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Data Science with R Programming & Python

Data Science using Python& R Programming

(Duration : 3months)

Module 1 : Overview of Data Science

  • Introduction to Data Science
  • Use Cases
  • The need for Business Analytics
  • Data Science Life Cycle
  • Different tools available for Data Science

Module 2 : R Programming

  1. Introduction to R Programming
  2. Installing R and R-Studio
  3. R packages and R Operators
  4. if statements and loops (for, while, repeat, break, next), switch case.

Module 3 : Python Programming

  • Introduction to Python
  • Installation and working with Python
  • Understanding Python variables
  • Python Operators
  • Python blocks
  • Flow Control Conditional blocks using if, else and elif
  • Simple for loops in python
  • For loop using ranges, string, list and dictionaries
  • Use of while loops
  • Loop manipulation using pass, continue, break and else
  • Programming using Python conditional and loops block

Module 4: Data Types

  1. Declaring and using Numeric data types
  2. Using String data type and operations
  3. Defining List
  4. Use of Tuple data type

Module 5 : Functions, Modules & Packages

  • Organizing python codes using functions
  • Organizing python projects into modules
  • Importing own module & external modules
  • Understanding Lamda function in python
  • Programming using functions, modules and external packages

 

Module: 6 String, List & Dictionary

  • Building blocks of python programs
  • Understanding String in-build methods
  • List manipulation using in-build methods
  • Dictionary manipulation
  • Programming using String, List and Dictionary in-build functions

Module: 7 File Operation

  • Reading config files in python
  • Writing log files in python
  • Understanding read functions, read(), readline() and readlines()
  • Understanding write functions, write() and writelines()
  • Manipulating file pointer using seek Programming using file operations

Module: 8 Object Oriented Programming

  • Concept of class, object and instances
  • Constructor, class attributes and destructors
  • Inheritance, Overlapping and Overloading operators
  • Adding and retrieving dynamic attributes of classes

Module: 9 Regular Expression

  1. Pattern matching and searching
  2. Pattern searching using regex, real time parsing of data using regex
  3. Password, email, url validation using regular expression

Module: 10 Exception Handling

  1. What is exception handling
  2. Safe guarding file operation using exception handling
  3. Handling error code
  4. Programming using Exception handling

Module: 11 Multithreading

  1. Understanding threads
  2. Forking threads
  3. Synchronizing the threads

 

Advanced Python

Module 1: Overview

  • Python Iterators
  • Python Generators
  • Python Closures
  • Python Decorators
  • Python @property

Module: 2 Python XML & JSON parser

  • What is XML?
  • Difference between XML and HTML
  • Difference between XML and JSON
  • How to Parse XML
  • How to write XML
  • How to parse JSON
  • How to write JSON

Module : 3 Python Data Communication

  • Creating a Database with SQLite 3,
  • Creating a Database Object.
  • Python MySQL Database Access
  • DML and DDL Operations with Databases
  • Performing Transactions
  • Handling Database Errors
  • Disconnecting Database

 Introduction To Machine Learning With Python (Including Project)

Module: 1 Introduction to Machine Learning

  • What is Machine learning?
  • Machine Learning Methods
  • Predictive Models
  • Descriptive Models
  • What are the steps used in Machine Learning?

Module: 2 Regression

  • Simple Linear Regression
  • Multiple Linear Regression
  • Bias-Variance trade-off

Module: 3 Classification

  • Logistic Regression
  • K-Nearest Neighbors (K-NN)
  • SVM
  • Decision Trees
  • Random Forest

Module: 4 Clustering

  • K-means
  • Hierarchical
  • DBSCAN

Module: 5 Dimensionality Reduction

  • Linear discriminant analysis
  • Principal component analysis

Soft Skills& Interview Techniques

  • Interview Techniques
  • Frequently Asked Questions
  • Group Discussion
  • Resume Writing
  • Mock Test Based on MNC Test Pattern

Evaluation

  • Technical Assignments
  • Technical Test
  • Technical Interview