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Data Science for Business

4.3+
17 reviews
Beginner

Learn about data science for managers and businesses and how to use data to strengthen your organization.

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2 Hours14 Videos51 Exercises83,092 Learners

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Course Description

Learn to Use Data Science for Business

What is data science, and how can you use it to strengthen your organization? This course will teach you about the skills you need on your data team and how you can structure that team to meet your organization's needs.

This course will also provide you with an understanding of data sources your company can use and how to store, analyze, and visualize that data.

Understand the Data Science Workflow

You’ll start with an introduction to data science for businesses, looking at the data science workflow and how to apply it to real-world problems. You’ll also explore how data collection works, looking at how you can source and store data.

Learn to Analyze and Visualize Your Data

You'll also discover ways to analyze and visualize your data through dashboards and A/B tests. To wrap up the course, we'll discuss exciting topics in machine learning, including clustering, time series prediction, natural language processing (NLP), deep learning, and explainable AI.

Along the way, you'll learn about a variety of real-world applications of data science and gain a better understanding of these concepts through practical exercises.

This is an ideal introduction to data science for managers, giving you the chance to learn about this powerful business tool.
  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 business problems. We'll finish the chapter by learning about ways to structure your data team to meet your organization's needs.

    Play Chapter Now
    What is Data Science?
    50 xp
    Customer Segmentation Workflow
    100 xp
    Building a customer service chatbot
    100 xp
    Improving OKRs
    50 xp
    Applications of Data Science
    50 xp
    Assigning data science projects
    100 xp
    Investment research
    50 xp
    Building a data science team
    50 xp
    Interpreting a team sprint
    50 xp
    Editing a job posting
    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. We'll learn about the different data sources your company can draw from, and how to store that data once it's collected.

    Play Chapter Now
    Data sources and risks
    50 xp
    Classifying data for security
    100 xp
    Creating web data events
    50 xp
    Protecting PII
    50 xp
    Solicited data
    50 xp
    Identifying question purpose
    100 xp
    Validating focus group feedback
    50 xp
    Net Promoter Score
    50 xp
    Collecting additional data
    50 xp
    Sorting data sources
    100 xp
    Asthma frequency
    50 xp
    Data storage and retrieval
    50 xp
    Cloud platforms
    50 xp
    Querying a database
    50 xp
    Which type of database?
    100 xp
  3. 3

    Analysis and Visualization

    In this chapter, we'll discuss ways to explore and visualize data through dashboards. We'll discuss the elements of a dashboard and how to make a directed request for a dashboard. This chapter will also cover making ad hoc data requests and A/B tests, which are a powerful analytics tool that de-risk decision-making.

    Play Chapter Now
    Dashboards
    50 xp
    Classifying dashboard elements
    50 xp
    Improving a dashboard
    50 xp
    Choosing the right dashboard
    100 xp
    Ad hoc analysis
    50 xp
    Filling out an ad hoc request
    50 xp
    Classifying requests
    100 xp
    A/B Testing
    50 xp
    Creating an A/B testing workflow
    100 xp
    Sample size
    50 xp
    Intermediate results
    50 xp
  4. 4

    Prediction

    In this final chapter, we'll discuss the buzziest topic in data science: machine learning! We'll cover supervised and unsupervised machine learning, and clustering. Then, we'll move on to special topics in machine learning, including time series prediction, natural language processing, deep learning, and explainable AI!

    Play Chapter Now
    Supervised machine learning
    50 xp
    When to use Supervised Learning
    100 xp
    Features and labels
    50 xp
    Model evaluation
    50 xp
    Clustering
    50 xp
    Supervised vs. unsupervised
    100 xp
    Cluster size selection
    50 xp
    Special topics in Machine Learning
    50 xp
    Classifying machine learning tasks
    100 xp
    Sentiment Analysis
    50 xp
    Deep Learning and Explainable AI
    50 xp
    Finding the correct solution
    100 xp
    Should I use Deep Learning?
    50 xp

In the following tracks

Data Skills for Business

Collaborators

Amy Peterson
Hillary Green-Lerman
Mari Nazary HeadshotMari Nazary

VP Product & Learner Experience at the Lambda School

Mari Nazary is a global EdTech executive who partners with educators and subject matter experts to build and scale effective, outcomes-focused, SaaS learning solutions. After spending over a decade working in EdTech for multimillion dollar brands and startups, Mari knows what truly closes the skills gap across the world—and it’s not mastering the marketing flavor of the week. It’s how well you understand your learners’ needs in order to help them measure and achieve real-world success. Mari has designed digital learning solutions for worldwide audiences including Rosetta Stone, EF Education First, and DataCamp. In addition to her instructional design and curriculum development expertise, Mari is a certified Agile scrum master, Python programmer, and data analyst. Mari holds an MA in Linguistics from Middlebury College and a BA from Barnard College in Classics.
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Michael Chow HeadshotMichael Chow

Data Scientist

Michael is a data scientist at DataCamp, where he develops models for adaptive assessment. He has programmed in python and R for a little over a decade, and received a PhD in cognitive psychology from Princeton University. His research interests include statistical methods, skill acquisition, and human memory. You can follow him on twitter @chowthedog.
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kaelen medeiros Headshotkaelen medeiros

Data Scientist

Kaelen is a data scientist and an admin for the R-Ladies Global community. Kaelen received a MS in Biostatistics from Louisiana State University Health Sciences Center, where they worked at the Louisiana Tumor Registry. Before DataCamp, they designed experiments (and more!) for the American College of Surgeons, HERE Technologies, and HealthLabs. If you meet them, you will undoubtedly hear about their cat, Scully, within the first 3 minutes. Other favorite topics include aliens, popcorn, podcasts, and nail polish.
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Ramnath Vaidyanathan HeadshotRamnath Vaidyanathan

VP of Product Research at DataCamp

Ramnath Vaidyanathan is the VP of Product Research at DataCamp, where he drives product innovation and data-driven development. He has 10+ years experience doing statistical modeling, machine learning, optimization, retail analytics, and interactive visualizations. He brings a unique perspective to product development, having worked in diverse industries like management consulting, academia, and enterprise softwares. Prior to joining DataCamp, he worked as a data scientist at Alteryx, leading the roadmap for interactive visualizations and dashboards for predictive analytics. Prior to Alteryx, he was an Assistant Professor of Operations Management in the Desautels Faculty of Management at McGill University. His research primarily focused on the application of predictive analytics and optimization methodologies to improve operational decisions in retailing. He got his Ph.D. in Operations Management from the Wharton School.
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Don’t just take our word for it

*4.3
from 17 reviews
65%
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  • András G.
    about 2 months

    Video, transcription, exercises: together they help to learn quicker, I like it.

  • MICHAEL M.
    2 months

    nan

  • Samuel O.
    2 months

    It was engaging and I enjoyed the course

  • David G.
    3 months

    Loved the section about ai and the different types of ai and how they can be used to solve business problems.

  • Ramana T.
    3 months

    Easy to use. Works well. Very intuitive.

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"Video, transcription, exercises: together they help to learn quicker, I like it."

András G.

"nan"

MICHAEL M.

"It was engaging and I enjoyed the course"

Samuel O.

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