Phase 1 : Python Environment Setup and Essentials
Introduction to Data Science with Python
  • What is data science??cl
  • Why should you think career as Data Scientist?
  • Best Source of Data Science Talent
  • Why Python??
  • What is Python?
  • Python Rank in IT domain
  • Why Is Python So Popular?
  • Properties of Python
  • What do businesses use python for?
  • Working as an analyst/scientist
Python Environment Setup and Essentials
  • Introduction to Python
  • Origin of the Name….
  • Installing Python on Windows , Linux and Mac
  • Which Python is right for you ?
  • Installation Steps on Windows
  • Interactive Shell
  • Python IDLE as first IDE
  • Installing Anaconda
  • Anaconda Navigator
  • Working with Spyder IDE
  • Working with Jyupter IDE
  • Working with Jypyter Notebook
Operators & Control Structures
  • Operators & Expressions
  • Arithmetic Operators
  • Relational Operators
  • Logical Operators
  • Control Statements
  • If statement
  • If else statement
  • If elif statement
  • For and While Loops
  • Break and Continue in Loops
Data Structures in Python
  • Native Data Types
  • Immutable & Mutable Data Types
  • Manipulating Strings
  • List and List Comprehension
  • Tuple Immutable Data Type
  • Set operations
  • Dictionary as key-value pair Json
Writing Functions in Python
  • Function Parameters
  • Local Variables
  • The global statement
  • Default Argument Values
  • Keyword Arguments
  • VarArgs parameters
  • The return statement
  • Lambda, Map and Filter functions
  • DocStrings
Writing Object Oriented Program in Python
  • Writing our first Class
  • The Self clause
  • Methods in Classes
  • The ‘init’ – Initialization of Class
  • Class And Object Variables
  • Inheritance in Python
DataBase connection with Python
  • Implement Database using SQLite
  • Perform CRUD operations on SQLite database
  • Create Tables in Database
  • Read and select rows from tables
  • Update rows of table
  • Delete rows from table
File Handling in Python
  • Open a File
  • Read from a File
  • Write into a File
  • WITH clause for file
  • The Pickle Library (Serialize and De-serialize Python Objects)
  • The Shelve Library (Overcome the limitation of Pickle)
Exception Handling in Python
  • What is Exception?
  • Handling an exception
  • The except Clause with No Exceptions
  • The except Clause with Multiple Exceptions
  • The try-finally Clause
  • Argument of an Exception
  • Raising an Exception
Phase 2 : Data Science with Python
Overview of Analytics
  • What is Analytics
  • What is Data Analytics
  • What are the different types of variables
  • What is Data
  • What is Statistics
  • Data on Data Analytics
Descriptive Statistics
  • What is Analytics
  • What is Data Analytics
  • What are the different types of variables
  • What is Data
  • What is Statistics
  • Data on Data Analytics
Mathematical Computing with Python (NumPy)
  • NumPy Overview
  • Properties, Purpose and Types of nd array
  • Class and Attributes of nd array Object
  • Basic Operations: Concept and Examples
  • Accessing Array Elements: Indexing, Slicing, Iteration
  • Indexing with Boolean Arrays
  • Broadcasting
Data Visualization using Matplotlib
  • Matplotlib Overview and Installation
  • First Plot with button details
  • Pyplot with Plot() , Subplot()
  • Legends, Titles, and Labels
  • Bar Charts and Histograms
  • Scatter , Stack Plots and Pie Charts
Data Manipulation with Python (Pandas)
  • Introduction to Pandas
  • Data Structures
  • Series
  • DataFrame
  • Missing Values
  • Data Operations
  • Data Standardization
  • Pandas File Read and Write Support
  • SQL, Group by, Merge & Concatenate with Pandas
Case Study I : Restaurant Data Analysis with SeaBorn Library
  • Reading the Data Set
  • Analysing Data Set with Head & Tail
  • DistPlot Analysis
  • JointPlot Analysis
  • PairPlot Analysis
  • Bar & Count plot Analysis
  • Box Plot analysis
  • Lmplot analysis
Credit Score Case Study -Part_1
  • Reading the Data Set
  • Analysing Data Set with Head & Tail
  • Missing Data manipulation on Data Set
  • Categorical to Numerical value conversion of features
  • Storing the DataFrame object with Pickle
Scientific Computing with Python (SciPy)
  • SciPy and its Characteristics
  • SciPy sub-packages
  • SciPy sub-packages–Integration
  • SciPy sub-packages–Optimize
  • LinearAlgebra
  • SciPy sub-packages–Statistics
Phase 3 : Advances in Data Science
Machine Learning with Python (Scikit-Learn)
  • Introduction to machine learning
  • Naive Bayes Classification
  • Linear Regression
  • Logical Regression
  • Time Series
  • Support Vector Machines (SVM)
  • Decision Trees and Random Forests
  • K-Nearest Neighbor Classifier (k-nn)
  • K-Means Clustering
  • Neural Networks
Credit Score Case Study -Part_2
  • Loading the DataFrame object using Pickle
  • Train Test Split the Data Set
  • Data Preprocessing and Standardization of Data Set
  • Training the model using different ML models
Conceptualization of R Programming
  • Introduction to R programming language
  • Installation of R commander
  • Working with R Commander
  • Installation of R Studio
  • Understanding R Studio
  • Working with R Studio
  • Installing packages in R
Data Structures in R
  • Vector
  • Matrix
  • List
  • Data frame
  • Manipulation on Data structures
  • Reading CSV file in DataFrame
Control Statements & Functions
  • Conditional Programming
  • If statements
  • if else statements
  • Loops in R programming
  • For loop
  • while loop
  • repeat until loop
  • Functions in R programming
Graphical Features of R
  • Graphical Analysis
  • Box Plots
  • Line Charts
  • Bar Charts
  • Histograms
  • Pie Charts
Machine Learning Methods with R
  • Linear Regression
  • Support Vector Machines
  • K-NEAREST NEIGHBOR CLASSIFIER (k-nn)
  • k-Means Clustering
Python in Big Data using MongoDB
  • Why MongoDB??
  • Installation of MongoDB
  • Simple CRUD commands of MongoDB
  • Python and MongoDB
  • Python and Twitter API Integration
  • Python, MongoDB and Twitter API Integration
  • Sentiment analysis of tweets in Python
Phase 4 : Job Orientation
JOB READINESS
  • JOB READINESS
  • Resume Building
  • Interview Preparation
  • One to One MOCK INTERVIEWS
  • Live Group Discussion
  • Technical Job search training
Industry Interaction
  • Guest Lectures by Industry Experts
  • Group Project development and presentation
  • Live seminar through Webinar
Paid Internships

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