CSE3505 - Foundations of Data Analytics

Course Overview

The "Foundations of Data Analytics" course provides the essential theoretical framework for data analysis. This course introduces students to the fundamentals of data analytics using the R/Python programming language, equipping them with practical skills to source, clean, analyze, and interpret real-world data. Students begin by exploring the analytics life cycle across diverse application domains — including lending, healthcare, financial, sports, and recommendation analytics — before building hands-on proficiency in coding. The course progresses through core data-handling skills such as working with multiple file formats, data cleaning, imputation, and conversion, followed by foundational statistical learning concepts including descriptive statistics, correlation, covariance, outlier detection, and regression analysis. Students will implement these techniques directly in Python/R through a strong practical lab component that mirrors every theoretical module. Beyond technical analytics skills, the course also builds essential professional competencies: structured document creation and knowledge sharing, effective self and work management, and collaborative teamwork and communication — preparing students not just to analyze data, but to operate effectively within a professional analytics team. More details on the topics covered can be obtained from the syllabus.

Syllabus

You can find the syllabus of this course here

Textbooks

Following are the text books for reference -

• Sanjeev Wagh, Manisha Bhende, Anuradha Thakare, ‘Fundamentals of Data Science, CRC Press, 1st Edition, 2022.
• Trevor Hastie and Rob Tibshirani, “An Introduction to Statistical Learning with Applications in R”, Springer, 2017

Tentative Schedule

Date	Lecture	Readings	Announcements
Welcome to Foundations of Data Analytics!
Tue, 7th July	Lecture 1: Meet and Greet, Introduction to Data Science applications [Slides]	Ch 1 - Sanjeev Wagh
Thu, 9th July	Lecture 2: Data Anlatics Introduction continued with different application, Need for Data Analytics etc. [Slides]	Ch 1 - Sanjeev Wagh
Tue, 14th July	Lecture 3: Data Science/Analytics life cycle [Slides]	Ch 1 - Sanjeev Wagh
Thu, 16th July	Lecture 4: Business Analytics, Data Analysis and Data Analytics, Core componenets of Business analytics [Slides]	Ch 1 - Sanjeev Wagh
Tue, 21 July	Lecture 5: Prerequisites for a Data Scientist, Tools and Skills required [Slides]	Ch 1 - Sanjeev Wagh
Thu, 23rd July	Lecture 6: Data Types, Variable types, Descriptive and inferential statistics, Sampling techniques [Slides]	Ch 2 - Sanjeev Wagh
Tue, 28th July	Lecture 7: Data Analytics life cycle, Discovery [Slides]	Ch 4 - Sanjeev Wagh
Thu,30th July	Lecture 8: Dat Preprocessing, handling missing data [Slides] [Code notebook - Foundations 1] [Code notebook - Foundations 2]	Ch 4 - Sanjeev Wagh
Tue, 4th August	Lecture 9: Dat Preprocessing, Transformation, etc. [Slides]	Ch 4 - Sanjeev Wagh
Thu, 6th Aug	Lecture 10: Dat Preprocessing, Feature selection , Model Evaluation[Slides] [Code notebook- Missing values] [Code notebook - Concat, Append] [Code notebook - Merge, Join]	Ch 4 - Sanjeev Wagh
Tue, 18th Aug	Lecture 11: Discussion - CAT1 Quetion paper solutions
Thu, 20th Aug	Lecture 12: mean, median, standard deviation, variance, correlation, covariance - Outliers [Slides]	Ch 4 - Sanjeev Wagh
Tue, 25th Aug	Lecture 13: Introduction to Document creation and knowledge sharing [Lecture notess]
Thu, 27th Aug	Lecture 14: Working with existing documents [Lecture notess]
Tue, 1st Sept	Lecture 15: Standard templates - MOM, case sudy [Lecture notess]
Tue, 8th Sept	Lecture 16: Standard templates - Technical report, CLient report [Lecture notess] [slides]
Thu, 10th Sept	Lecture 17: Self and work Management [Lecture notess] [slides]
Tue, 15th Sept	Lecture 18: Self and work Management [Lecture notess] [slides]
Tue, 17th Sept	Lecture 19: outlier detection [Lecture notess]
Thu, 8th October	Lecture 20: Practice problems
Tue, 12th Sept	Lecture 21: Team work and communication [Lecture notess]