Data Science comprises of innumerable topics. The aim of this 100 Days series is to get you started assuming that you have no prior knowledge of any of these topics.

In this

You are expected to give anywhere between 1-2 hours per day. Don’t jump to the next topic until and unless you are done with the previous day syllabus. Following are the topics which we are planning to cover in the next 100 Days of Challenge:-

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 |

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 |