Interactive Course

Data Science for Everyone

An introduction to data science with no coding involved.

  • 4 hours
  • 15 Videos
  • 48 Exercises
  • 20,396 Participants
  • 3,100 XP

Loved by learners at thousands of top companies:

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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!

  1. 1

    Introduction to Data Science

    Free

    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.

  2. 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.

  3. 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.

  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.

  1. 1

    Introduction to Data Science

    Free

    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.

  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.

  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.

  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.

What do other learners have to say?

Devon

“I've used other sites, but DataCamp's been the one that I've stuck with.”

Devon Edwards Joseph

Lloyd's Banking Group

Louis

“DataCamp is the top resource I recommend for learning data science.”

Louis Maiden

Harvard Business School

Ronbowers

“DataCamp is by far my favorite website to learn from.”

Ronald Bowers

Decision Science Analytics @ USAA

Sara Billen
Sara Billen

Curriculum Manager at DataCamp

Sara is a graduate of a master's degree in Business Engineering and Marketing Analysis. Prior to working at DataCamp she worked as a Data Science consultant for a Belgian IT company. Sara is passionate about education, data science, and business and loves that she is able to combine all of these disciplines in her job as curriculum manager at DataCamp.

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Lis Sulmont
Lis Sulmont

Curriculum Manager at DataCamp

Lis is a Curriculum Manager at DataCamp. She holds a Master's degree in Computer Science from McGill University with a focus in computer science education research and applied machine learning. Lis is passionate about teaching all things related to data and improving the accessibility of these topics.

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Hadrien Lacroix
Hadrien Lacroix

Content Developer at DataCamp

Hadrien has collaborated on 30+ courses ranging from machine learning to database administration through data engineering. He's currently enrolled in a Masters of Analytics at Georgia Tech.

Hadrien started using DataCamp when the platform only had 27 courses. He then joined the Support team and helped students before becoming a Content Developer himself.

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