceNet, and DeepID.
Implement face recognition and biometrics techniques using Python libraries such as OpenCV and TensorFlow.

Day 239: Optical Character Recognition (OCR)

Learn about optical character recognition (OCR) techniques, including preprocessing, feature extraction, and classification.
Understand the difference between traditional OCR methods and deep learning-based OCR methods.
Implement OCR techniques using Python libraries such as Tesseract and OpenCV.

Day 240: Video Analysis and Tracking

Learn about video analysis and tracking techniques, including optical flow, object tracking, and activity recognition.
Understand the various video analysis frameworks such as OpenCV and TensorFlow.
Implement video analysis and tracking techniques using Python libraries such as OpenCV and TensorFlow.

Days 241 - 250: Learn CyberSecurity and Privacy [Medium].

Day 241: Introduction to Cybersecurity and Privacy

Learn the basic concepts of cybersecurity and privacy in the context of data science.
Understand the potential risks and threats to data privacy and security.
Explore the legal and ethical considerations in data privacy and security.

Day 242: Cryptography and Data Encryption

Learn the fundamentals of cryptography, including encryption and decryption techniques.
Explore various cryptographic protocols such as RSA, AES, and DES.
Implement encryption and decryption using Python libraries such as Cryptography, PyCryptodome, and PyCrypto.
Understand the concepts of digital signatures and digital certificates.

Day 243: Network Security and Packet Analysis

Learn about network security and packet analysis techniques.
Understand network protocols such as TCP/IP, HTTP, and SSL/TLS.
Use Python libraries such as Scapy and Wireshark to capture and analyze network traffic.
Implement network security measures such as firewall configuration and intrusion detection.

Day 244: Web Application Security

Learn about web application security techniques.
Understand the OWASP Top 10 vulnerabilities such as SQL injection, cross-site scripting (XSS), and cross-site request forgery (CSRF).
Use Python libraries such as Requests, BeautifulSoup, and Selenium to perform web scraping and testing.
Implement web application security measures such as input validation and sanitization.

Day 245: Operating System Security

Learn about operating system security measures and techniques.
Understand the security risks associated with popular operating systems such as Windows and Linux.
Use Python libraries such as os, subprocess, and socket to implement OS security measures such as user authentication, file permissions, and network security.

Day 246: Machine Learning Security

Learn about the security risks associated with machine learning models and algorithms.
Understand the potential vulnerabilities of ML models such as data poisoning and model stealing.
Use Python libraries such as Adversarial Robustness Toolbox (ART) and CleverHans to implement security measures for ML models such as adversarial training and model verification.

Day 247: AWS Cloud Security

Learn about AWS security measures and techniques, including identity and access management (IAM), network security, and data encryption.
Understand the potential security risks associated with using AWS and how to mitigate them.
Use Python libraries such as Boto3 to implement AWS security measures such as configuring IAM roles and policies, creating and managing security groups, and enabling encryption for AWS resources.

Day 248: Privacy-preserving Techniques

Learn about privacy-preserving techniques such as differential privacy, homomorphic encryption, and secure multi-party computation.
Use Python libraries such as PySyft and PyGrid to implement privacy-preserving measures for data analysis and sharing.

Day 249: Blockchain and Distributed Ledger Technologies

Learn about blockchain and distributed ledger technologies.
Understand the basic concepts of blockchain such as hashing, digital signatures, and consensus algorithms.
Use Python libraries such as Pyethereum and Hyperledger to implement blockchain-based security measures such as smart contracts and decentralized applications.

Day 250: Cybersecurity and Privacy in IoT

Learn about cybersecurity and privacy risks associated with Internet of Things (IoT) devices.
Understand the basic architecture and protocols of IoT networks.
Use Python libraries such as Paho-MQTT and Blynk to implement IoT security measures such as secure communication and data encryption.

250 Days Complete Roadmap for Data Science After Learning Python

Directory:

Days n - 0: Learn Python (SQL is also recommended).

Days 1 - 10: Learn Data Analysis Libraries NumPy and Pandas [Easy].

Days 11 - 20: Learn Data Visualization Libraries (Matplotlib, Seaborn, Plotly, etc) [Easy].

Days 21 - 30: Learn Machine Learning (ML) and Basic algorithms (Scikit-Learn) [Medium].

Days 31 - 40: Learn Deep Learning and Neural Networks (Keras) [Medium].

Days 41 - 50: Learn Advanced Machine Learning (ML) (Scikit-Learn) [Hard].

Days 51 - 60: Learn Natural Language Processing (NLP) (Scikit-Learn, NLTK, Spacy, etc) [Hard].

Days 61 - 70: Learn Time Series Analysis (TSA) (Pandas, statsmodels) [Hard].

Days 71 - 80: Learn Advanced Deep Learning (DL) (Keras, Tensorflow) [Medium].

Days 81 - 90: Learn Data Visualization Tools (PowerBI, Tableau) [Easy].

Days 91 - 100: Learn Linux and Shell Commands [Easy].

Days 101 - 110: Explore Different Databases (MySQL, PostgreSQL, PyMongo, etc) [Medium].

Days 111 - 130: Learn Big Data Tools "Hadoop Ecosystem" (Hadoop, Spark, Hive, etc) [Hard].

Days 131 - 150: Learn AWS, Microsoft Azure, or GCP (Based on your choice, we'll be using AWS) [Hard].

Days 151 - 160: Learn Extract, Transform, and Load (ETL) (Apache NiFI, AWS Glue) [Medium].

Days 161 - 180: Learn Development Frameworks (Flask and Django) [Medium].

Days 181 - 190: Learn Version Control (Git and GitHub) [Easy].

Days 191 - 210: Learn Containerization and Orchestration (Docker and Kubernetes) [Medium].

Days 211 - 220: Learn Advanced Natural Language Processing (NLP) (Tensorflow, Gensim, LIME, etc) [Hard].

Days 221 - 230: Learn Reinforcement Learning (RL) [Medium].

Days 231 - 240: Learn Computer Vision (Tensorflow, OpenCV, COLMAP, Scikit-Image, etc) [Medium].

Days 241 - 250: Learn CyberSecurity and Privacy [Medium].

Days 251 - n: You are an Industry Standard Data Scientist.

You're an Industry Standard Data Scientist with vast knowledge of the IT fields. Keep Learning, and get in touch with the latest technologies.

Thanks for reading. HAPPY CODING!Created with 🖤 by Muhammad Huzaifa AsimFacebook. Instagram. Twitter. YouTube. GitHub. Website.

Posted by Huzaifa Asim

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Created with 🖤 by Muhammad Huzaifa Asim
Facebook. Instagram. Twitter. YouTube. GitHub. Website.