Program Description

  • Introduction to Data Science and the working concepts of ML/AI
  • Introduction to the basics of Python Programming
  • Comprehensive overview into the foundational concepts of Data Science i.e., Statistics, Linear Algebra and Optimization
  • Introduction to Data Visualization techniques
  • In-depth explanation into the working concepts of Predictive Modeling Algorithms. Extensive hands-on sessions to practice the implementation methods of techniques learnt
  • Introduce the participants to real-life applications of Data Science – a case study approach

Key Highlights

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Describe a flow process for data science problems (Remembering)

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Classify data science problems into standard typology (Comprehension)

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Develop Python codes for data science solutions (Application)

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Correlate results to the solution approach followed (Analysis)

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Assess the solution approach (Evaluation)

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Self-Paced Learning Learn from IIT Madras Faculty & Industry Practitioners

Learning Format

Online

Duration

12 weeks

Certified by

IITM Pravartak Technologies Foundation
Technology Innovation Hub (TIH) of IIT Madras and
GITAA

Program Fee

Rs. 15,000 + GST

Program Brochure

Education Qualification

UG & PG students, Industry Professionals

Learning Schedule

Introduction to data science Why Python for data science

Setting working directory
Creating and saving script files
Executing pieces of code
Commenting
Clearing the environment and console
Removing variables from environment
Commenting script files
Creating variables in Python and naming conventions
Arithmetic operators
Logical operators
Data types and related conversions

Strings
Lists
Arrays
Tuples
Dictionary
Sets
Range

ndArray

Descriptive statistics
- Measures of central tendency
- Measures of spread
- Distribution of mean and variance
- Sampling basics
- Notion of probability

Reading files
- Comma separated value files
- Tab-delimited files
- Excel files
Exploratory data analysis
Data preparation and pre- processing

Scatter Plot
Bar Plot
Histogram
Box plot
Pair plot

If-else-if family
For loop
For loop with” if break”
While loop

Introduction to hypotheses testing
Performance of hypotheses tests
Test for mean (one sample)
Test for differences in means (two sample test)
Test for differences in variances (F test)

Eigen values & Eigen vectors
- Singular Value Decomposition
Understanding independence of variables
Understanding relationships between variables

Basics of optimization - objective function, constraints,
decision variables
Types of optimization problems
Statement of first order KKT necessary conditions
Basic concepts in multi-objective optimization
Introduction to optimization viewpoint in predictive
modelling and machine learning

Correlation
Basics of regression
Ordinary least squares
Model building
Model assessment and improvement
Diagnostics
Multiple linear regression (model building & assessment)
Random forest & Decision tree

Classification
- Logistic regression
- K nearest neighbours
Clustering
- K means

Dimensionality reduction methods
- Principal component analysis and its variants
Participants

Support vector machine
Neural networks



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