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
