IIT Madras - Become a Data Science Professional with IIT Certification in Advanced Programming 

Why Python

Python IDE

Hello World Program

Variables & Names

String Basics

List

Tuple

Dictionaries

Conditional Statements

For and While Loop

Functions

Numbers and Math Functions

Common Errors in Python

Functions as Arguments

List Comprehension

File Handling

Debugging in Python

Class and Objects

Lambda, Filters and Map

Regular Expressions

Python PIP

Read Excel Data in Python

Python MySQL

Iterators

Pickling

Python JSON

Introduction to algorithmic Thinking

Algorithm Efficiency and time complexity

Example algorithms - binary search,

Euclid’s algorithm

Data structures - stack, heap and binary trees

Memory Management/Technologies

Best Practices – Keeping it simple, dry code, naming Conventions, Comments and docs.

Assessment

Introduction to Pandas

Series Data Structure - Querying and Indexing

Data Frame Data Structure - Querying,

Indexing and loading

Merging data frames

Group by operation

Pivot table

Date/Time functionality

Example: Manipulating Data Frame

No Schema

Install MongoDB

How MongoDB Works?

Insert First Data

CRUD Operations

Insert Many

Update and Update Many

Delete and Delete Many

Projection

Intro to Embed Documents

Embed Documents in Action

Adding Arrays

Fetching Data From Structured Data

Schema Types

Types of Data in MongoDB

Relationship between data's

One to One using Embed Method

One to One using Reference Many

One to Many Embed

One to Many Reference Method

Assessment - MongoDB

Basics of sample and event space

Axioms of probability

Total Probability theorem and Bayes Theorem

Random variables, PMF and CDF

Discrete Distributions - Bernoulli, Binomial and Geometric

Expectation and its properties

Variance and its properties

Continuous Distributions - uniform, exponential and normal

Sampling from continuous distributions

Simulation techniques - simulating in NumPy

Assessment

CLT and it’s proof

Chi-squared distribution and its properties

Point and Interval Estimators

Estimation technique - MLE

Interval Estimator of ? with unknown ?

Examples of estimators

Hypothesis Testing - I

Hypothesis Testing - II

Hypothesis Testing - III

Assessment

Styling Tabulation

Distribution of Data - Histogram

Box Plot

Data Visualization - Recap

Pie Chart

Donut Chart

Stacked Bar Plot

Relative Stacked Bar Plot

Stacked Area Plot

Scatter Plots

Bar Plot

Continuous vs Continuous Plot

Line Plot

Line Plot Covid Data

Assessment

Dash by Plotly setup

Dash core components

Style our Dash Application

Callbacks,

Adding interactivity to our Dash Apps using

Callbacks

Handling missing data

Techniques to impute missing values

Encoding the data

Outlier detection and correction

Meaningful data transformation

Assessment

How computers process and understand images,

Pixel

Basic Properties of Images

Greyscale, Processing Pixel Values

Masking

Image Processing

Text data preprocessing

Cleaning Text Data

Exploratory Data Analysis on Image and text data

Assessment

Expert systems and 6 Jars

Supervised Learning - Regression and

Classification

Evaluation metrics and measuring accuracy

Introduction to regression

Interpreting models

Feature selection

Regularisation - Ridge and Lasso

Assessment

Introduction to classification

Evaluation metrics - TP, FP and AUC

Classification using logistic regression

Classification using KNN

SVM

Assessment

Introduction to decision trees

Building, pruning and interpreting trees

Ensemble techniques - Bagging and boosting

Random forests

Boosted trees - Gradient boosting

Assessment

Comparison of supervised

Techniques - when to use what?

Do s and Dont s while training ML models

Handling imbalanced data

Undersampling

Oversampling

Other methods - ROSE, SMOTE, etc.

Assessment

Introduction to unsupervised learning

Market Basket Analysis

K means algorithm

Assessment

Syntactic Analysis

Tokenization

Part of Speech Tagging (PoS Tagging)

Lemmatization and Stemming

Stop word removal

Semantic Analysis

Word sense disambiguation

Relationship extraction

Sentiment Analysis, Text extraction

Case Study I : Credit Card Fraud detection

Case Study II : Airline Customer segmentation

Case Study III : Product recommendation engine

Mock Interviews