Data Analytics With Python
This course provides a comprehensive introduction to data analysis using Python, covering essential libraries like Pandas, Matplotlib, Seaborn, and Plotly. You will learn how to manipulate and visualize data through interactive and static charts, conduct exploratory data analysis, and apply statistical techniques to uncover trends and patterns. By the end of the course, you’ll be able to create meaningful visualizations, work with real-world datasets, and make data-driven decisions, preparing you for a career in data analytics.
This course is a part of Data Analytics Program.
$1400
6-Module Program
Perfect for beginners and professionals alike!
Live, Instructor-Led
Learn from experienced instructors in real-time.
40-Hour Course
6 Hours/Week, Weekend classes
Online Classes
Join the course from anywhere. Save on travel-time.
Course Modules
Module 01 - Introduction
- What Is Python?
- Why Python?
- Who uses Python?
- History of Python
- Features of Python
- Where Python can be used?
- Installing Python from the command line
- IDLE
- Running Python scripts on Windows/Unix/Linux
- Basic Syntax, Comments, Keywords
- Data types, Naming Conventions
- Print() Function, Type() and Id() Functions
- Input() and raw_input() Functions
- Type Conversion functions
- Del Keyword
- What is a string?, String Indexing, String Slicing
- Basic Operations
- Working with String Functions
- Arithmetic Operators, Relational Operators, Logical Operators, Assignment Operators
- Membership Operators, Identity Operators
- Precedence of Operators
Module 02 - Data Gathering
- Introduction to RDBMS
- Installation of Oracle Database Access
- Creating Oracle Database Instances
- Connecting to Multiple Databases
- Establishing Connection with Oracle
- Executing SQL Queries
- How to Parse XML
- How to Create XML Node
- How to Parse JSON
Module 03 - Data Exploration
- What is a file?
- Opening a file
- Reading data from a file
- Writing data to a file
- Closing a file
- Working with the methods of file objects
- Replacing the content of a file
- Working with directories
- Working with CSV files
- Assignments
- Introduction to OOP’s Programming
- Features of OOPS
- Classes and Objects
- Encapsulation
- Abstraction
- Inheritance
- Polymorphism
Module 04 - Data Transformation/Data Cleaning
- if statement (One-way decisions)
- if-else statement (Two-way decisions)
- if-else if-else statement (Multi-way decisions)
- Nested if-else
- Single line if-else statement
- Break statement
- Continue statement
- Pass statement
Module 05 - Data Analysis
- Overview of Plotly and Its Uses in Data Analytics
- Installing Plotly and Cufflinks
- Creating Basic Plots (Line, Scatter, Bar)
- Customizing Layouts and Colors
- Using Plotly Express for Quick Visualization
- Plotting with Pandas DataFrames
- Working with Categorical and Numeric Data
- 3D Plots and Surface Plots
- Adding Annotations, Tooltips, and Legends
- Interactive Elements (Sliders, Dropdowns, Buttons)
- Advanced Customizations with Layouts and Subplots
- Overview of Cufflinks and Integration with Plotly
Module 06 - Data Visualization
- Bar Chart
- Pie Chart
- Area Chart
- Scatter Plot Chart
- Play with Chart Properties
- Export the Chart
- plt.subplots() Notation
- Legend Alignment of Chart
- Creating Basic Plots (Line, Bar, Histogram)
- Understanding Axes and Figure Layouts
- Categorical Data
- Univariate Analysis
- Bivariate Line Plots
- Multivariate Analysis with Pair and Joint Plots
- Heatmaps and Correlation Matrices
- Violin and Box Plots for Distribution
- Swarm and Strip Plots for Categorical Data
- Customizing Plot Elements
- Adding Annotations and Titles
- Visualizing Trends and Patterns in Data
Get Expert Career Guidance
Not sure which course is right for you? Book a free consultation with one of our career strategists! Our experts are here to help you explore your options, answer your questions, and guide you toward the best path to achieve your career goals.
Course Outcomes
- Develop the ability to clean, transform, and analyze datasets using Pandas and NumPy, ensuring your data is ready for meaningful analysis.
- Learn to create interactive and dynamic visualizations with Plotly, enabling you to build engaging and user-friendly charts for better data communication.
- Gain expertise in exploratory data analysis (EDA) and apply statistical methods for univariate, bivariate, and multivariate analysis to identify patterns and insights in your data.
- Master the creation of various types of charts—bar, scatter, heatmaps, 3D plots, and more—using Matplotlib and Seaborn to effectively present data findings.
- Work with practical, industry-relevant datasets to apply your analytical and visualization skills, preparing you for real-world data analytics challenges and decision-making.
Join Now