Python Data Analytics

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Python Data Analytics

Name: Python Data Analytics
Rating: 4.330578512396694 (242 reviews)

This course is part of multiple programs.

Instructor: Victor Geislinger

29,263 already enrolled

Included with

5 modules

Gain insight into a topic and learn the fundamentals.

242 reviews

Beginner level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

86%

Most learners liked this course

5 modules

Gain insight into a topic and learn the fundamentals.

242 reviews

Beginner level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

86%

Most learners liked this course

What you'll learn

Sort, query, and structure data in Pandas, the Python library
Describe how to model and interpret data using Python
Create basic data visualizations with Python libraries

Skills you'll gain

Category: Data Manipulation
Category: Exploratory Data Analysis
Category: Scripting
Category: Data Visualization
Category: Data Visualization Software
Category: Regression Analysis
Category: Data Cleansing
Category: Matplotlib
Category: Programming Principles
Category: Data Analysis

Tools you'll learn

Category: Python Programming
Category: Pandas (Python Package)
Category: Jupyter

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

15 assignments

Taught in English

Build your subject-matter expertise

This course is available as part of

When you enroll in this course, you'll also be asked to select a specific program.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate from Meta

There are 5 modules in this course

This course introduces the use of the Python programming language to manipulate datasets as an alternative to spreadsheets. You will follow the OSEMN framework of data analysis to pull, clean, manipulate, and interpret data all while learning foundational programming principles and basic Python functions. You will be introduced to the Python library, Pandas, and how you can use it to obtain, scrub, explore, and visualize data.

By the end of this course you will be able to: • Use Python to construct loops and basic data structures • Sort, query, and structure data in Pandas, the Python library • Create data visualizations with Python libraries • Model and interpret data using Python This course is designed for people who want to learn the basics of using Python to sort and structure data for data analysis. You don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate.

In this module you will be introduced to Python and how it can be used in data analytics. You will also learn how to use the Jupyter Notebook programming environment.

What's included

8 videos4 readings2 assignments1 programming assignment

8 videos Total 31 minutes

Introduction to the Program 6 minutes
Instructor Introduction Video 2 minutes
Approaching Data Analysis with the OSEMN Framework 4 minutes
Why Python for Data Analysis 3 minutes
Jupyter Notebook: Where We Write Our Code 1 minute
Basics of Using Jupyter Notebook 7 minutes
Using Jupyter Notebook on Coursera 3 minutes
Reviewing Example of a Typical Notebook on Coursera Activity 5 minutes

4 readings Total 35 minutes

Course Syllabus 5 minutes
Join the Meta Marketing Analytics Community or the Meta Data Analyst Community! 10 minutes
How to be Successful in this Program 10 minutes
Tips for Using Jupyter Notebook 10 minutes

2 assignments Total 25 minutes

Review Your Community Knowledge 10 minutes
Practice Quiz: Python for Data Analysis 15 minutes

1 programming assignment Total 30 minutes

Activity: Example of a Typical Notebook on Coursera 30 minutes

In this module, you will learn basic programming principles such as variables and variable types using Python. You’ll also delve into basic Python statements such as Booleans and conditional statements.

What's included

12 videos1 reading3 assignments5 programming assignments

12 videos Total 82 minutes

What Does a Variable Mean in Python? 10 minutes
Variable Types 5 minutes
Working with Types in Python 7 minutes
Reviewing Variables in Python Activity 2 minutes
Lists & Tuples 12 minutes
Reviewing Lists & Tuples Activity 7 minutes
Dictionaries 7 minutes
Reviewing Dictionaries Activity 4 minutes
Booleans in Python 7 minutes
Reviewing Using Booleans Activity 6 minutes
Conditional Statements 11 minutes
Reviewing Using Conditionals Activity 5 minutes

1 reading Total 10 minutes

Other Python Data Structures 10 minutes

3 assignments Total 80 minutes

Knowledge Check on Variable Types 15 minutes
Knowledge Check on Conditionals 15 minutes
Graded Quiz: Basic Python Concepts 50 minutes

5 programming assignments Total 150 minutes

Activity: Variables in Python 30 minutes
Activity: Using Lists & Tuples 30 minutes
Activity: Using Dictionaries 30 minutes
Activity: Using Booleans 30 minutes
Activity: Using Conditionals 30 minutes

This week is focused on using a Python library called Pandas. You will learn how to use Pandas to load, select, and clean data.

What's included

12 videos1 reading3 assignments4 programming assignments

12 videos Total 87 minutes

Introduction to Libraries 8 minutes
What is Pandas? 8 minutes
Working with Pandas Series & DataFrames 9 minutes
Reviewing Pandas Activity 6 minutes
Subsets with Pandas 9 minutes
Reviewing Selective Subsets Activity 6 minutes
What is Scrubbing? 2 minutes
Removing Data 10 minutes
Reviewing Removing Data Activity 7 minutes
Modifying Values 7 minutes
Replacing Values 5 minutes
Reviewing Replacing Values Activity 10 minutes

1 reading Total 10 minutes

Python Syntax and Dot Notation Reference 10 minutes

3 assignments Total 80 minutes

Knowledge Check on Libraries 15 minutes
Knowledge Check on Pandas 15 minutes
Graded Quiz: Obtaining and Scrubbing Data with Pandas 50 minutes

4 programming assignments Total 120 minutes

Activity: Using Pandas 30 minutes
Activity: Selective Subsets 30 minutes
Activity: Removing Data 30 minutes
Activity: Modifying and Replacing Values 30 minutes

This week you will further explore and analyze datasets with Python. You will learn how to calculate basic statistics and create data visualizations with Pandas and Matplotlib, another Python library.

What's included

17 videos4 assignments4 programming assignments

17 videos Total 101 minutes

Why Exploration? 2 minutes
Exploring Relates to Scrubbing 2 minutes
Exploration: Basic Statistics 11 minutes
Exploration: Filtering Data 16 minutes
Reviewing Basic Exploration Activity 6 minutes
A Picture is Worth a Thousand Words 3 minutes
Introduction to the Purpose of Visualizations 1 minute
Types of Exploratory Visualizations: Distributions 3 minutes
Types of Exploratory Visualizations: Category 2 minutes
Types of Exploratory Visualizations: Relationship 3 minutes
Using Pandas and Matplotlib to Create Visualizations 11 minutes
Reviewing Creating Visualizations Activity 8 minutes
Understanding Visualizations for Exploration 8 minutes
Reviewing Exploring with Visualization Activity 6 minutes
Where Aggregations Help Us Understand Data 2 minutes
Working with Groups in Pandas 12 minutes
Reviewing Aggregations Activity 5 minutes

4 assignments Total 95 minutes

Knowledge Check on Exploration 15 minutes
Knowledge Check on Basic Statistics 15 minutes
Knowledge Check on Exploratory Visualizations 15 minutes
Graded Quiz: Exploring Data with Python 50 minutes

4 programming assignments Total 130 minutes

Activity: Basic Exploration 30 minutes
Activity: Creating Visualizations 40 minutes
Activity: Exploring With Visualizations 30 minutes
Activity: Aggregations 30 minutes

This week you will focus on modeling data with Python and interpreting the model results. You complete a data analytics challenge that applies the knowledge of Python and the application of the OSEMN framework you have gained throughout the course.

What's included

9 videos3 assignments1 programming assignment1 discussion prompt

9 videos Total 76 minutes

Modeling & Interpreting Data 2 minutes
Overview of Modeling 3 minutes
Modeling with Python 14 minutes
Overview of Interpreting 5 minutes
Interpreting Model Results 8 minutes
Exploratory vs. Explanatory Visualizations 4 minutes
Creating Explanatory Visualizations 12 minutes
OSEMN: Tying It All Together 12 minutes
Reviewing Full OSEMN Activity 18 minutes

3 assignments Total 80 minutes

Knowledge Check on Modeling & Interpreting 15 minutes
Knowledge Check on Interpreting 15 minutes
Graded Quiz: Modeling and Interpreting Data with Python 50 minutes

1 programming assignment Total 90 minutes

Activity: Full OSEMN 90 minutes

1 discussion prompt Total 10 minutes

Share Your Thoughts! 10 minutes

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Instructor

Instructor ratings

(62 ratings)

Victor Geislinger

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Learner reviews

5 stars
62.39%
4 stars
21.07%
3 stars
8.26%
2 stars
3.71%
1 star
4.54%

Showing 3 of 242

Reviewed on Nov 21, 2024

very interesting and easy for first time learner , with real world examples

Reviewed on Jul 14, 2024

this course is really helpful for me to study Data Analysis and good for my career

Reviewed on Oct 25, 2023

I liked the content and lessons learned in using Python. However, platform-wise, some assignments were not graded by the grader.

Frequently asked questions

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When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.