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Foundation of Data Analytics

Kickstart your career with this beginner-friendly Data Analytics course by learning how to use data for making informed decisions.

(FDN-DA.AE1) / ISBN : 978-1-64459-277-9
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27 Rezension
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Über diesen Kurs

Foundation of Data Analytics is a beginner-friendly certification course that focuses on data science tools for effective analysis and visualization. This introduction to data analytics course teaches you the techniques for transforming raw data into actionable insights that can be used for making informed decisions. Learn how to perform data manipulation and the role of this information in improving business statistics. Understand the dynamics of data optimization, and forecasting techniques using regression analysis, and data visualizations.

Fähigkeiten, die Sie erwerben werden

  • Expertise is using Excel for handling and manipulating data
  • Identify issues and perform data clean up
  • Expertise in hypothesis testing, regression analysis, and correlation analysis
  • Understanding the business landscape and use of data
  • Knowledge of business analysis
  • Maintain high-quality data for effective data management
  • Creating informative charts for visual representation
  • Communicate data-driven insights
  • Exposure to R for Data science
Kursübersicht herunterladen

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 (σ Known)
  • Testing Hypotheses About a Population Mean Using the t Statistic (σ 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

1

The Value of Data

  • Summarizing the Aspects of Business Analytics and its Applications
2

Working with Data

  • Freezing the Top Row
  • Using the AVERAGE Function
  • Using Relative, Absolute, and Mixed References
  • Formatting Numbers
  • Applying Conditional Formatting
  • Using the Paste Special Feature
  • Analyzing Data Using a Line Chart
  • Creating a PivotTable Automatically
  • Calculating the Minimum and Maximum Sales Value
  • Using the SUM Function
  • Using the MATCH Function
  • Using the VLOOKUP Function
  • Sorting Data
3

Data Typologies and Governance

  • Understanding Big Data
  • Understanding the Relational Database Model
4

Business Statistics

  • Understanding Business Statistics
  • Calculating the Statistics
5

Optimization and Forecasting

  • Using the SUMIF Function
  • Using the IF Function
6

Other Data Analytic Tools

  • Using the factor() Function
  • Using the str() Function
  • Using the sqrt() Function
  • Using the matrix() Function
  • Using the length() Function
  • Using the rbind() and cbind() Functions
  • Using the aggregate() Function
  • Using the order() Function
  • Using the predict() Function
  • Using the print() Function
  • Using the summary() Function
  • Using the which() Function
7

Data Visualization

  • Visualizing Data
  • Understanding Data Visualization
  • Creating and Analyzing Chart Types

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It is a basic data analytics certification course that teaches how to use data for making informed decisions.

Yes, it is the perfect beginner data analysis course and you don’t need any prior experience for enrollment.

Yes, you’ll have to appear for a post assessment at the end of this course to earn your certification.

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