Enhance your data analytics skills with a Data Analytics foundation course and lab. The course and lab offer interactive learning resources that help candidates learn how to use data typologies, data analytics tools, business statistics, data visualization with the working and value of data, and many more. The course also covers the subject areas like processing, collecting, storing, and analyzing to help you in advancing your professional career. The course will help you in advancing your professional career.

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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

Gain the foundational knowledge you need to take the first stesp on this topic.

1

  • The Value of Data

  • Opening Case
    Introduction
    Managers and Decision Making
    The Business Analytics Process
    Business Analytics Tools
    "Business Analytics Models: Descriptive Analytics, Predictive Analytics, and Prescriptive
    Analytics"
    Summary
    Discussion Questions
    Closing Case 1
    Closing Case 2
2

  • Working with Data

  • Some Sample Data
    Moving Quickly with the Control Button
    Copying Formulas and Data Quickly
    Formatting Cells
    Paste Special Values
    Inserting Charts
    Locating the Find and Replace Menus
    Formulas for Locating and Pulling Values
    Using VLOOKUP to Merge Data
    Filtering and Sorting
    Using PivotTables
    Using Array Formulas
    Solving Stuff with Solver
    OpenSolver: I Wish We Didn't Need This, but We Do
3

  • Data Typologies and Governance

  • Opening Case
    Introduction
    Managing Data
    The Database Approach
    Big Data
    Data Warehouses and Data Marts
    Knowledge Management
    Summary
    Discussion Questions
    Problem-Solving Activities
    Closing Case 1
    Closing Case 2
4

  • Business Statistics

  • Introduction to Probability
    Structure of Probability
    Marginal, Union, Joint, and Conditional Probabilities
    Addition Laws
    Multiplication Laws
    Conditional Probability
    Revision of Probabilities: Bayes' Rule
    Introduction to Hypothesis Testing
    Testing Hypotheses About a Population Mean Using the z Statistic (? Known)
    Testing Hypotheses About a Population Mean Using the t Statistic (? Unknown)
    Testing Hypotheses About a Proportion
    Testing Hypotheses About a Variance
    Solving for Type II Errors
    Summary
    Formulas
    Supplementary Problems
    Analyzing the Databases
    Case - Colgate-Palmolive Makes a “Total” Effort
5

  • Optimization and Forecasting

  • Why Should Data Scientists Know Optimization?
    Starting with a Simple Trade-Off
    Fresh from the Grove to Your Glass…with a Pit Stop through a Blending Model
    Modeling Risk
    Wait, What? You're Pregnant?
    Don't Kid Yourself
    Predicting Pregnant Customers at RetailMart Using Linear Regression
    Predicting Pregnant Customers at RetailMart Using Logistic Regression
    For More Information
    Correlation
    Introduction to Simple Regression Analysis
    Determining the Equation of the Regression Line
    Residual Analysis
    Standard Error of the Estimate
    Coefficient of Determination
    Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model
    Estimation
    Using Regression to Develop a Forecasting Trend Line
    Interpreting the Output
    Summary
    Formulas
    Supplementary Problems
    Analyzing the Databases
    Case - Caterpillar, Inc.
6

  • Other Data Analytic Tools

  • Getting Up and Running with R
    Doing Some Actual Data Science
7

  • Data Visualization

  • Why Do We Visualize Data?
    How Do We Visualize Data?
    Color
    Common Chart Types
    When Our Visual Processing System Betrays Us
    Every Decision Is a Compromise
    Summary

Audience

Anyone wishing to explore this topic will benefit.

Language

English

Prerequisites

While there are no prerequisites for this course, please ensure you have the right level of experience to be successful in this training.

Length: 365.0 days ( hours)

Level:

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