This page will contain topics and links to the learning modules/tutorials that we are going to cover during the 5-Week Data Data Science Bootcamp.
FAQ’s for learners who enrolled for the bootcamp: https://bit.ly/DPhiBootcampFAQ
Week #0
Introduction to Data Science and Python Crash Course
Pre-work – Setting up environment to code first program in python
Day#0 – Basic of Python and Intro to Data Types
Day#1 – Data Structures – Lists and Tuples. Intro to Data Science & its real-life applications
Day#2 – Introduction to Loops and Range
Day#3 – Conditional Statement and Dictionaries
Day#4 – Functions, Methods and Packages
Day #5 – Introduction to NumPy
Day #6 – NumPy Practice
Day #7 – Solutions to Practice Exercises
Week #1
Day #1 – Introduction to Pandas
Day #2-3 – Self-practice on Pandas
Day #4 – Intro to Data Visualization using Matplotlib and Seaborn
Day #5 – More resources on Matplotlib, Seaborn and Assignment 1
Day #6 – Data Visualization and Assignment
Day #7 – Linear Algebra and Basic Statistics
Week #2
Day #1 – Exploratory Data Analysis
Day #3 – Basics on Statistics and EDA
Day #4 – Pre-Session Slides on Imbalanced Dataset
Day #4 – Handling Class Imbalance
Day #5 – Cybersecurity Analysis
Day #6 – Imbalanced Dataset Docmented Notebook
Day #7 – Assignment Solutions
Week #3
Day #1 – Building your first ML Model Notebook
Day #2 – Build your first ML Model Slides
Day #2 – More on Decision Trees
Day #3 – Linear Regression
Day #4 – Session on Linear Regression
Day #5 – Logistic Regression Notebook (Optional)
Day #6 – Assignment & Quiz are put up on Learning Platform
Day #7 – Logistic Regression Intuition and Notebook
Week #4
Day #1 – Model Evaluation and Hyperparameter tuning
Day #2 – Practice problems on Linear Regression and Logistic Regression
Day #3 – Prep material – Ensemble Models
Day #4 –
Random Forest Model Notebook
Bias and Variance
Day #5 – ANN Slides and Notebook
Day #6 – Building a basic logistic regression
Day #7 – Gave a problem to solve on learning platform
Week #5
Day #1 – Input variables, target variable, train and test data intuition – Machine Learning
Titanic Dataset Optimisation – Notebook
Day#2 and #3 – Module for Regression Algorithms & its evaluation metrics
Module for Classification Algorithms & its evaluation metrics
Day #4 – Feature Selection and Feature Importance
Day #5-6 – Feature Selection and importance session slides
Notebook of random forest by Prasad
Note: This page will be updated as and when we progress further with the bootcamp.
Cheers,
Chanukya Patnaik
Team DPhi