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Self-Paced Course

Statistics for Data Analysis

Learn essential statistics for data analysis, including probability distributions, confidence intervals, hypothesis tests, regression and more

Course Hours13 hours
Skills Learned
Data Analysis
Tools
Excel
Course Level
Overview
Credentials

Course Description

This is a hands-on, project-based course designed to help you learn and apply essential statistics concepts for data analysis & business intelligence.

Our goal is to simplify and demystify the world of statistics, and empower everyday people to understand and apply these tools and techniques – even if you have absolutely no background in math or stats!

We'll start by discussing the role of statistics in business intelligence, the difference between sample and population data, and the importance of using statistical techniques to make smart predictions and data-driven decisions.

Next we'll explore our data using descriptive statistics and probability distributions, introduce the normal distribution and empirical rule, and learn how to apply the central limit theorem to make inferences about populations of any type.

From there we'll practice making estimates with confidence intervals, and using hypothesis tests to evaluate assumptions about unknown population parameters. We'll introduce the basic hypothesis testing framework, then dive into concepts like null and alternative hypotheses, t-scores, p-values, type I vs. type II errors, and more.

Last but not least, we'll introduce the fundamentals of regression analysis, explore the difference between correlation and causation, and practice using basic linear regression models to make predictions.

Throughout the course, you'll play the role of a Recruitment Analyst for Maven Business School. Your goal is to use the statistical techniques you've learned to explore student data, predict the performance of future classes, and propose changes to help improve graduate outcomes.

You'll also practice applying your skills to 5 real-world bonus projects, and use statistics to explore data from restaurants, medical centers, pharmaceutical companys, safety councils, airlines, and more.

If you're an analyst, data scientist, business intelligence professional, or anyone looking to make smart, data-driven decisions, this course is for you.

 

COURSE CONTENTS:

  • 8 hours on-demand video (13 CPE credits)

  • 18 homework assignments (plus 5 course projects)

  • 6 quizzes

  • 2 skills assessments (1 benchmark, 1 final)

COURSE CURRICULUM:

Course Outline

WHO SHOULD TAKE THIS COURSE?

  • Analysts or data scientists looking to learn essential statistics
  • BI professionals who want to make confident, data-driven decisions
  • Anyone using data to make assumptions, estimates or predictions

WHAT ARE THE COURSE REQUIREMENTS?

  • You do NOT need a math or stats background to take this course
  • We will be using Microsoft Excel (Office 365) for course demos
  • No advance preparation is required (familiarity with basic descriptive statistics is a plus, but not a prerequisite)

WHAT ARE THE COURSE OBJECTIVES?

  • Identify the role and applications of statistics in the data analytics landscape, specifically in regard to making estimates about population parameters from sample statistics

  • Identify and interpret different types of descriptive statistics, including frequency distributions, measures of central tendency, and measures of variability

  • Identify different types of probability distributions and their properties, and interpret calculations using the normal distribution, including probabilities, value estimates, and z-scores

  • Identify the properties, implications, and applications of the Central Limit Theorem, including the concept of a sampling distribution and the standard error

  • Identify and interpret the main components of a confidence interval for the mean and proportions of one or two populations, including the point estimate and margin of error

  • Identify and interpret the steps and components of a hypothesis test, including the null & alternative hypotheses, the significance level, the test statistic, the p-value, and the possible conclusions and errors

  • Identify linear relationships between numerical variables, and interpret their linear regression models, including model evaluation statistics like the determination, standard error, and F significance

CPE ACCREDITATION DETAILS:

  • CPE Credits: 13.0

  • Field of Study: Information Technology

  • Delivery Method: QAS Self Study

Maven Analytics LLC is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have the final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org.

For more information regarding administrative policies such as complaints or refunds, please contact us at admin@mavenanalytics.io or (857) 256-1765.

*Last Updated: November 16, 2022

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