1h 39m • COURSE

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.

2h 6m • COURSE

Statistics is not just the realm of data scientists. All types of jobs use statistics. Statistics are important for making decisions, new discoveries, investments, and predictions. Whether the subject is political races, sports rankings, shopping trends, or healthcare advancements, statistics is an instrument for understanding your favorite topic at a deeper level. With these beginner-level lessons, you too can master the terms, formulas, and techniques needed to perform the most common types of statistics.

Professor Eddie Davila covers statistics basics, like calculating averages, medians, modes, and standard deviations. He shows how to use probability and distribution curves to inform decisions, and how to detect false positives and misleading data. Each concept is covered in simple language, with detailed examples that show how statistics are used in real-world scenarios from the worlds of business, sports, education, entertainment, and more. These techniques will help you understand your data, prove theories, and save time, money, and other valuable resources—all by understanding the numbers.

1h 56m • COURSE

Statistics are a core skill for many careers. Basic stats are critical for making decisions, new discoveries, investments, and even predictions. But sometimes you need to move beyond the basics. Statistics Fundamentals – Part 2 takes business users and data science mavens into practical, example-based learning of the intermediate skills associated with statistics: samples and sampling, confidence intervals, and hypothesis testing.

Eddie Davila first provides a bridge from Part 1, reviewing introductory concepts such as data and probability, and then moves into the topics of sampling, random samples, sample sizes, sampling error and trustworthiness, the central unit theorem, t-distribution, confidence intervals (including explaining unexpected outcomes), and hypothesis testing. This course is a must for those working in data science, business, and business analytics—or anyone else who wants to go beyond means and medians and gain a deeper understanding of how statistics work in the real world.

1h 41m • COURSE

Statistics are everywhere, in every industry, but they're a must for anyone working in data science, business, or business analytics. If you're in one of these specialized fields, chances are you need an advanced understanding of statistics. Complete your mastery in this course, part 3 of our Statistics Fundamentals series. Eddie Davila covers concepts such as small sample sizes, t-distribution, degrees of freedom, chi-square testing, and more. This advanced skills training moves learners into the practical study and application of experimental design, analysis of variance, population comparison, and regression analysis. Use these lessons to go beyond the basics and dive deeper into the specific factors that influence your own calculations and results.

1h 21m • COURSE

Big data is transforming the world of business. Yet many people don't understand what big data and business intelligence are, or how to apply the techniques to their day-to-day jobs. This course addresses that knowledge gap, giving businesspeople practical methods to create quick and relevant business forecasts using big data.

Join Professor Michael McDonald and discover how to use predictive analytics to forecast key performance indicators of interest, such as quarterly sales, projected cash flow, or even optimized product pricing. All you need is Microsoft Excel. Michael uses the built-in formulas, functions, and calculations to perform regression analysis, calculate confidence intervals, and stress test your results. You'll walk away from the course able to immediately begin creating forecasts for your own business needs.

LinkedIn Learning (Lynda.com) is a PMI Registered Education Provider. This course qualifies for professional development units (PDUs). To view the activity and PDU details for this course, click here.

The PMI Registered Education Provider logo is a registered mark of the Project Management Institute, Inc.

1h 28m • COURSE

A proper predictive analytics and data-mining project can involve many people and many weeks. There are also many potential errors to avoid. A "big picture" perspective is necessary to keep the project on track. 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. Here he shares his knowledge with you. Walk through each step of a typical project, from defining the problem and gathering the data and resources, to putting the solution into practice. Keith also provides an overview of CRISP-DM (the de facto data-mining methodology) and the nine laws of data mining, which will keep you focused on strategy and business value.

1h 37m • COURSE

1h 37m • COURSE

Big data is transforming the world of business. Yet many people don't understand what big data and business intelligence are, or how to apply the techniques to their day-to-day jobs. This course addresses that knowledge gap by showing how to use large volumes of economic data to gain key business insights and analyze market conditions.

Professor Michael McDonald demonstrates how to harness the wealth of information available on the Internet to forecast statistics such as industry growth, GDP, and unemployment rates, as well as factors that directly affect your business, like property prices and future interest rate hikes. All you need is Microsoft Excel. Michael uses the built-in formulas, functions, and calculations to perform regression analysis, calculate confidence intervals, and stress test your results. He also covers time series exponential smoothing, fixed effects regression, and difference estimators. You'll walk away from the course able to immediately begin creating forecasts for your own business needs.

LinkedIn Learning (Lynda.com) is a PMI Registered Education Provider. This course qualifies for professional development units (PDUs). To view the activity and PDU details for this course, click here.

The PMI Registered Education Provider logo is a registered mark of the Project Management Institute, Inc.

49m 5s • COURSE

In a world where nearly everyone uses data to inform their business methodologies, an emerging consensus is that more emphasis needs to be placed on validating data; verifying that data-driven conclusions are accurate; and minimizing the risk that your conclusions are incorrect. Although most researchers know what meta-analysis is, few understand how to calculate an effect size from popular metrics such as risk ratios, or how the distinction between fixed and random effects can lead the meta-analyst astray. This advanced-level course for data science and statistics practitioners and researchers covers raw mean differences—specifically for experimental and comparison groups—and how to convert useful outcome measures such as relative risk and odds ratios to commensurate measures of effect size. Plus, learn about how confidence intervals are created for binary outcome measures.