Course Details

1. Introduction to Data Science

Data Science: Overview and Terminology

Data Scientist: Role, Skill set and Job opportunities

Data science applications

2. Understanding the Statistics and Probability

Introduction to Descriptive and Inferential statistics

Measures of central tendency

Measures of variability

Central limit theorem and Normal distribution

Confidence Interval and error

Hypothesis testing

Probability Distributions

Probability types : Marginal, Joint and Conditional

Bayes Theorem

3. Basics of Machine Learning

Introduction to Machine learning

Basics of model building: Training set and Test set

Supervised learning models

Accuracy and Confusion matrix

Unsupervised learning models

4. Implementation of machine learning models using Python

Python packages and libraries for model building and data analysis

Building simple supervised learning models to train and test using Python

Building unsupervised learning models using Python

Duration: 30 hours

Fees: INR 2000

Intake: 30 Students

Credit: 2

*Open for all Information Technology, Computer Science, Mathematics and Statistics students.

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