100 Days Data Science Challenge

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 series we will try to keep everything using Python, less because of its robustness but more because the codes in Python are self explanatory. 

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 and Interview Questions
Day 60 – Web Scrapping in Python ? Why and How?
Day 61 – Regular Expression in Python
Day 62 – How to scrape 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 algorithms
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 – XGBoost
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