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Understanding Data Science

BasicSkill Level
4.8+
6,545 reviews
Updated 05/2026
An introduction to data science with no coding involved.
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TheoryData Literacy2 hr15 videos47 Exercises2,800 XP840K+Statement of Accomplishment

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Course Description

What is data science, why is it so popular, and why did the Harvard Business Review hail it as the “sexiest job of the 21st century”? In this non-technical course, you’ll be introduced to everything you were ever too afraid to ask about this fast-growing and exciting field, without needing to write a single line of code. Through hands-on exercises, you’ll learn about the different data scientist roles, foundational topics like A/B testing, time series analysis, and machine learning, and how data scientists extract knowledge and insights from real-world data. So don’t be put off by the buzzwords. Start learning, gain skills in this hugely in-demand field, and discover why data science is for everyone!The videos contain live transcripts that can be found by clicking "Show transcript" at the bottom left of the videos.The course glossary can be found on the right in the resources section.To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.

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What you'll learn

  • Define core data science concepts, workflows, roles, and commonly used tools.
  • Identify data types, sources, and storage methods used in data-driven organizations.
  • Evaluate real-world applications of data science across industries and use cases.
  • Assess the purpose of data pipelines, exploration, and visualization in data workflows.
  • Recognize how modeling techniques like A/B testing and machine learning inform decisions.

Prerequisites

There are no prerequisites for this course
1

Introduction to Data Science

We'll start the course by defining what data science is. We'll cover the data science workflow and how data science is applied to real-world problems. We'll finish the chapter by learning about different roles within the data science field.
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2

Data Collection and Storage

Now that we understand the data science workflow, we'll dive deeper into the first step: data collection and storage. We'll learn about the different data sources you can draw from, what that data looks like, how to store the data once it's collected, and how a data pipeline can automate the process.
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3

Preparation, Exploration, and Visualization

Data preparation is fundamental: data scientists spend 80% of their time cleaning and manipulating data, and only 20% of their time actually analyzing it. This chapter will show you how to diagnose problems in your data, deal with missing values and outliers. You will then learn about visualization, another essential tool to both explore your data and convey your findings.
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4

Experimentation and Prediction

In this final chapter, we'll discuss experimentation and prediction! Beginning with experiments, we'll cover A/B testing, and move on to time series forecasting where we'll learn about predicting future events. Finally, we'll end with machine learning, looking at supervised learning, and clustering.
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Understanding Data Science
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FAQs

Is this course suitable for beginners?

Absolutely! This course is designed for those who are new to data science, and no prior coding experience is necessary.

Who will benefit from this course?

Knowing the fundamentals of data science is essential for a variety of career path roles, including but not limited to data analysts, data engineers, data scientists, business analysts, and research scientists.

Will I receive a certificate at the end of the course?

Yes! Upon completion of the course, you will be eligible to receive a certificate of achievement from DataCamp.

What will I learn in this course?

You will learn about the different roles within data science, foundational topics such as A/B testing and time series analysis, machine learning, how to extract knowledge and insights from real-world data, and more.

Will I need to write code in this course?

No, this course is non-technical and does not require any coding.

How long is the course?

The course takes approximately 2 hours to complete.

Can I earn CPE credits for this course?

You can earn 2.8 CPE credits for this Information Technology course through the QAS Self Study Delivery Method. DataCamp is currently applying to be registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continued professional education on the National Registry of CPE. Its NASBA-accredited CPE courses for data and finance professionals offer an excellent way to stay up-to-date with industry standards. While learners can currently access CPE credits in five courses, DataCamp is actively working to extend its accreditation across its extensive course library.

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