BSMA1002 · workspace

Statistics I

Syllabus, study tools, lectures, and curriculum map.

← Back to hub

More courses

Browse course shelf

Weekly outline

Syllabus

Week topics from the course map

00W00

Topic

Incomplete

01W01

Introduction and type of data, Types of data, Descriptive and Inferential

Incomplete

02W02

Describing categorical data

Incomplete

03W03

Describing numerical data

Incomplete

04W04

Association between two variables -

Incomplete

05W05

Basic principles of counting and factorial concepts -

Incomplete

06W06

Permutations and combinations

Incomplete

07W07

Probability

Incomplete

08W08

Conditional probability -

Incomplete

09W09

Random Variables -

Incomplete

010W10

Expectation and Variance -

Incomplete

011W11

Binomial and poisson random variables -

Incomplete

012W12

Introduction to continous random variables -

Incomplete

Study tools

Memorize first

Compact recall strip

Open notes

1 min

Sampling Methods — How to Collect Representative Data

Open this first for the core logic and traps.

Dive in

13 min

Bayes' Theorem — Reversing Conditional Probability, Base Rates & Updating (Layered)

Layered chapter on Bayes' theorem: reversing conditional probabilities, partition theorem, false-positive paradox, frequency grids, and question-pattern anatomy.

Dive in

14 min

Conditional Probability and Independence

Learn why the denominator changes, distinguish independence from disjointness, and practise conditional reasoning with worked examples and attempt-first checks.

Dive in

Practice Dock

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Use this when the theory is already clear and the goal is fast, clean retrieval under pressure. Mark weak questions, copy a revision pack, and move on.

TheoTeacherTextbookSpeedrun

Ask less. Recall more.

Open Practice Dock

SM-2 Active

Spaced Repetition

Flashcards

Spaced repetition engine for long-term retention of core concepts.

Start session

Not Available

Interactive Simulations

Visual Labs

Exam Mode

Adaptive Practice

Mock Exam

Set paper

Code Protocol

OPPE Simulator

Secure Comm-Link Terminal

Secure Comm-Link // @IITMadrasBSDegreeProgramme

Uplink 12ms

Syllabus Synchronization: Active

Open current cue

Syllabus Matrix Registry

Global Course Index

Open Full Frame

Master Registry

v6.4 Directory

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

BSMA1002

Foundational

4 Credits

Statistics for Data Science I

The students will be introduced to large datasets. Using this data, the students will be introduced to various insights one can glean from the data...

Deployment Hub External Docs

Execution Protocol

Module 0

Topic

Module 1

Introduction and type of data, Types of data, Descriptive and Inferential

Module 2

Describing categorical data

Module 3

Describing numerical data

Module 4

Association between two variables -

Module 5

Basic principles of counting and factorial concepts -

Module 6

Permutations and combinations

Module 7

Probability

Module 8

Conditional probability -

Module 9

Random Variables -

Module 10

Expectation and Variance -

Module 11

Binomial and poisson random variables -

Module 12