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Data Analysis with Python: Zero to Pandas

via Jovian

Overview

"Data Analysis with Python: Zero to Pandas" is a practical and beginner-friendly introduction to data analysis covering the basics of Python, Numpy, Pandas, Data Visualization, and Exploratory Data Analysis.

  • Watch hands-on coding-focused video tutorials
  • Practice coding with cloud Jupyter notebooks
  • Build an end-to-end real-world course project
  • Earn a verified certificate of accomplishment

The course is self-paced and there are no deadlines. There are no prerequisites for this course.

Syllabus

Lesson 1 - Introduction to Programming with Python
  • Course overview & curriculum walkthrough
  • First steps with Python and Jupyter notebooks
  • A quick tour of variables and data types
  • Branching with conditional statements and loops
Lesson 2 - Next Steps with Python
  • Branching with conditional statements and loops
  • Write reusable code with Functions
  • Working with the OS & Filesystem
  • Assignment and course forum walkthrough
Assignment 1 - Python Basics Practice
  • Solve word problems using variables & arithmetic operations
  • Manipulate data types using methods & operators
  • Use branching and iterations to translate ideas into code
  • Explore the documentation and get help from the community
Lesson 3 - Numerical Computing with Numpy
  • Going from Python lists to Numpy arrays
  • Working with multi-dimensional arrays
  • Array operations, slicing and broadcasting
  • Working with CSV data files
Assignment 2 - Numpy Array Operations
  • Explore the Numpy documentation website
  • Demonstrate usage 5 numpy array operations
  • Publish a Jupyter notebook with explanations
  • Share your work with the course community
Lesson 4 - Analyzing Tabular Data with Pandas
  • Reading and writing CSV data with Pandas
  • Querying, filtering and sorting data frames
  • Grouping and aggregation for data summarization
  • Merging and joining data from multiple sources
Assignment 3 - Pandas Practice
  • Create data frames from CSV files
  • Query and index operations on data frames
  • Group, merge and aggregate data frames
  • Fix missing and invalid values in data
Lesson 5 - Visualization with Matplotlib and Seaborn
  • Basic visualizations with Matplotlib
  • Advanced visualizations with Seaborn
  • Tips for customizing and styling charts
  • Plotting images and grids of charts
Course Project - Exploratory Data Analysis
  • Find a real-world dataset of your choice online
  • Use Numpy & Pandas to parse, clean & analyze data
  • Use Matplotlib & Seaborn to create visualizations
  • Ask and answer interesting questions about the data
Lesson 6 - Exploratory Data Analysis - A Case Study
  • Finding a good real-world dataset for EDA
  • Data loading, cleaning and preprocessing
  • Exploratory analysis and visualization
  • Answering questions and making inferences

Taught by

Aakash N S

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