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

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Overview

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Hands-on video course teaching data analysis in Python from the ground up. Starts with Python programming fundamentals in Jupyter notebooks — arithmetic, variables and data types, conditional branching, loops, functions, scope and docstrings — then moves to numerical computing with Numpy arrays, reading and writing files, and analyzing tabular data with Pandas (retrieving, querying, sorting, grouping, aggregating and merging data frames). A visualization section covers line charts, scatter plots, histograms, bar charts, heatmaps and image display with Matplotlib and Seaborn. The course closes with an exploratory data analysis case study and a guided student project covering data cleaning, analysis, visualization and conclusions. Suited to learners new to Python and data analysis.

Syllabus

Course Introduction.
Python Programming Fundamentals.
Course Curriculum.
Notebook - First Steps with Python and Jupyter.
Performing Arithmetic Operations with Python.
Solving Multi-step problems using variables.
Combining conditions with Logical operators.
Adding text using Markdown.
Saving and Uploading to Jovian.
Variables and Datatypes in Python.
Built-in Data types in Python.
Further Reading.
Branching Loops and Functions.
Notebook - Branching using conditional statements and loops in Python.
Branching with if, else, elif.
Non Boolean conditions.
Iteration with while loops.
Iteration with for loops.
Functions and scope in Python.
Creating and using functions.
Writing great functions in Python.
Local variables and scope.
Documentation functions using Docstrings.
Exercise - Data Analysis for Vacation Planning.
Numercial Computing with Numpy.
Notebook - Numerical Computing with Numpy.
From Python Lists to Numpy Arrays.
Operating on Numpy Arrays.
Multidimensional Numpy Arrays.
Array Indexing and Slicing.
Exercises and Further Reading.
Assignment 2 - Numpy Array Operations.
100 Numpy Exercises.
Reading from and Writing to Files using Python.
Analysing Tabular Data with Pandas.
Notebook - Analyzing Tabular Data with Pandas.
Retrieving Data from a Data Frame.
Analyzing Data from Data Frames.
Querying and Sorting Rows.
Grouping and Aggregation.
Merging Data from Multiple Sources.
Basic Plotting with Pandas.
Assignment 3 - Pandas Practice.
Visualization with Matplotlib and Seaborn.
Notebook - Data Visualization with Matplotlib and Seaborn.
Line Charts.
Improving Default Styles with Seaborn.
Scatter Plots.
Histogram.
Bar Chart.
Heatmap.
Displaying Images with Matplotlib.
Plotting multiple charts in a grid.
References and further reading.
Course Project - Exploratory Data Analysis.
Exploratory Data Analysis - A Case Study.
Notebook - Exploratory Data Analysis - A case Study.
Data Preparation and Cleaning.
Exploratory Analysis and Visualization.
Asking and Answering Questions.
Inferences and Conclusions.
References and Future Work.
Setting up and running Locally.
Project Guidelines.
Course Recap.
What to do next?.
Certificate of Accomplishment.
What to do after this course?.
Jovian Platform.

Taught by

freeCodeCamp.org

Reviews

5.0 rating, based on 4 Class Central reviews

Start your review of Data Analysis with Python Course - Numpy, Pandas, Data Visualization

  • Kaviya Bharathi K
    Thank you so much for your valuable section and course. It's really amazing and a great opportunity to learn data analysis with python
  • Profile image for Elhadri Aymane
    Elhadri Aymane
    “A very clear and practical course. It helped me understand the fundamentals of data analysis using Python, especially with NumPy, Pandas, and data visualization tools. Highly recommended for beginners and intermediate learners.”
  • Profile image for Degia Parlopa Pasaribu
    Degia Parlopa Pasaribu
    This is a very good lesson. Each session is easy to understand, and the practical steps are also quite easy. Thank you for this lesson.
  • Betul ALBAYRAK
    Hi Aakash, your explanations, practical examples, and hands-on approach made complex concepts like Numpy arrays, Pandas data manipulation, and data visualization with libraries like Matplotlib and Seaborn not only understandable but also enjoyable to learn. The way you broke down each topic and connected it to real-world applications was incredibly inspiring and has already had a significant impact on my ability to analyze and interpret data effectively. Thanks for everything.
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