Python for Data Science Course

Learning Objectives:

In this module you will learn to convert messy text into something useful. You will also learn to use regular expressions to utilize search methods and patterns.

Topics Covered:

• String Operations
• Uses of Regular Expressions
• Methods of Regular Expressions
• Commonly used Operators

Hands-on:

Write Python code to use regular expressions and implement search methods.

Learning Objectives:

Learn about the Data structures that are used in Python

Topics Covered:

• Arrays
• Lists
• Tuples
• Dictionaries
• Sets

Hands-on:

Write Python Code to understand and implement Python Data Structures.

Learning Objectives:

Learn all about loops and control statements in Python.

Topics Covered:

• For Loop
• While Loop
• Break Statement
• Next Statements
• Repeat Statement
• if, if…else Statements
• Switch Statement

Hands-on:

Write Python Code to implement loop and control structures in R.

Learning Objectives:

Write user-defined functions in Python. Learn about Lambda function and also the object-oriented way of writing classes & objects

Topics Covered:

• Writing your own functions (UDF)
• Calling Python Functions                 
• Functions with Arguments
• Calling Python Functions by passing Arguments
• Lambda Functions

Hands-on:

Write Python Code to create your own custom functions without or with arguments. Know how to call them by passing arguments wherever required.

Learning Objectives: 

Gain knowledge on OOPs to code easily and efficiently. Learn to construct classes and define objects.

Topics Covered:

• Introduction to Python Classes
• Defining Classes
• Initializers
• Instance Methods
• Properties
• Class Methods and Data
• Static Methods
• Private Methods and Inheritance
• Module Aliases and Regular Expressions

Hands-on:

Write Python code to construct a class and define objects.

Learning Objectives: 

Learn how to import datasets into Python. Also, learn how to write output into files from Python.

Topics Covered:

• Reading files using Pandas library
• Understanding function parameters
• Writing files from Python
• Reading files using Pandas library
• Reading various other file types in python

Hands-on:

Write Python Code to read and write data from/to Python.

Learning Objectives:

Manipulate & learn to transform raw data using Pandas library. Learn to generate insights from your data.

Topics Covered:

• Clean & Prepare Datasets
• Data Transformations
• Encoding

Hands-on:

Write Python code to manipulate data frames and churn insights using various python libraries.

Learning Objectives:

Learn to summarize dataset through descriptive statistics. Use a variety of measurements to better understand your data. Learn to treat missing values. Also, learn how to discover patterns in your data.

Topics Covered:

• Summarize Data
• Statistical analysis of Data
• Extensive Data Exploration for deeper insights
• Missing value treatment
• Quality Analysis

Hands-on:

Write Python code to summarize dataset through descriptive statistics and treat the missing values after analysis.

Learning Objectives:

Master in a commonly used Python graphics module, Matplotlib. Learn to create charts such as histogram, pie-chart, box plots and so on using matplotlib.

Topics Covered:

• Installing matplotlib
• Importing matplotlib
• Modules of matplolib: pyplot, image, animation
• Charts using matplotlib: box plot, pie-charts, scatterplots, histogram, bar-chart

Hands-on:

Write Python code using Python library: matplotlib to visualize data and represent it graphically.

Learning Objectives:

Learn how to use one of Python’s most convenient libraries, Seaborn, for data visualization. Build charts such as violin plots, scatterplot, heat-maps and so on using Seaborn.

Topics Covered:

• Installing Seaborn
• Importing Seaborn
• Applications of Seaborn
• Visualizing statistical relationships
• Seaborn figure styles

Hands-on:

Write Python code using Python library: Seaborn to visualize data and represent it graphically.

Learning Objectives:

Jump into an advanced visualization technique, that is ggplot in Python. Understand Grammar of Graphics and apply it to create charts.

Topics Covered:

• 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

Learning Objectives:

Learn to implement data analyzing methods, visualize the same and understand missing value treatment methods.

Topics Covered:

• Checking missing values
• Visualize data after analyzing
• Methods to impute missing values

Hands-on:

Write Python code to analyze, visualize and treat missing values.

Learning Objectives:

Hands-on session on a real-life case study.

Topics Covered:

• Real-Life Case Study

Hands-on:

Case Study: House Attributes and Sales Price data. Use this data to explore more. Deep Dive into advanced explorations. Analyze and Visualize missing data, treat missing data to missing value imputation. Visualize data with various libraries. Gain deep insights on your data.