VyuWing Learning

Data Science Roadmap

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Roadmap to becoming an Industry Leading Data Scientist.

After multiple hours of research with a number of industry professionals currently working as Data Scientists in Fortune 500 tech companies, we've aggregated all the best, free and open source learning material that they used to become a Data Scientist.

The roadmap has been carefully and accurately broken down into a step by step action plan of theory and practice resources to make sure you never miss any aspect in your Data Science learning.

Data Science Roadmap

Things to keep in mind while going through

• The courses that we describe are available for free to audit and need not be bought. This document is in no way any paid promotion of the described courses, we recommend them based on community feedback and experience. Similar applies to the links/exercises we follow.

• The Week Structure we illustrate may not be in complete balance with the candidate's timeline. Therefore, in the cases like these, we advise considering the stipulated time frame 1 week/topic to be more than 1 (maybe 2 weeks/topic) as we strongly advise to not break the structure of course action.

SQL for Reference (MySQL)

Suggested Course Link

Topic	Topic/Tutorial	Exercises
SQL	Tutorial (ER Diagrams towards the end of the video are optional)	Exercise 1 Exercise 2
Week 3: Databases and SQL for Data Science with Python	String Pattern, Ranges and Operations on Sets Video	Sorting & Grouping Problem
	Functions, Multiple Tables and Sub-Queries Video	Course Lab 1 Course Lab 2 Course Lab 3
Week 4: Database and SQL for Data Science with Python	Methods and Tools to access Database with Python	Quiz Course Lab 1 Course Lab2
Week 5: Database and SQL for Data Science with Python	Hands-on SQL Experience with Real-world Data	Assignment
SQL Project View	Take a Look at the Project Level Implementation of SQL	College-ERP ansql

Excel for Reference

Topic	Course/Tutorial	Exercises
Excel	Tutorial 1 Tutorial 2	Excel Interview Questions
Excel Advanced(Preferred if your Job Description requires sound knowledge of Excel)	Tutorial 1 Tutorial 2	Excel VBA for Creative problem solving

Project to follow after completing Excel:

• Data Analysis Project in MS Excel

Python Programming

Week 1

Python Environment Setup

Windows

Linux 1

Linux 2

Tutorial to follow

• Tutorial

Coding Questions

• Hackerrank Easy+Medium+hard
• Hackerrank Easy+Medium

Topic	Practice Resource	Coding Question
Variables	Quiz 1 Quiz 2 Quiz 3 Quiz 4	Practice from Hackerrank given above
Conditional Statements	Quiz 1 Quiz 2 (Solve Conditional Statements Questions only)	Practice from Hackerrank given above
Functions	Quiz 1 Quiz 2 Quiz 3 Quiz 4 Quiz 5 Quiz 6 Quiz 7	Practice from Hackerrank given above
Control Flow	Quiz 1 Quiz 2 Quiz 3 Quiz 4 Quiz 5 Quiz 6 Quiz 7	Practice from Hackerrank given above
Bitwise Operators	Quiz 1 Quiz 2 Quiz 3	Practice from Hackerrank given above
Strings	Quiz 1 Quiz 2 Quiz 3 Quiz 4 Quiz 5 Quiz 6 Quiz 7 Quiz 8 Quiz 9 Quiz 10 Quiz 11 Quiz 12 Quiz 13 Quiz 14	Practice from Hackerrank given above
List, Tuples	Quiz 1 Quiz 2 Quiz 3 Quiz 4 Quiz 5 Quiz 6 Quiz 7 Quiz 8 Quiz 9 Quiz 10 Quiz 11 Quiz 12	Practice from Hackerrank given above
Dictionary	Quiz 1 Quiz 2 Quiz 3 Quiz 4 Quiz 5	Practice from Hackerrank given above
Sets	Quiz 1 Quiz 2 Quiz 3 Quiz 3 Quiz 4 Quiz 5	Practice from Hackerrank given above

Week 2

Tutorial to Follow

• Classes and objects - Overriding

• Polymorphism

• Data Hiding

• Regular Expression

Topic	Practice Resource	Coding Question
Classes and Objects	Quiz 1 Quiz 2	Python Classes and Objects
Attributes & Constructors	Quiz	Python Class Attributes Class Instance Attributes Constructors in Python
Inheritance	Quiz 1 Quiz 2 Quiz 3	Inheritance example Python Inheritance Questions & Answers
Overloading	Quiz 1 Quiz 2	Method Overloading Overloading in Python
Overriding	Quiz 1	Method Overriding in Python
Polymorphism	Quiz 1	Theory 1 Theory 2
Data hiding	Quiz 1 Quiz 2	Data hiding and object printing
Regular Expression	Quiz 1 Quiz 2 Quiz 3	Regex Coding Problems

Data Structures and Algorithms

Week 3

Tutorial to Follow Link

Coding Questions

• Hackerrank Easy + Medium Pt 1
• Hackerrank Easy + Medium Pt 2

Topic	Practice Resource	Coding Question
Time Complexity	Quiz 1 Quiz 2 Quiz 3	Theory
Recursion	Quiz 1 Quiz 2	Theory
Linked List	Practice Problems	Practice from Hackerrank given above Additional Problems
Stacks and Queues	Quiz 1	Practice from Hackerrank given above Additional Problems
Hashing	Quiz	Practice from Hackerrank given above
Searching Algorithms	Quiz	Practice from Hackerrank given above Additional Problems
Sorting Algorithms	Quiz

Databases

Week 4

Introduction to Database

Topic	Course/Tutorial	Exercises
NumPy	Tutorial	Exercise 1 Exercise 2 Quiz
Pandas	Tutorial	Exercise 1 Exercise 2 Quiz Problems
Matplotlib	Tutorial	Quiz

Mathematics

Week 5

Topic	Practice Resource	Coding Question
Statistics	Course	Statistics Interview Questions
Probability Advanced	Theory	40 Probability questions

Week 6

Topic	Learning Resource	Coding Question
Probability Miscellaneous	Theory
Linear Algebra	Theory	Question
Multivariate Calculus(Skip Simple Neural Network and Simple Artificial Neural Network from Week 3)	Theory
PCA	Theory

Machine Learning

Week 7

Course to follow

Andrew NG Stanford Machine Learning Coursera Link

For this Course, all Exercises in Python: Link

Course Section	Topic	Practice Codes	Additional Resource
Week 1: Andrew NG Stanford Machine Learning	What is Machine Learning
	Supervised Learning
	Unsupervised Learning
	Linear Regression with One Variable Cost Function Gradient Descent	Linear Regression without Library in Python One variable Linear Regression One variable Linear Regressions (Advanced)	Learn EDA before coding EDA 1(EDA Methods Code) EDA 2(EDA Project) Data Analysis
	Linear Algebra Review Vector Arithmetic Operations		Do a quick review as you have done a detailed one initially
Week 2: Andrew NG Stanford Machine Learning	Linear Regression with multiple variables Multiple Features Gradient Descent for Multiple Variables Polynomial Regression Normal Equation Non-Invertibility		ML without libraries ML using libraries Project

Week 8

Course Section	Topic	Practice Codes
Week 3: Andrew NG Stanford Machine Learning	Logistic Regression: Classification Hypothesis Representation Decision Boundary Cost Function Optimization	Logistic Regression Practice from scratch Logistic Regression using Library Logistic Practice examples
	Regularization Overfitting Regularised Linear Regression Regularised Logistic Regression	Regularization without library

Summary Exercise:

Based on what you have Learned, conduct the following project: Link

Note: Use the links below for a structured approach to a project.

• Getting Started (Kaggle)

• Step by step tutorial for beginners

Course Section	Topic	Practice Codes
Week 7: Andrew NG Stanford Machine Learning	Support Vector Machines Optimization Large Margin Intuition Underlying Mathematics Kernels	SVM using Library 1 SVM using Library 2 SVM without Library

Week 9

Applied Data Science Course Link

Course Section	Topic	Practice Codes
Week 1: Applied Data Science Course	Intro to SciKit Learn	Environment Setup Code Tutorial Project
Week 1: Applied Data Science Course	K Nearest Neighbors	KNN from scratch KNN using SKLearn UCI Glass detection
Week 2: Applied Data Science Course	Introduction to Supervised Learning Overfitting and Underfitting Supervised Learning: Datasets K Nearest Neighbours
Week 2: Applied Data Science Course	Linear Regression: Least Squares	Boston Housing Problem - Linear Regression Simple Linear Regression
Week 2: Applied Data Science Course	Linear Regression: Lasso	Lasso Regression without library Lasso Regression with library
Week 2: Applied Data Science Course	Linear Regression: Polynomial	Polynomial Regression without Library

Week 10

Course Section	Topic	Practice Codes
Week 2: Applied Data Science Course	Logistic Regression	Since it's already covered, do a quick review here.
Week 2: Applied Data Science Course	Support Vector Machines	SVM using Library SVM with Python SVM without library
Week 2: Applied Data Science Course	Decision Trees	Naive Bayes without library Decision tree without library
CS229 Stanford	Ensemble Methods	Theory
Week 4: Applied Data Science Course	Naive Bayes	Naive Bayes Sklearn Classifiers

Week 11

Course Section	Topic	Practice Codes
Week 4: Applied Data Science Course	Random Forests	Random forest without library Random forest Classifier
Week 4: Applied Data Science Course	Dimensionality Reduction and Manifold Learning	Good Read Dimensionality reduction and classification on Hyperspectral images

Week 12

Quick Review

IBM's Machine Learning with python

For this Course, all Exercises in Python:

Machine Learning exercise with Python (IBM)

Course Section	Topic	Practice Codes
Week 1: Machine Learning With Python	Python for Machine Learning Intro	Course Practice Problems
	Supervised vs Unsupervised	Course Practice Problems
Week 2: Machine Learning With Python	Linear Regression Model Evaluation Evaluation Metrics	Course Practice Problems
	Non-Linear Regression	Course Practice Problems
Week 3: Machine Learning With Python	K-Nearest Neighbours Intro to Classification KNN Evaluation Metrics	Course Practice Problems
	Decision Trees Building Decision Trees	Course Practice Problems
	Logistic Regression Logistic vs Linear Regression Logistic Regression training	Course Practice Problems
	Support Vector Machine	Course Practice Problems
Week 4: Machine Learning With Python	k-Means Clustering	Course Practice Problems
	Hierarchical Clustering	Course Practice Problems
	Density-Based Clustering	Course Practice Problems
Week 5: Machine Learning With Python	Content based Recommendation Engines Recommender Systems Collaborative Filtering	Course Practice Problems
Week 6: Machine Learning With Python	Course Project

Week 13

Continuing the course Andrew NG Stanford Machine Learning Coursera Link

Course Section	Topic	Practice Codes
Week 4: Andrew NG Stanford Machine Learning	Neural Networks: Representation Non-Linear Hypothesis Model Representation Intuitions	Getting used to Neural nets
Week 5: Andrew NG Stanford Machine Learning	Neural Networks: Learning Cost Function Backpropagation Gradient Checking Random Initialization Autonomous Driving	Deep Learning for Beginners
Week 6: Andrew NG Stanford Machine Learning	Machine Learning System Design Evaluating Hypothesis Model Selection Bias vs. Variance Regularization Learning Curves	Machine Learning Systems design

Week 14

Andrew NG Stanford Machine Learning Coursera Link

Course Section	Topic	Practice Codes
Week 8: Andrew NG Stanford Machine Learning	Unsupervised Learning Introduction K-means Optimization Objective Initialization Picking Clusters	Handson unsupervised learning Unsupervised Learning Unsupervised learning without libraries
	Dimensionality Reduction Data Compression Visualization PCA	Dataset Dimensionality reduction in Python Complete Project
Week 9: Andrew NG Stanford Machine Learning	Anomaly Detection Gaussian Distribution Anomaly Detection vs Supervised Learning- Choosing Features- Multivariate Gaussian	Anomaly Detection
	Recommender Systems Content Based Recommendations Collaborative Filtering Vectorization	Amazon Product Recommender System
Week 10: Andrew NG Stanford Machine Learning	Refer to course Videos
Week 11: Andrew NG Stanford Machine Learning	Refer to course Videos

Deep Learning

Week 15

Continue with Deeplearning.ai specialization

Course Section	Topic	Practice Codes
Week 1: Neural Networks and Deep Learning	What are neural networks and deep learning? Supervised Learning with Neural Networks	Course Practice Problem
Week 2: Neural Networks and Deep Learning	Binary Classification Logistic Regression Cost Function Gradient Descent Derivatives Computation Graph	Course Practice Problem
Week 2: Neural Networks and Deep Learning	Vectorization Broadcasting in Python Getting Started with Jupyter Notebook	Course Practice Problem
Week 3: Neural Networks and Deep Learning	Neural network representation Vectorising Activation Functions Non Linear Activation Functions Derivation of Activation Functions Gradient Descent for Neural Networks Backpropagation Random Initialization	Course Practice Problem
Week 4: Neural Networks and Deep Learning	Forward Propagation Deep PresentationsParameters vs Hyperparameters	Course Practice Problem

Week 16

Continue with Deeplearning.ai specialization

Course Section	Topic	Practice Codes
Week 1: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization	Train/Dev/Test Bias/Variance	Course Practice Problem 1 Course Practice Problem 2
Week 1: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization	Regularization Importance of Regularization Dropout Normalizing Input Vanishing/Exploding Gradients Weight Initialization Gradient Checking	Course Practice Problem
Week 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization	Mini Batch Gradient Descent Bias Correction Momentum RMSProp	Course Practice Problem
Week 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization	Adam optimization Learning Rate Decay Local Optima	Course Practice Problem
Week 3: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization	Hyperparameter tuning	Course Practice Problem 1 Course Practice Problem 2
Week 3: Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization	Tuning Picking Hyperparammeters Normalising Activators Fitting Batch Norm- Softmax	Course Practice Problem

Week 17

Continue with Deeplearning.ai specialization

Course Section	Topic	Practice Codes
Week 1: Structuring Machine Learning Projects	Orthogonalisation Satisficing & Optimising Metric Varying Dev/Test sets & Metrics Avoidable Bias	Course Practice Problem
Week 2: Structuring Machine Learning Projects	Error Analysis	Course Practice Problem
Week 2: Structuring Machine Learning Projects	Mismatched training and dev/test set	Course Practice Problem
Week 2: Structuring Machine Learning Projects	Learning from Multiple Tasks	Course Practice Problem
Week 2: Structuring Machine Learning Projects	End to end deep learning	Course Practice Problem

Keras

Week 18

Topic	Tutorial
Getting Started with Keras Modules	Video
Keras Implementation Example (Classification Boilerplate Code)	Video Code
Step By Step Making model in Keras	First Neural Network in Keras
Binary Classification In Keras	Code using NN
Logistic Regression in Keras	Code
End to end deep learning	Video
Neural Networks	Code Tutorial
Regularization Keras: L1/L2	Theory
Regularization Keras: DropOut	Theory
Weight Init	Theory
Optimizer Keras	Theory
Learning Rate Decay	Theory
Convolutional Network	Theory

Neural Networks and Deep Learning with Keras:

The concepts shown below are covered in the previous sections of

• Neural Networks and Deep Learning
• Improving Deep Neural Networks
• Hyperparameter Tuning
• Regularization and Optimization

Week 19

Continue with Deeplearning.ai specialization Link

Course Section	Topic	Practice Codes
Week 1: Convolutional Neural Networks	Convolutional Neural Networks Foundation Edge Detection Padding Stride Convolution over volume Pooling	Course Practice Problem 1 Course Practice Problem 2
Week 2: Convolutional Neural Networks	Case Studies using ConvNets Resnets Inception Mobile Net Efficient Net Transfer Learning Data Augmentation	Course Practice Problem
Week 3: Convolutional Neural Networks	Object Localization Landmark Detection Object Detection Sliding Windows Bounding Box IoU Non max suppression Anchor Box YOLO Region Proposal Semantic Segmentation Transpose Convolution U-Net	Course Practice Problem
Week 4: Convolutional Neural Networks	Face recognition & Neural style transfer Face Recognition One Shot Learning Siameses Net Triplet Loss Face Verification & Binary Classification Neural Style Transfer Deep ConvNets Learning Cost Function Content Cost Function Style Cost Function 1D & 3D Generalisations	Course Practice Problem 1 Course Practice Problem 2

Week 20

Continue with Deeplearning.ai specialization Link

Course Section	Topic	Practice Codes
Week 1:Sequence Models	Recurrent Neural Networks Backpropagation through time Types of RNN Language Model & Sequence Generation Novel Sequences Vanishing Gradients with RNN, GRU, LSTM, Bidirectional RNN, Deep RNN	Course Practice Problem
Week 2:Sequence Models	Word Representation Word Embeddings Embedding Matrix Word2Vec Negative Sampling GloVe Sentiment Classification Debiasing word embeddings	Course Practice Problem
Week 3:Sequence Models	Various sequence to sequence architectures Basic Models Picking Most Likely Sentence Beam search Error Analysis in Beam Search Bleu Score Attention Model	Course Practice Problem
Week 3:Sequence Models	Speech Recognition Trigger Word Detection	Course Practice Problem

Revise the progress till now from

https://github.com/mrdbourke/zero-to-mastery-ml

Then progress to Stanford CS231 Link

Helpful Projects to follow after learning

• Image Classification using CNN & Keras
• Weather Forecast & Prediction by Machine Learning
• DeepMoji
• FaceSwap
• Social Distancing with AI
• Keras Deep Speech

Additional Topic Wise Helpful Resources

• Linear Algebra
• What is Machine Learning?
• Supervised v Unsupervised v Reinforcement Learning
• Getting started with Jupyter Notebook
• Linear Regression
• Non-Linear Regression
• Multiple Variable Linear Regression
• Classification
• Logistic Regression
• Regularisation
• K Nearest Neighbours
• Classification Metrics
• Support Vector Machine
• Naive Bayes
• Support Vector Machine
• Decision Tree
• Random Forest
• Stochastic Gradient Descent
• Neural Network
• Clustering
• K Means Clustering
• Hierarchical Clustering
• Mean Shift Clustering
• DBSCAN
• EM Algorithm

Helpful Books for Reference

• Deep learning book
• Dive into Deep Learning
• Mathematics for Machine Learning
• Probabilistic Machine Learning: An Introduction

Kaggle Resources

Click

Congrats!

Once you have made till here, you can jump on to solving Kaggle and taking up a bundle of Data Science projects!