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

freeCodeCamp via YouTube

Overview

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.

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

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