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
Please click here to download curriculum content