Loved by learners at thousands of companies
Learn to Use Data Science for BusinessWhat 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 WorkflowYou’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 DataYou'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.
Introduction to Data ScienceFree
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.What is Data Science?50 xpCustomer Segmentation Workflow100 xpBuilding a customer service chatbot100 xpImproving OKRs50 xpApplications of Data Science50 xpAssigning data science projects100 xpInvestment research50 xpBuilding a data science team50 xpInterpreting a team sprint50 xpEditing a job posting50 xpMatching skills to jobs100 xpClassifying data tasks100 xp
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.Data sources and risks50 xpClassifying data for security100 xpCreating web data events50 xpProtecting PII50 xpSolicited data50 xpIdentifying question purpose100 xpValidating focus group feedback50 xpNet Promoter Score50 xpCollecting additional data50 xpSorting data sources100 xpAsthma frequency50 xpData storage and retrieval50 xpCloud platforms50 xpQuerying a database50 xpWhich type of database?100 xp
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.
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!Supervised machine learning50 xpWhen to use Supervised Learning100 xpFeatures and labels50 xpModel evaluation50 xpClustering50 xpSupervised vs. unsupervised100 xpCluster size selection50 xpSpecial topics in Machine Learning50 xpClassifying machine learning tasks100 xpSentiment Analysis50 xpDeep Learning and Explainable AI50 xpFinding the correct solution100 xpShould I use Deep Learning?50 xp
In the following tracksData Skills for Business
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.
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.
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.
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.