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Data analysts examine information using data analysis tools and help their teams develop insights and business strategies. You’ll need skills in math, statistics, communications, and working with tools designed to do data analytics and data visualization. Explore this high-demand career.

• Learn the technical skills for data analyst career paths.

• Develop your competencies in high-demand analysis tools.

• Build communication, teamwork, and problem-solving skills.

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First course : Learning Data Analytics1.

Every person who works with data has to perform analytics at some point. This popular training course—dramatically expanded and enhanced for 2018—teaches analysts and non-analysts alike the basics of data analytics and reporting. 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. Coverage continues with best practices for data analytics projects, such as verifying data and conducting effective meetings, and common mistakes to avoid. Then learn techniques for repurposing, charting, and pivoting data. Plus, get helpful productivity-enhancing shortcuts and troubleshooting tips for the most popular data analytics program, Microsoft Excel.

• Define data analysis and data analyst.

• List roles in data analysis.

• Explain data fields and types.

• Define syntax.

• Explain how to interpret existing data.

• Describe data best practices.

• Repurpose data.

• Create a data dictionary.

• Compare and contrast linking versus embedding charts and data.

• Build pivot charts with slicers.

• Data Analytics

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Second course : Data Fluency: Exploring and Describing Data

Data analysis isn’t just for specialists who need to make sense of massive datasets. Decision-makers in every industry can benefit from a basic understanding of the goals and concepts of applied data analysis. 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.

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• The ROI of data fluency

• Data ethics

• Preparing data

• Assessing the quality of data

• Visualizing data with bar, pie, and line charts

• Describing variability with the variance and standard deviation

• Describing associations with correlations

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Third course : Excel Statistics Essential Training: 1

Data isn’t valuable until you put it to good use. Statistics transforms data into meaningful information, enabling organizations to make better decisions and predictions. That’s why statistics—collecting, analyzing, and presenting data—is a valuable skill for anyone in business or academia. In this course, Joseph Schmuller teaches the fundamentals of descriptive and inferential statistics and shows you how to apply them in Microsoft Excel—an inexpensive and accessible application that offers an array of powerful statistical tools. Using the built-in functions, and charts, along with the Analysis Toolpak add-on, Joe explains how to organize and present data, understand sampling distributions, test hypotheses, and draw conclusions. He covers probabilities, averages, variability, distribution, estimation, variance, regression testing, and more. By the end of the course, you should be able to fully understand and apply basic statistical concepts to a wide variety of data.

• Explain how to calculate simple probability.

• Review the Excel statistical formulas for finding mean, median, and mode.

• Differentiate statistical nomenclature when calculating variance.

• Identify components when graphing frequency polygons.

• Explain how t-distributions operate.

• Describe the process of determining a chi-square.

• Microsoft Excel

• Statistical Data Analysis

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Fourth course : Learning Excel: Data Analysis

Microsoft Excel is an important tool for data analysis. It helps companies accurately assess situations and make better business decisions. 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. Finally, he reviews the process of calculating Bayesian probabilities in Excel. Each chapter includes practical examples that show how to apply the techniques to real-world business problems.

• Distinguish between the mean, median, and mode.

• Describe the relationship between variance and standard deviation.

• Identify a nondirectional hypothesis.

• Point out the difference between COVARIANCE.P and COVARIANCE.S.

• Explain correlation.

• Analyze Bayes’ rule.

• Data Analysis

• Microsoft Excel

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Fifth course: Learning Data Visualization

Got a big idea? You need to get it across quickly and efficiently, or modern audiences will move on to the next story clamoring for their attention. Data visualization allows you to make the complex simple, the abstract tangible, and the invisible (data) visible with great illustrations. In this course, Bill Shander shows how to understand your data and your audience, craft the story you need to tell, and determine the best visual model and details to use for that story.

• Describe the process by which individuals’ interests are incorporated into data visualizations.

• Differentiate the use of the Ws in data visualization.

• Explain techniques involved in defining your narrative when visualizing data.

• Identify the factors that make data visualizations relatable to an audience’s interests and needs.

• Review the appropriate use of charts in data visualizations.

• Define the process involved in applying interactivity to data visualizations.

• Data Visualization

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Sixth course : Power BI Essential Training

Discover how to quickly glean insights from your data using Power BI. This formidable set of business analytics tools—which includes the Power BI service, Power BI Desktop, and Power BI Mobile—can help you more effectively create and share impactful visualizations with others in your organization. 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.

• Apply the required hardware to successfully run Power BI.

• Distinguish between different data types in Power BI.

• Correlate the similarities between visualizations in Power BI.

• Identify the outcomes when changes are made within Power BI and Power BI Desktop.

• Differentiate between dashboards and reports within Power BI.

• Determine user roles within a workspace in Power BI.

• Manage workspaces and tools within the Power BI mobile application.

• Business Analytics

• Performance Dashboards

• Microsoft Power BI

• Data Visualization

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Seventh course : Tableau Essential Training (2020.1)

Tableau is a widely used data analytics and visualization tool that many consider indispensable for data-science-related work. Its drag-and-drop interface makes it easy to sort, compare, and analyze data from multiple sources, including Excel, SQL Server, and cloud-based data repositories. 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. Along the way, he highlights the new features packed into this edition of the software, including Viz Animations, which allows you to visually follow the movement of marks in a data visualization to see and understand your changing data.

• Explain where a user would navigate to seek specific help in Tableau.

• Determine the best approach for using Excel in Tableau.

• Interpret how to use the features and functions of Tableau when creating charts.

• Describe how best to manage data in a worksheet or visualization.

• Explain how to create a selection filter for certain values.

• Explain how to manage data for different chart formats.

• Tableau

• Data Analytics

• Data Visualization

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