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Data Science Fundamentals Part 2: Unit 1

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Data Science Fundamentals Part 2: Unit 1

This course is part of Data Science Fundamentals, Part 2 Specialization

Instructors: Pearson

Included with

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1 module

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

1 module

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Gain a foundational understanding of Exploratory Data Analysis (EDA) and its historical context.
Develop practical skills in Python data visualization using matplotlib and seaborn.
Learn to identify and interpret relationships and correlations within datasets using advanced charting techniques.
Recognize and avoid common pitfalls in data analysis, including mixed effects and Simpson’s Paradox.

Skills you'll gain

Tools you'll learn

Seaborn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

1 assignment

Taught in English

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Build your subject-matter expertise

This course is part of the Data Science Fundamentals, Part 2 Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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

There is 1 module in this course

This course teaches how to ask the right questions and extract meaningful insights from complex datasets. Through hands-on lessons, you’ll master industry-standard Python libraries such as matplotlib and seaborn, enabling you to create compelling visualizations like histograms, boxplots, and scatter plots. You'll learn to uncover patterns, relationships, and correlations within your data, and develop a critical eye for interpreting results. You'll also learn about the pitfalls of data interpretation, including the impact of mixed effects and the nuances of Simpson’s Paradox, ensuring you can navigate and communicate findings with confidence. Whether you’re a beginner or looking to deepen your analytical toolkit, this course will empower you to transform raw data into actionable knowledge.

This module introduces Exploratory Data Analysis (EDA), emphasizing its historical context and importance in asking the right questions of data. Learners will use Python’s matplotlib and seaborn libraries to visualize and analyze data, starting with single-variable plots like histograms and boxplots, then advancing to multi-dimensional visualizations such as scatter plots. The module also covers identifying relationships and correlations between variables, and concludes with a discussion of statistical pitfalls like Simpson’s Paradox, highlighting the need for careful interpretation of data.

What's included

20 videos1 assignment

20 videos Total 423 minutes

Specialization Introduction 7 minutes
Topics 1 minute
Introduction to the pandas Library, Part 1 50 minutes
Introduction to the pandas Library, Part 2 23 minutes
Introduction to the pandas Library, Part 3 23 minutes
Data Manipulation with Pandas 32 minutes
Grouping and Summarizing Data with Pandas 16 minutes
Exploratory versus Explanatory Visualization, Part 1 8 minutes
Exploratory versus Explanatory Visualization, Part 2 21 minutes
Visualizing One Dimension: Histograms and Boxplots 30 minutes
Visualizing Two Dimensions: Bars, Lines, and Scatterplots 9 minutes
Multivariate Visualization: Facets, Small Multiples, and Dashboards, Part 1 14 minutes
Multivariate Visualization: Facets, Small Multiples, and Dashboards, Part 2 11 minutes
Visualizing One Dimension: Histogram and KDE, Part 1 30 minutes
Visualizing One Dimension: Histogram and KDE, Part 2 27 minutes
Visualizing One Dimension: Comparing Distributions, Part 1 29 minutes
Visualizing One Dimension: Comparing Distributions, Part 2 28 minutes
Visualizing One Dimension: Comparing Distributions, Part 3 7 minutes
Visualizing Two Dimensions: Line Charts and Scatter Plots 41 minutes
Mixed Effects and Simpson's Paradox 16 minutes

1 assignment Total 30 minutes

Exploring Data--Analysis and Visualization Quiz 30 minutes

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Instructors

Pearson

268 Courses 48,983 learners

Offered by

Pearson

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Frequently asked questions

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

Data Science Fundamentals Part 2: Unit 1

Data Science Fundamentals Part 2: Unit 1

What you'll learn

Skills you'll gain

Tools you'll learn

Details to know

See how employees at top companies are mastering in-demand skills

Build your subject-matter expertise

There is 1 module in this course