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-gramĀ |

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 |