Course Dashboard | Summer Analytics

Summer Analytics 2025: Welcomes you aboard to this wonderful course.

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WEEK 1	WHAT'S IN THERE	TASK 1	TASK 2	TASK 3
Day 0	Getting Started by setting up Anaconda environment.	Kaggle - Mastering Kaggle Notebooks: A Complete Tutorial How to Add Kaggle Dataset to Kaggle Notebook	Installing anaconda for Windows Installing anaconda for Mac Installing anaconda for Linux
Day 1	Hey there, excited to start learning? First, we will begin with learning the basics of Python and understanding ML.	Beginner Tutorial for Python Programming (upto 30 mins)	Moving Ahead (from 30 mins to 1 30 hr)	What is ML?
Day 2	We will continue with basics of Python and gain an overview of NumPy.	Basics of Python continued (1 30 hr to end)	Numpy Video	Numpy Notebook
Day 3	Gaining an overview of Pandas. You will be using this extensively in your Data Science journey.	Pandas Overview	Kaggle Micro-course on Pandas (only exercise)	Pandas Notebook
Day 4	Today, we will look into Matplotlib and understand common problems solved by ML.	Introduction to Matplotlib	GfG Article(for latest version) - Optional Task	Common problems solved by ML
Day 5	To conclude the week, we will understand Seaborn and Descriptive Statistics.	GfG Article for Seaborn Library	Data Types in Statistics	Central Tendencies (3-7 videos) & Normal Distribution (18-20)

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WEEK 2	WHAT'S IN THERE	TASK 1	TASK 2	TASK 3
Day 1	Hey, excited for Week 2? Often the data we deal with can have various issues like missing values, categorical values and outliers. Today we will learn about basic techniques to deal with such issues!	Introduction to Feature Engineering Outlier Analysis	Handling Missing Values	Practical Handling Missing Values
Day 2	Today’s light on tech—we'll explore ML basics, supervised vs. unsupervised learning, and how to handle categorical data. Optional math refresher included!	Handling Categorical Variables	Supervised and unsupervised learning	(optional)[Linear Algebra, Refresher required for those who don't have mathematical base: (watch chapters 1,2,3,4,9,14 )]
Day 3	Today we dive into the basics of ML with Linear Regression, Cost Function, and Gradient Descent—simple concepts with powerful impact!	Linear Regression Blog Loss Function Blog	Linear Regression with One Variable	Cost Function Explained
Day 4	Time to level up—today we tackle Linear regression with Multiple features and get hands-on with Scikit-learn, plus a sneak peek into Logistic Regression!	Linear Regression with Multiple Variables (Videos 21 - 24)	Linear Regression with Scikit-learn	Logistic Regression Blog
Day 5	Today we will be introduced to our first ever classification model, Logistic Regression. Learn it inside out!	Logistic Regression Videos 31 - 36	Logistic Regression with SciKit-learn Logistic Regression from scratch	Data Streaming 1 Data Streaming 2

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WEEK 3	WHAT'S IN THERE	TASK 1	TASK 2	TASK 3

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WEEK 4	WHAT'S IN THERE	TASK 1	TASK 2	TASK 3

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WEEK 5	WHAT'S IN THERE	TASK 1	TASK 2	TASK 3

LINK	DEADLINE	INSTRUCTIONS
Assignment 1	June 4th, 2025 (EOD)
Quiz 1	June 4th, 2025 (EOD)	Complete the assignment first, as the quiz includes questions from it and requires submission in quiz form.

LINK	DATE	ABOUT
Session 1 - Aditi Agrawal	May 25th, 2025	Introductory Session & A Guide to Data Analysis
Session 2 - Tarun Jain	June 1st, 2025	Advanced ML Algorithms & Model Tuning

WHAT'S IN THERE	LINK(S)

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