## Data Science - Machine Learning & AI

Data Science – Machine Learning & AI Course In Pune

# Syllabus

#### DATA SCIENCE SYLLABUS

WHAT YOU WILL LEARN IN DATA SCIENCE

• Data Science Mathematics – Revising School Level Math
• Python and R Programming Languages
• Python Data Science Libraries
• R Programming Data Science Libraries
• Data Science Techniques
• Basic Of Artificial Intelligence
• Machine Learning
• Data Visualization Tools

DATA SCIENCE INTRODUCTION – MODULE I

• Data Science and It’s Concepts
• Scope Of Data Science
• Data Science Field Discussions
• Data Science Artificial Intelligence (AI) and AI Subset Machine Learning (ML) and ML Subset Deep Learning (DL) Involvements
• Analytics – Introduction
• Understanding Data, Types Of Data
• Understanding Dataset – Structured, Unstructured and Semi Structured

DATA SCIENCE MATHEMATICS – MODULE II

• Revising School Level Mathematics For Data Science
• Statistics and Probabilities
• Statistics – Descriptive Statistics
• Statistics – Inferential Statistics
• Statistics – Hypothesis and Hypothesis Testing
• Linear Algebra
• Linear Algebra – Matrix Introductions
• Linear Algebra – Matrix Types and Practical Example
• Linear Algebra – Matrix Arithmetic Operations
• Linear Algebra – Scalar and Vector
• Calculus
• Calculus – Limit
• Calculus – Differentials Calculus: Derivatives
• Calculus – Integral Calculus: Integrations

DATA SCIENCE PROGRAMMING LANGUAGES – MODULE III

Python Programming Language

• Python – Introduction
• Python – Setup and Interpreter
• Python – Keywords, Statements and Statements Syntax
• Python – Variables, Literals, Data Types and Data Structure
• Python – Operators
• Python – Functions
• Python – Input and Output (IO)
• Python – Errors and Exceptions
• Python – Modules
• Python – classes
• Python – Batteries
• Python – Package Management Tools: pip and conda
• Python – Virtual Environments

R Programming Language

• R – Introduction
• R – Setup and R Studio
• R – Objects
• R – Evaluations Of Expressions
• R – Functions
• R – Object Oriented Programming (OOP)
• R – Computing on The Language
• R – System and foreign language interfaces
• R – Exception Handling
• R – Debugging
• R – Parsers
• R – Data Science Libraries: Dplyr, Ggplot2, mlr etc.
• R – Data imports & exports

DATABASES

• Structure Query Language (SQL)
• SQL – Introduction
• SQL – Data Definition Language(DDL)
• SQL – DDL Operations – create tables or views, alter tables or views etc.
• SQL – Data Manipulation Language(DML)
• SQL – DML Operations – insert, update and delete etc.
• SQL – Select
• SQL – Constraints
• SQL – Normalizations
• SQL – Joins and indexes

VISUALIZATION TOOLS

• Tableau
• Plotly

MACHINE LEARNING (ML) – MODULE IV

• What is Machine Learning (ML)?
• Introducing Supervised ML
• Introducing Unsupervised ML
• Introducing Reinforcement Or Semi Supervised ML
• Supervised ML Algorithms (Regression and Classification)
• Unsupervised ML Algorithms (Association and Clustering)
• Reinforcement ML Algorithms
• Python Machine Learning
• R Programming Machine Learning

BIG DATA and HADOOP – MODULE V

• Basic Core Java Language Conceptual Guide
• Big Data Introduction
• Big Data Characteristics