Course Detail

Course Description

Python, Data Analytics & Data Visualisation Training Syllabus

MODULE -1 GETTING STARTED PYTHON PROGRAMMING

• What is Python and brief history
• Why Python and who use Python
• Discussion on Python 2 and 3 Unique features of Python
• Discussion on various IDE’s
• Demonstration of practical use cases
• Python use cases using data analysis

MODULE 2 - SETTING UP AND INSTALLATIONS

• Installing python
• Setting up Python environment for development
• Installation of Jupyter Notebook
• How to access our course material using Jupyter
• Write your first program in python

MODULE 3 - PYTHON OBJECT AND DATA STRUCTURES OPERATIONS

• Python built-in functions
• Number objects and operations
• Variable assignment and keywords String objects and operations
• Print formatting with strings
• List objects and operations
• Tuple objects and operations
• Dictionary objects and operations
• Sets and Boolean
• Object and data structures assessment test

MODULE 4 - PYTHON STATEMENTS

• Introduction to Python statements
• If, elif and else statements
• Comparison operators
• Chained comparison operators
• What are loops
• For loops
• While loops
• Useful operators
• List comprehensions
• Statement assessment test
• Game challenge

MODULE - 5 UDF FUNCTIONS AND METHODS METHODS

• Methods
• What are various types of functions
• Creating and calling user defined functions
• Function practice exercises
• Lambda Expressions
• Map and filter
• Nested statements and scope
• Args and kwargs
• Functions and methods assignment
• Milestone Project (Making tic-tac-toe in python)

Module - 6 FILE AND EXCEPTION HANDLING

• Process files using python
• Read/write and append file object
• File functions
• File pointer and operations
• Introduction to error handling
• Try, except and finally
• Python standard exceptions
• User defined exceptions
• Unit testing
• File and exceptions assignment

Module - 7 PYTHON MODULES AND PACKAGES

• Python inbuilt modules
• Creating UDM-User defined modules
• Passing command line arguments
• Writing packages
• Define PYTHONPATH
• name and main

Module - 8 OBJECT ORIENTED PROGRAMMING

• Object oriented features
• Implement object oriented with Python
• Creating classes and objects
• Creating class attributes
• Creating methods in a class
• Inheritance
• Polymorphism
• Special methods for class
• Assignment - Creating a python script to replicate deposits and withdrawals in a bank with appropriate classes and UDFs.

Module - 9 ADVANCED PYTHON MODULES

• Collections module
• Datetime
• Python debugger
• Timing your code
• Regular expressions
• StringIO
• Python decorators
• Python generators

Module - 10 PACKAGE INSTALLATION AND PARALLEL PROCESSING

• Install packages on python
• Introduction to pip, easy_install
• Multithreading
• Multiprocessing

MODULE- 11 REST APIS WITH FLASK AND PYTHON

• REST principles
• Creating application endpoints
• Implementing endpoints
• Using Postman for API testing
• Module - 12 Rest Api Integration With Databases
• For Web App Development
• CRUD operations on database.
• REST principles and connectivity to databases

MODULE- 12 REST API INTEGRATION WITH DATABASES FOR WEB APP DEVELOPMENT

• CRUD operations on database.
• REST principles and connectivity to databases.
• Creating a web development API for login register and connecting it to database.
• Deploy the API on local server.

MODULE- 13 DATA WRANGLING WITH PYTHON AND EDA (EXPLORATORY DATA ANALYSIS) :

• Data Analysis using Numpy and Pandas
• Data Visualization using Matplotlib and Seaborn
• Dealing with missing values, NaN values
• Standard scaler
• Correlation-based processing
• Divide data in test and train splits to supply to machine-learning algorithms
• Time series analysis

• Data Manipulation as per requirement
• Derive columns based on rules
• Filtering data
• Compression and decompression techniques

MODULE- 14 DATA ANALYSIS WITH PYTHON

• Introduction to data analysis
• Why Data analysis?
• Data analysis and Artificial intelligence bridge
• Introduction to Data Analysis libraries
• Data analysis introduction assignment challenge

MODULE- 15 DATA ANALYSIS USING NUMPY

• Introduction to Numpy arrays
• Creating and applying functions
• Numpy Indexing and selection
• Numpy Operations
• Exercise and assignment challenge

MODULE- 16 DATA ANALYSIS IN PYTHON WITH PANDAS AND ADVANCED ANALYSIS

• Pandas series
• Introduction to DataFrames
• Missing data
• Groupby
• Merging, joining and Concatenating
• Operations
• Data Input and Output
• Pandas in depth coding exercises
• POC - Analysis of e-commerce dataset using pandas
• POC - Getting insights on employee salaries data using data analysis in python

MODULE- 17 DATA VISUALIZATION WITH PYTHON

• Plotting using Matplotlib
• Plotting Numpy arrays
• Plotting using object-oriented approach
• Subplots using matplotlib
• Matplotlib attributes and functions
• Matplotlib exercises
• Seaborn visualization
• Categorical Plot using Seaborn
• Distributional plots using Seaborn
• Matrix plots
• Grids
• Seaborn exercises
• Project - Getting insights using python analysis and visualizations on finance credit score data. Pandas built-in data visualization
• Data visualization assignment

Python and Web based Project Development