DATA SCIENCE

Syllabus

DATA SCIENCE SYLLABUS

WHAT YOU WILL LEARN IN DATA SCIENCE – ML AND AI

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

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 PROGRAMMING LANGUAGE (PYTHON)– MODULE II

 

  • Python – Introduction and installation
  • 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

DATABASES MODULE- III

 

  • 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

DATA SCIENCE  LIBRARIES: Numpy, Pandas, Scipy, Scikit-learn, Matplotlib  MODULE  -IV

          Introduction to Anaconda/Jupyter Notebook

Numpy:

  • Introduction to numpy
  • Difference between Python Lists and Numpy
  • Creating arrays
  • Using arrays and Scalars
  • Indexing Arrays
  • Array Random functions
  • Array Search,Sort
  • Array Filter
  • Array Input and Output
  • Exercise on Numpy

Pandas:

  • Introduction to pandas?
  • Where it is used?
  • Index objects
  • Data Structure of Pandas
  • Reindex
  • Drop Entry
  • Selecting Entries
  • Data Alignment
  • Rank and Sort
  • Loc and iloc indexing
  • Summary Statics
  • Handling Missing Data
  • Index Hierarchy
  • Exercise on Pandas

Matplotlib: Data Visualization

  • Introduction of Data Visualization
  • Introduction to Matplotlib
  • Types of plots in Matplotlib
  • 3D plotting with Matplotlib
  • Basic and Specialized Visualization Tools

Scikit-learn

  • Machine learning Process Flow
  • Feature selection and extraction in machine learning

PROBABILITY AND STATISTICS  MODULE – V

  • Probability, Mean, Median, SD, Variance
  • Probability distributions, Poisson distribution, Binomial distribution.

ARTIFICAL INTELLIGENCE AND MACHINE LEARNING (ML) – MODULE VI

 

  • 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

 

MACHINE LEARNING Algorithms – MODULE VII

  • Linear Regression
  • Polynomial Regression
  • Multinomial Regression
  • Train/Test method
  • KNN
  • K Means
  • Logistic Regression
  • Support Vector Machine 2.5.6
  • Decision Tree
  • Naïve Bayes
  • Ensemble Methods – Random Forest, Boosting and Optimization
  • Clustering and PCA
  • Recommendation system
  • Time Series Analysis

Deep Learning – MODULE VIII

  • Introduction to Deep Learning
  • Importance of Deep Learning
  • Types of Deep Learning
  • Introduction to Tensor flow & Keras
  • Artificial Neural Network
  • Convolutional Neural Network
  • Recurrent Neural Network
  • Introduction to Computer Vision, Open CV library
  • Recurrent Neural Network
  • Introduction to Natural Language Processing
  • Using Regex in NLP
  • Category of Techniques for NLP
  • Spacy vs NLTK
  • Tokenization in Spacy
  • Stemming and Lemmatization
  • Name Entity Recognition