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
  • Big Data and Hadoop

DATA SCIENCE INTRODUCTION – MODULE I

  • Data Science and It’s Concepts
  • Scope Of Data Science
  • Data Science Business and Business Intelligence (BI) Use Cases
  • 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 – Threading and Multi-threading
  • 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
  • Hadoop Introduction
  • Hadoop Setup and Configurations
  • Hadoop Modules Introduction
  • Hadoop HDFS
  • Hadoop MapReduce
  • Hadoop Yarn/MapReduce Version 2

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