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Data Analysis with Python

In today's data-driven world, the ability to extract valuable insights from vast amounts of information is a highly sought-after skill. "Mastering Data Analysis with Python" is an immersive and comprehensive course designed to equip learners with the essential tools, techniques, and knowledge to excel in the field of data analysis using Python.


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Data Analysis

Course Overview

The course starts with a solid foundation in Python programming, ensuring that even those with no prior coding experience can participate and thrive. As you progress, you will delve into the fundamental concepts of data analysis, learning how to effectively handle, clean, and preprocess data to ensure its quality and reliability.

Python Programming Fundamentals

This introduction to Python will kickstart your learning of Python for data analytics, as well as programming in general. This beginner-friendly Python course will take you from zero to programming in Python in a matter of hours.

Upon its completion, you’ll be able to write your own Python scripts and perform basic hands-on data analysis using our Jupyter-based lab environment. If you want to learn Python from scratch, this course is for you.

Course Outline

  • Your first program
  • Types
  • Expressions and Variables
  • String Operations
  • Lists and Tuples
  • Sets
  • Dictionaries
  • Conditions Statements
  • Loops
  • File Handling
  • Functions and Lambdas
  • Objects and Classes


Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!

Data Analysis with Python is delivered through lectures, hands-on labs, and assignments.

Course Outline

  • What is data analysis?
  • Overview of Python and its data analysis libraries (NumPy, pandas, Matplotlib, Seaborn)
  • Setting up your Python environment
  • Importing data from various sources (CSV, Excel, API, Web Scraping)
  • Exploring and understanding the dataset
  • Handling missing data: imputation techniques
  • Dealing with outliers and anomalies
  • Data transformation: normalization, standardization
  • Data integration and manipulation using pandas

  • Descriptive statistics: mean, median, mode, variance, etc.
  • Histograms, box plots, scatter plots
  • Correlation analysis and heatmaps
  • Data visualization using Matplotlib and Seaborn
  • Advanced data visualization techniques: bar plots, line plots, pie charts, etc.
  • Effective data storytelling and communication
  • Applying data analysis concepts to real datasets
  • Solving data analysis challenges and problems
  • Creating a portfolio of data analysis projects

Final Project

The final project consists of problems, solutions, source code, and a project report. More details are provided in the final project guideline.

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