Courses Description

Python has been widely accepted as the programming language of choice for high-level data processing because of its no-mess syntax, easy readability, and easy comprehension. Its numerous data structures, classes, nested functions, iterators, the flexible function calling syntax and extensive libraries make it apt for data analyzing, extracting information and making informed business decisions.

Course content

• Python Basics
  • Install Anaconda
  • Data Types & Variables
• String and Regular Expressions
  • String Operations
  • Uses of Regular Expressions
  • Methods of Regular Expressions
  • Commonly used Operators
• Data Structures in Python
  • Arrays
  • Lists
  • Tuples
  • Dictionaries
  • Sets
• Control & Loop Statements in Python
  • For Loop
  • While Loop
  • Break Statement
  • Next Statements
  • Repeat Statement
  • if, if…else Statements
  • Switch Statement
• Functions & Classes in Python
  • Writing your own functions (UDF)
  • Calling Python Functions
  • Functions with Arguments
  • Calling Python Functions by passing Arguments
  • Lambda Functions
• Object Oriented Programming
  • Introduction to Python Classes
  • Defining Classes
  • Initializers
  • Instance Methods
  • Properties
  • Class Methods and Data
  • Static Methods
  • Private Methods and Inheritance
  • Module Aliases
• Working with Data
  • Reading files using Pandas library
  • Understanding function parameters
  • Writing files from Python
  • Reading various other file types in python
• Data Manipulation Using Pandas
  • Clean & Prepare Datasets
  • Data Transformations
  • Encoding
• Exploratory Data Analysis
  • Summarize Data
  • Statistical analysis of Data
  • Extensive Data Exploration for deeper insights
  • Missing value treatment
  • Quality Analysis
• Data Visualization with Matplotlib
  • Installing matplotlib
  • Importing matplotlib
  • Modules of matplolib: pyplot, image, animation
  • Charts using matplotlib: box plot, pie-charts, scatterplots, histogram, bar-chart
• Data Visualization with Seaborn
  • Installing Seaborn
  • Importing Seaborn
  • Applications of Seaborn
  • Visualizing statistical relationships
  • Seaborn figure styles
• Data Visualization with ggplot
  • Grammar of Graphics
  • Installing ggplot
  • Importing ggplot
  • Components of ggplot
  • Charts using ggplot: Bar charts, Scatterplots, Polar PlotsHands-on:: Write Python code using ggplot to visualize data and represent it graphically
• Analyze Visualize & Treat Missing Values
  • Checking missing values
  • Visualize data after analyzing
  • Methods to impute missing values
• Case Study
  • Real-Life Case Study