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⇱ Data Science Fundamentals Part 2: Unit 1 | Coursera

Data Science Fundamentals Part 2: Unit 1

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Data Science Fundamentals Part 2: Unit 1

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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

8 hours to complete
Flexible schedule
Learn at your own pace
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.

Details to know

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Assessments

1 assignment

Taught in English

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•65,339 learners

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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.

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

This course is currently available only to learners who have paid or received financial aid, when available.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,