Data Analysis with Python Course - Numpy, Pandas, Data Visualization

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Learn the basics of Python, Numpy, Pandas, Data Visualization, and Exploratory Data Analysis in this course for beginners. This was originally presented as a live course.

By the end of the course, you will be able to build an end-to-end real-world course project and earn a verified certificate of accomplishment. There are no prerequisites for this course.

Learn more and register for a certificate of accomplishment here:

This full course video includes 6 lectures (all in this video):
• Introduction to Programming with Python
• Next Steps with Python
• Numerical Computing with Numpy
• Analyzing Tabular Data with Pandas
• Visualization with Matplotlib and Seaborn
• Exploratory Data Analysis - A Case Study

???? Code References
• First steps with Python:
• Variables and data types:
• Conditional statements and loops:
• Functions and scope:
• Working with OS & files:
• Numerical computing with Numpy:
• 100 Numpy exercises:
• Analyzing tabular data with Pandas:
• Matplotlib & Seaborn tutorial:
• Data visualization cheat sheet:
• EDA on StackOverflow Developer Survey:
• Opendatasets python package:
• EDA starter notebook:

⭐️ Course Contents ⭐️
0:00:00 Course Introduction

Lecture 1
0:01:42 Python Programming Fundamentals
0:02:40 Course Curriculum
0:05:24 Notebook - First Steps with Python and Jupyter
0:08:30 Performing Arithmetic Operations with Python
0:11:34 Solving Multi-step problems using variables
0:20:17 Combining conditions with Logical operators
0:22:22 Adding text using Markdown
0:23:50 Saving and Uploading to Jovian
0:26:38 Variables and Datatypes in Python
0:31:28 Built-in Data types in Python
1:07:19 Further Reading

Lecture 2
1:08:46 Branching Loops and Functions
1:09:02 Notebook - Branching using conditional statements and loops in Python
1:09:24 Branching with if, else, elif
1:15:25 Non Boolean conditions
1:19:00 Iteration with while loops
1:28:57 Iteration with for loops
1:36:27 Functions and scope in Python
1:36:53 Creating and using functions
1:42:24 Writing great functions in Python
1:45:38 Local variables and scope
2:08:19 Documentation functions using Docstrings
2:11:40 Exercise - Data Analysis for Vacation Planning

Lecture 3
2:17:17 Numercial Computing with Numpy
2:18:00 Notebook - Numerical Computing with Numpy
2:26:09 From Python Lists to Numpy Arrays
2:29:09 Operating on Numpy Arrays
2:34:33 Multidimensional Numpy Arrays
3:03:41 Array Indexing and Slicing
3:17:49 Exercises and Further Reading
3:20:50 Assignment 2 - Numpy Array Operations
3:29:16 100 Numpy Exercises
3:31:25 Reading from and Writing to Files using Python

Lecture 4
4:02:59 Analysing Tabular Data with Pandas
4:03:58 Notebook - Analyzing Tabular Data with Pandas
4:16:33 Retrieving Data from a Data Frame
4:32:00 Analyzing Data from Data Frames
4:36:27 Querying and Sorting Rows
5:01:45 Grouping and Aggregation
5:11:26 Merging Data from Multiple Sources
5:26:00 Basic Plotting with Pandas
5:38:27 Assignment 3 - Pandas Practice

Lecture 5
5:52:48 Visualization with Matplotlib and Seaborn
5:54:04 Notebook - Data Visualization with Matplotlib and Seaborn
6:06:43 Line Charts
6:11:27 Improving Default Styles with Seaborn
6:16:51 Scatter Plots
6:28:14 Histogram
6:38:47 Bar Chart
6:50:00 Heatmap
6:57:08 Displaying Images with Matplotlib
7:03:37 Plotting multiple charts in a grid
7:15:42 References and further reading
7:20:17 Course Project - Exploratory Data Analysis

Lecture 6
7:49:56 Exploratory Data Analysis - A Case Study
7:50:55 Notebook - Exploratory Data Analysis - A case Study
8:04:36 Data Preparation and Cleaning
8:19:37 Exploratory Analysis and Visualization
8:54:02 Asking and Answering Questions
9:22:57 Inferences and Conclusions
9:25:00 References and Future Work
9:29:41 Setting up and running Locally
9:34:21 Project Guidelines
9:45:00 Course Recap
9:48:01 What to do next?
9:49:10 Certificate of Accomplishment
9:50:11 What to do after this course?
9:52:16 Jovian Platform

✏️ This course is taught by Aakash N S, co-founder, and CEO of Jovian.

Jovian's YouTube channel:
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