The 56 Best LinkedIn Learning Data Analytics Courses for 2023
The editors at Solutions Review have compiled this list of the best LinkedIn Learning data analytics courses (top-rated) to consider taking.
Data analytics skills are in high demand among organizations that are looking to use their collected data to generate valuable business insight. The pandemic and subsequent ‘new normal’ of remote work are furthering demands for these skills. Many are turning to online learning platforms to up their game and acquire the data analytics skills most likely to help them stand out. And whether you are looking to acquire those skills for work or for play, this collection of Pluralsight data analytics courses will help you learn the ropes so you can pilot some of the most widely used tools in no time!
With this in mind, the editors at Solutions Review have compiled this list of the best LinkedIn Learning data analytics courses to consider taking. The platform is perfect for those looking to take multiple courses or acquire skills in different areas, or for those who want the most in-depth experience possible through access to LinkedIn Learning’s entire course library or learning paths. In sum, LinkedIn Learning offers training in more than 13 distinct categories with thousands of modules.
Note: Our editors assembled this directory by listing the best LinkedIn Learning data analytics courses in the most popular associated coverage areas including Tableau Software, MicroStrategy, Alteryx, Microsoft Power BI, Databricks, Qlik Sense, Apache Spark, Data Literacy, Predictive Analytics, Machine Learning, Data Visualization, Data Science, Business Analytics, Business Intelligence, Excel Data Analysis, R, and Data Analysis Expressions (DAX).
Description: In this course, Robin Hunt defines what data analytics is and what data analysts do. She then shows how to identify your data set—including the data you don’t have—and interpret and summarize data. She also shows how to perform specialized tasks such as creating workflow diagrams, cleaning data, and joining data sets for reporting.
Related paths/tracks: Data Analytics for Business Professionals, Data Visualization for Data Analysis and Analytics, Introduction to Business Analytics, Business Analytics Foundations: Predictive, Prescriptive, and Experimental Analytics
Description: In this course, learn what you need to know to analyze and display data using Tableau 2020—and make better, more data-driven decisions for your company. Discover how to install Tableau, connect to data sources, and sort and filter your data. Instructor Curt Frye also demonstrates how to create and manipulate data visualizations—including highlight tables, charts, scatter plots, histograms, maps, and dashboards—and shows how to share your visualizations. We also encourage you to browse LinkedIn’s complete Tableau training library, which features more than 700 (!) results based on your experience level.
Description: In this course, Gini von Courter helps you get started with this powerful toolset. Gini begins by covering the web-based Power BI service, explaining how to import data, create visualizations, and arrange those visualizations into reports. She discusses how to pin visualizations to dashboards for sharing, as well as how to ask questions about your data with Power BI Q&A. She also provides coverage of Power BI Mobile and shows how to use the data modeling capabilities in Power BI Desktop.
Related path/track: Advanced Microsoft Power BI
Description: In this course, Lynn Langit digs into patterns, tools, and best practices that can help developers and DevOps specialists use Azure Databricks to efficiently build big data solutions on Apache Spark. Lynn covers how to set up clusters and use Azure Databricks notebooks, jobs, and services to implement big data workloads. She also explores data pipelines with Azure Databricks—including how to use ML Pipelines—as well as architectural patterns for machine learning.
Description: In this course, learn how to analyze and display data using this powerful platform. Instructor Curt Frye shows how to install or connect to Qlik Sense, import and summarize data, create an app from a data source, and create and manage tables. He also steps through how to manage Qlik Sense sheets, which provide a canvas you can fill with data, charts, and other visualizations. Plus, learn how to create and manipulate a variety of data visualizations, from bar charts to histogram charts; create PivotTables and reports; sort and filter data; and combine your app’s sheets with objects, text, and images to create a story that clearly conveys your message.
Description: In this course, get up to speed with Spark, and discover how to leverage this popular processing engine to deliver effective and comprehensive insights into your data. Instructor Ben Sullins provides an overview of the platform, going into the different components that make up Apache Spark. He shows how to analyze data in Spark using PySpark and Spark SQL, explores running machine learning algorithms using MLib, demonstrates how to create a streaming analytics application using Spark Streaming, and more.
Description: In this course, join Barton Poulson as he focuses on the fundamentals of data fluency, or the ability to work with data to extract insights and determine your next steps. Barton shows how exploring data with graphs and describing data with statistics can help you reach your goals and make better decisions. Instead of focusing on particular tools, he concentrates on general procedures that can help you solve specific problems. Find out how to prepare data, explore it visually, and use statistical methods to describe it.
Description: This course provides that perspective through the lens of a veteran practitioner who has completed dozens of real-world projects. Keith McCormick is an independent data miner and author who specializes in predictive models and segmentation analysis, including classification trees, cluster analysis, and association rules.
Related paths/tracks: Predictive Analytics Essential Training for Executives, Python: Working with Predictive Analytics, Business Analytics Foundations: Predictive, Prescriptive, and Experimental Analytics
Description: In this course, we review the definition and types of machine learning: supervised, unsupervised, and reinforcement. Then you can see how to use popular algorithms such as decision trees, clustering, and regression analysis to see patterns in your massive data sets. Finally, you can learn about some of the pitfalls when starting out with machine learning.
Description: Join data visualization expert Bill Shander as he guides you through the process of turning “facts and figures” into “story” to engage and fulfill our human expectation for information. This course is intended for anyone who works with data and has to communicate it to others, whether a researcher, a data analyst, a consultant, a marketer, or a journalist.
Description: This course provides an accessible, non-technical overview of the field, covering the vocabulary, skills, jobs, tools, and techniques of data science. Instructor Barton Poulson defines the relationships to other data-saturated fields such as machine learning and artificial intelligence. He reviews the primary practices: gathering and analyzing data, formulating rules for classification and decision-making, and drawing actionable insights. He also discusses ethics and accountability and provides direction to learn more.
Related paths/tracks: Introduction to Data Science, Learning Data Science: Understanding the Basics, Python for Data Science Essential Training Part 1, Data Science Foundations: Data Mining, Learning Data Science: Tell Stories With Data, Data Science Foundations: Data Engineering
Description: In this introductory overview, economist and author John Johnson shows leaders and executives how to use analytics to make data-driven decisions and gain a competitive advantage. First, see examples of real-life analytics in action. Then explore the differences between predictive and prescriptive analytics, and find out how to formulate questions—a process that can be almost as revealing as finding the answers. John then shows how to collect, clean, and aggregate data from different sources across your organization, and identify when data is flawed.
Related path/track: Learning Digital Business Analysis
Description: This course explains what business intelligence is, why it’s important, and how consultants can tap into business intelligence when delivering outcomes for clients. Instructor Joshua Rischin has been a consultant to over 40 organizations. Here he shares techniques and examples from his career with you. Learn how to profile the client’s business, gather high-quality and relevant data, and present your insights and recommendations to clients with detailed visualizations and reports.
Related path/track: Excel Business Intelligence
Description: This course helps you unlock the power of your organization’s data using the data analysis and visualization tools built into Excel. Author Curt Frye starts with the foundational concepts, including basic calculations such as mean, median, and standard deviation, and provides an introduction to the central limit theorem. He then shows how to visualize data, relationships, and future results with Excel’s histograms, graphs, and charts. He also covers testing hypotheses, modeling different data distributions, and calculating the covariance and correlation between data sets.
Related paths/tracks: Excel 2016: Managing and Analyzing Data, Managing and Analyzing Data in Excel, Data Visualization for Data Analysis and Analytics, Excel: Power Pivot for Beginners, Excel: PivotTables in Depth, Excel: Tracking Data Easily and Efficiently, Excel Statistics Essential Training: 1, Excel: Advanced Formulas and Functions, SQL: Data Reporting and Analysis, Excel: Economic Analysis and Data Analytics
Description: Learn the basics of R and get started finding insights from your own data, in this course with professor and data scientist Barton Poulson. The lessons explain how to get started with R, including installing R, RStudio, and code packages that extend R’s power. You also see first-hand how to use R and RStudio for beginner-level data modeling, visualization, and statistical analysis. By the end of the course, you’ll have a thorough introduction to the power and flexibility of R, and understand how to leverage this tool to explore and analyze a wide variety of data.
Description: In this course, Gini von Courter covers the essentials of working with DAX, sharing best practices for data model design and optimization along the way. Learn how to work with DAX aggregate functions, add calculated columns, create measures, and work with DAX logical and filter functions.
Related path/track: Excel Business Intelligence: Power Pivot and DAX
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