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 top-rated Pluralsight data analytics courses to consider taking. The platform is perfect for those looking to take multiple courses or acquire skills in multiple different areas, or for those who want the most in-depth experience possible through access to entire course libraries or learning paths. In sum, Pluralsight offers more than 7,000 expert-led video courses.
Note: Our editors assembled this directory by listing top-rated Pluralsight 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: Learn the skills needed to become a Tableau master. All of Pluralsight’s training goes through a rigorous vetting process to ensure the quality, usability, and uniqueness of the content. Stay up to date on one of the most popular data visualization products out there, and learn how to create and display data in Tableau. Scroll down the page to see other Tableau paths or browse Pluralsight’s collection of top Tableau courses.
Related path/track: Tableau Desktop for Analysts
Description: In this course, you will gain the ability to acquire data for use within MicroStrategy for analysis. First, you will learn how to work with the MicroStrategy object model. Next, you will discover the process for bringing in external unmodeled data. Finally, you will explore how to get that data in a format that supports your ability to analyze it. When you are finished with this course, you will have the skills and knowledge of MicroStrategy data acquisition needed to begin building your own reporting.
Description: In this course, you will gain the ability to work efficiently within the Alteryx Designer platform. First, you will learn to move and navigate around the user interface. Next, you will discover how to read in data from virtually any data source, and filter/blend/manipulate the data in order to gain that all-important actionable knowledge. Finally, you will explore how to visualize your data in a distribution ready report, and deploy that report to any number of recipients, all in one place – Alteryx Designer.
Description: You’ll start out with seeing how you can quickly and easily gather data from a variety of sources, and then cleanse and transform that data with just a few clicks. Next, you’ll also learn how you can enhance the results by integrating disparate data sources and adding simple calculations. Then, you’ll learn how to explore your data with visualizations and simple dashboards. Finally, you’ll learn what steps are necessary to keep your data up-to-date.
Description: In this course, Building Your First ETL Pipeline Using Azure Databricks, you will gain the ability to use the Spark-based Databricks platform running on Microsoft Azure, and leverage its features to quickly build and orchestrate an end-to-end ETL pipeline. And all this while learning about collaboration options and optimizations that it brings, but without worrying about the infrastructure management.
Description: Pluralsight’s Qlik Sense for Analysts path will teach you how to build effective visualizations and dashboards, as well as deeper mechanics of working with your data, and extending it with expressions and calculated fields. Pre-requisites include data literacy, data analytics literacy, and basic PC skills. This path is made up of six courses, two of which were designed for beginners, two for intermediate level users, and two for advanced personas.
Related path/track: Loading and Preparing Data for Analysis in Qlik Sense
Description: In this course, you’ll learn Spark from the ground up, starting with its history before creating a Wikipedia analysis application as one of the means for learning a wide scope of its core API. That core knowledge will make it easier to look into Spark’s other libraries, such as the streaming and SQL APIs. Finally, you’ll learn how to avoid a few commonly encountered rough edges of Spark. You will leave this course with a tool belt capable of creating your own performance-maximized Spark application.
Related paths/tracks: Beginning Data Exploration and Analysis with Apache Spark, Structured Streaming in Apache Spark 2
Description: In this course, you will learn foundational knowledge of critical and analytical thinking. First, you will explore critical thinking and different pieces of this crucial skill. Next, you will discover the systematic approach of analytical thinking. Finally, you will see the combined skills of critical and analytical thinking, which help impact data-informed decision making. When you’re finished with this course, you will have an understanding of these crucial skills, which are crucial for organizations to capitalize on their data.
Description: This course covers everything from the basic concepts of data analysis, to data warehouse design and data visualization principles. If you’re looking for a career change or already making your way into the world of transforming data into value, this course will help you understand all of the key concepts and get some hands-on skills, while being directed to where you can dig in deeper when you find something you’re interested in. If you’re already working in the data world, you can use this course as a reference.
Related path/track: Big Data Analytics with Tableau
Description: In this course, you will learn foundational knowledge of solving real-world data science problems. First, you will explore the basics of implementing supervised learning problems including linear regression and neural networks. Next, you will discover how recommendation systems can be implemented using TensorFlow. Finally, you will learn how to understand and implement reinforcement learning systems.
Description: Have you ever wondered what machine learning is? That’s what this course is designed to teach you. You’ll explore the open-source programming language R, learn about training and testing a model as well as using a model. By the time you’re done, you’ll have a clear understanding of exactly what machine learning is all about.
Related paths/tracks: Understanding Machine Learning with Python, Understanding Machine Learning with R, Machine Learning: Executive Briefing, How Machine Learning Works, Deploying Machine Learning Solutions
Description: In this course, you will gain the ability to build visually-pleasing charts that effectively communicate your message. First, you will learn the basic concept of data visualization, why the field is growing, and how data viz can make an impact. Next, you will discover a variety of effective chart types and learn the design practices that make them effective. Finally, you will explore how to leverage preattentive attributes in your visualizations in order to enable easy data interpretation.
Description: In this course, you will gain the ability to transform data into actionable insight. First, you will learn what data science is. Next, you will discover why data science is important for you and your business. Finally, you will explore how to get started becoming a data-driven organization. When you’re finished with this course, you will have the skills and knowledge of data science needed to transform data into actionable insight.
Description: This course introduces the work of business analysis, explores who undertakes business analyst functions, and the type of skills necessary to conduct business analysis successfully. Then, attention turns to needs assessment, where problems and opportunities are identified, organizational ability to respond is assessed, recommendations for action are developed, feasibility is weighed for various options, best options are selected, and business cases for action are developed.
Related path/track: Excel: An Analytics Superhub
Description: This course covers the current state of Business Intelligence in the cloud, as well as goes into functional examples using Google Big Query and Tableau Online. This is a beginner course, but it is assumed you are familiar with the basics of databases and business intelligence concepts.
Related paths/tracks: Introduction to Microsoft Business Intelligence End-User Tools, SQL Server Business Intelligence Overview, Introduction to Data Warehousing and Business Intelligence, Enterprise Business Intelligence with Tableau Server, Getting Started with Oracle Business Intelligence Enterprise Edition, Business Dashboard Fundamentals
Description: This course goes through how to use Excel as an analytics client for BI and Big Data. With coverage applicable to several versions of Excel, but with emphasis on the new capabilities of Excel 2013, the content includes details on, and numerous demos of, the analytics capabilities in the core Excel product and its add-ins, including PowerPivot, Power View, and preview add-ins code-named “Data Explorer” and “GeoFlow.” This course provides great detail on each tool and shows how they can be used in combination, as well as, with data from HDInsight/Hadoop; Microsoft’s Big Data Platform.
Related paths/tracks: Displaying Tables with Excel, Excel 2013: Analysis Techniques and Random Numbers, Loading Data into Excel, Exploring Data with PivotTables, Summarizing Data and Deducing Probabilities, Searching and Manipulating Data in Excel, Excel Data Lookup Function Playbook, Summarizing and Organizing Data in Excel, Pragmatic Self-Service BI with PowerPivot for Excel
Description: In this course, Programming with R, you will learn how to manipulate different objects. First, you will learn the basic syntax. Next, you will explore data types and data structures available in R. Finally, you will discover how to write your own functions by implementing control flow statements. When you are finished with this course, you will have a foundational knowledge of R programming that will help you as you move forward to data science.
Description: In this course, Getting Started with DAX Formulas in Power BI, Power Pivot, and SSAS, you’ll learn the basics of the DAX language. First, you’ll learn how DAX works and why it has such good performance. Next, you’ll explore how to encapsulate business logic using calculated columns and measures. Finally, you’ll discover a variety of ways to manipulate filters or analyze your data. When you’ve finished this course, you’ll have the skills and knowledge of DAX to model and analyze your data with the DAX expression language.
Related path/track: Common DAX Expressions and Scenarios Power BI Playbook
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