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Python Data Analysis and Visualization

Delve into Python data analysis and visualization using NumPy, SciPy, Pandas, and Seaborn. Gain insights into reading, cleaning, and visualizing data to generate actionable insights.

4.5

34 Lessons

2h 30min

Updated 5 months ago

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Learning Roadmap

34 Lessons1 Quiz5 Challenges

What is Analytics

Get familiar with transforming raw data into actionable insights using Python.

Introduction to Analytics

Analytics in Python

Python Basics for Analytics

Walk through Python basics, key data structures, control flow, Numpy, and Scipy usage.

Getting Started

Data Structures

Control Flow and Built-in Functions

Numpy an External Library

Scipy an External Library

Exercise: Using Numpy and Scipy

Solution Review: Using Numpy and Scipy

Reading Data

5 Lessons

Examine reading data from CSV, JSON, raw files, and using Pandas for dataset analysis.

Describing Data

4 Lessons

Grasp the fundamentals of data analysis and reshaping in Python using Pandas.

Cleaning Data

6 Lessons

Take a closer look at handling missing data, outliers, scaling, and encoding categorical data.

Visualizing Data

10 Lessons

Focus on Python data visualization using Matplotlib and Seaborn for comprehensive data analysis.

Fundamentals of Data Analysis

Mock Interview

Premium

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Developed by MAANG Engineers

ABOUT THIS COURSE

This course assumes no prior experience and can teach anyone how to use Python to analyze and visualize data. Stop using Excel and start harnessing the power of Python. Data analysis is one of the hottest careers of the 21st century. As an analyst, your goal is to peel back layers of data in order to answer questions of interest; that is the power of analytics. It allows you to take raw data and create meaningful, actionable insights. In this course, you’ll learn how to use Python, NumPy, SciPy, Pandas, and Seaborn to perform data analysis and visualization. You’ll explore the four crucial steps for any data analysis project: reading, describing, cleaning, and visualizing data. In each step, you will work with the most common and popular tools that data analysts use every day. By the end of the course, you will be able to confidently extract knowledge and answers from data.

ABOUT THE AUTHOR

Tyler Folkman

Head of AI at Branded Entertainment Network. Passionately pursuing great science, leadership and helping others along the way.

Learn more about Tyler

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