Statistics for Data Analytics - Live Online - begins June 23, 2020

June 23rd - 9 AM to 5 PM PT
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Businesses must turn their data into insights by using statistical analysis in order to be competitive. This course will teach you how to calculate and evaluate statistics for data analytics. Great course for beginners and analysts who want to leverage analytics within their transformation initiatives.

Data is everywhere!  But how can businesses turn that endless data into insights that will allow a competitive advantage?  What statistics are the most relevant to a specific business? 

This course is designed for beginners and analysts who have not used statistics frequently.  This is also a great class for managers or leaders who want to understand the statistics used by their analysts.  Managers will learn how to participate in the choices of statistical approaches and fully understand the impacts of the data analytics studies.  

You’ll use Microsoft Excel as a tool to calculate specific statistical values and discuss the interpretation of those statistics.  You will learn to create objectives for your data analysis by applying the SMART criteria. 

After mastering the basics, you will deep dive into more advanced statistics beginning with normal distributions learning how to recognize them and to understand the types of data statistics which can be applied to them including standard deviation.  The section on hypothesis testing for predictive analytics allows you to learn how to setup hypothesis testing to predict the future.  Basics of statistical probability including calculating permutations is explained. 

In addition, we will discuss how to evolve your data analytics through the different types of analytics using statistics.  All the concepts are explained in an easy-to-understand style with many hands-on exercises and discussions on best practices and practical usage.     

Course Outline:

  • Introduction to Statistics
  • Types of Data Analytics
  • Define Objectives for Data Analytics using SMART criteria
  • Statistics for Descriptive Analytics (mean, median, mode, range, etc)
  • Normal Distributions Statistics (variance, standard deviation, skewness, etc)
  • Benefits and Uses of All Statistics Discussed
  • Statistics for Diagnostic Analytics Including the Hypothesis Testing Process
  • Statistical Probability
  • Statistics for the Predictive Analytics Process
  • Using Practical Machine Learning in Prescriptive Analytics
  • Moving Between Types of Analysis

Unique Value of Course:

This data analytics course focuses on learning how to calculate different statistics and understanding when to use them.  All the concepts are explained in an easy-to-understand style with plenty of hands-on exercises and discussions on best practices and practical usage.  This course also includes managerial topics like creating objectives for data analytics and how to evolve your data analysis for better insights. 

Objectives:

  • Learn four different types of data analytics
  • Explore how to calculate and use statistics for common descriptive analytics
  • Practice creating objectives for data analytics in your organization by applying the SMART criteria
  • Practice how to recognize normal distributions and learn why they are important to data analytics
  • Calculate statistics for normal distribution analytics including standard deviation
  • Learn four steps to hypothesis testing including how to state the hypothesis and null hypothesis
  • Practice statistical probability including calculating permutations
  • Learn how to apply each of the six steps of the Predictive Analytics Process in order to predict the future
  • Learn the four steps of practical machine learning for data science in prescriptive analytics
  • Explore how to move between the types of analytics in order to evolve your insights and results

Target Audience / Who Should Attend:

  • Data Analysts
  • Business Analysts
  • Process Analysts
  • Agile Product Owners
  • Project Managers
  • Scrum Team Members
  • Executives
  • Managers in any of these areas
  • Anyone interested in applying analytics

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