| Topic |
| Day 1 – What is Data Science and how to make a career in it |
| Day 2 – What is Data ? Why Data Science? |
| Day 3 – Business Case Study 1 – How to solve a Business Problem |
| Day 4 – Business Case Study 2 – How to solve a Business Problem |
| Day 5 – Business Case Study 3 – How to solve a Business Problem |
| Day 6 – How to guesstimate? |
| Day 7 – Solving a couple of guesstimate question |
| Day 8 – Puzzles and aptitude problem asked in Data Science interviews |
| Day 9 – Introduction to Data Science, What to install ? Where to start with? |
| Day 10 – SQL Basics to get you started |
| Day 11 – SQL tricky questions |
| Day 12 – SQL one table 30 Questions |
| Day 13 – SQL revision with Intermediate level Interview Questions |
| Day 14 – Basic Statistics to get you started |
| Day 15 – Basic Statistics to get you started Cont. |
| Day 16 – Basic Statistics to get you started – Tests in Stats |
| Day 17 – Basic Statistics to get you started 1 – Correlation |
| Day 18 – Statistics Interview questions |
| Day 19 – Internmediate level interview
Questions |
| Day 20 – Welcome to Python – Install and understand how to run code in Python |
| Day 21 – Python 1 – Basic Data types and Data Structure |
| Day 22 – Python 2 – Conditional Statements |
| Day 23 – Python 3 – Loop |
| Day 24 – Python 4 – Functions |
| Day 25 – Python 5 – SQL in Python |
| Day 26 – Python tricky interview questions |
| Day 27 – Reading and writing files in Python |
| Day 28 – Python – Visualization |
| Day 29 – Python – Visualization 2 |
| Day 30 – What is Machine Learning? |
| Day 31 – Supervised Learning Overview |
| Day 32 – Unsupervised Learning Overview |
| Day 33 – Before you start Modeling – What is training and test dataset |
| Day 34 – Before you start Modeling – Feature Engineering |
| Day 35 – Before you start Modeling – How to calculate the performance of your model? |
| Day 36 – Statistics in Python |
| Day 37 – Statistics in Python 2 |
| Day 38 – First Step of Modeling – Cleaning data 1 |
| Day 39 – First Step of Modeling – Cleaning data 2 |
| Day 40 – First Step of Modeling – Cleaning data 3 |
| Day 41 – Understanding Linear Regression |
| Day 42 – Creating your first Linear Regression model |
| Day 43 – Interpreting Linear Regression model |
| Day 44 – Logistic Regression |
| Day 45 – Creating your first Logistic Regression model |
| Day 46 – Interpreting Logistic Regression model |
| Day 47 – Cross Validation and its uses |
| Day 48 – Classification using KNN |
| Day 49 – Understanding and implementing KNN in Python |
| Day 50 – Hyperparameter and Synopsis of complete Supervised Learning |
| Day 51 – Unsupervised Learning Basics |
| Day 52 – Understanding Clustering and its use case |
| Day 53 – Understanding Principal Component Analysis |
| Day 54 – Unsupervised Learning interview Questions |
| Day 55 – Natural Language Processing in Python ? What and Hows about NLP |
| Day 56 – TF-IDF and Word correlation |
| Day 57 – N-gram |
| Day 58 – Sentiment analysis in Python |
| Day 59 – NLP Revision and Interview Questions |
| Day 60 – Web Scrapping in Python ? Why
and How? |
| Day 61 – Regular Expression in Python |
| Day 62 – How to scrap a page? |
| Day 63 – Web Scrapping project with code |
| Day 64 – Forecasting in Python |
| Day 65 – Understanding Trend,
seasonality, cyclicity |
| Day 66 – Forecasting using Linear
Regression |
| Day 67 – ARIMA explanation and code |
| Day 68 – ARIMAX and some fancy algorthims |
| Day 69 – Ensemble models for forecasting |
| Day 70 – Neural Network Basics |
| Day 71 – Neural Network Basics |
| Day 72 – Neural Networks Intermediate |
| Day 73 – Neural Networks Intermediate |
| Day 74 – Neural Networks complete
code |
| Day 75 – Neural Networks complete code
and explanation |
| Day 76 – The Best Algorithm in Python –
XGB |
| Day 77 – Tree Based Modeling – Basic |
| Day 78 – Tree Based Modeling –
Intermediate |
| Day 79 – Tree Based Modeling –
Intermediate |
| Day 80 – Tree Based Modeling Code |
| Day 81 – Fraud Detection |
| Day 82 – Fraud Detection 2 |
| Day 83 – Supply Chain Analytics |
| Day 84 – Supply Chain Analytics 2 |
| Day 85 – Supply Chain Analytics 3 |
| Day 86 – HR Analytics Case Study |
| Day 87 – HR Analytics Case Study 2 |
| Day 88 – Financial Analytics Case Study |
| Day 89 – Financial Analytics Case Study 2 |
| Day 90 – How to solve the titanic problem
at Kaggle? |
| Day 91 – Introduction to PySpark |
| Day 92 – PySpark intermediate |
| Day 93 – Creating a recommendation engine
using PySpark |
| Day 94 – Creating a recommendation engine
using PySpark 2 |
| Day 95 – Creating a recommendation engine
using PySpark 3 |
| Day 96 – Chatbot using Python |
| Day 97 – Chatbot using Python 2 |
| Day 98 – Chatbot using Python 3 |
| Day 99 – Chatbot using Python 4 |
| Day 100 – Are we done ? No, we have just
started |