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Ben Bolstad has completed

Understanding Data Science

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2 hours
3,000 XP
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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!
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  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.

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    What is data science?
    50 xp
    Why data science?
    50 xp
    Going with the workflow
    50 xp
    Applications of data science
    50 xp
    Investment research
    50 xp
    Assigning data science project
    100 xp
    Data science roles and tools
    50 xp
    Editing a job post
    50 xp
    Matching skills to jobs
    100 xp
    Classifying data tasks
    100 xp
  2. 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. 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. 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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For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp platform, including all the features.
Hadrien Lacroix HeadshotHadrien Lacroix

Curriculum Manager 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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Sara Billen HeadshotSara Billen

Data Scientist 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 at DataCamp.
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Lis Sulmont HeadshotLis Sulmont

Content Program Manager at Duolingo

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