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Foundation of Data Analytics

Master data analytics fundamentals, from data value to AI-driven insights, with 31 labs for practical, real-world application.

(FDN-DA.AE2) / ISBN : 979-8-90059-116-2
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About This Course

This Foundation of Data Analytics course isn't about theory; it's about getting your hands dirty. We'll cut through the noise, showing you how to leverage data for critical business decisions, from initial data value assessment to advanced predictive analytics using AI models. You'll tackle real-world scenarios with 31 hands-on labs, understand data typologies, and navigate big data governance. We'll cover essential business statistics, optimization, and even get you running with Python and R for actual data science. Expect to learn effective data visualization, but also its pitfalls. This rigorous Foundation of Data Analytics training prepares you for certification, focusing on practical mastery. Be ready to challenge assumptions and build robust analytical skills, understanding that tools have limitations.

Skills You’ll Get

  • Data-Driven Decision Making: Understanding how data impacts managerial decisions, identifying the business analytics process, and selecting appropriate tools, recognizing that no single tool fits all scenarios.
  • Data Manipulation & Governance: Mastering efficient data handling, formatting, formula application, and comprehending data typologies, database approaches, and the inherent challenges of big data governance.
  • Statistical & Predictive Modeling: Applying probability, statistical laws, optimization techniques, and leveraging AI models for predictive analytics, including understanding their inherent trade-offs between complexity and interpretability.
  • Analytics Programming & Visualization: Gaining practical experience with Python and R for data science tasks, and developing skills in effective data visualization while recognizing its potential to mislead if not executed carefully.
Download Course Outline

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
  • AI in Business Analytics
  • Responsible AI and Ethics
  • 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
  • Basic Statistical Functions: Mean, Median, and Mode
  • Using XLOOKUP to Merge Data
  • Filtering and Sorting
  • Using PivotTables
  • Power Query for Data Cleaning
  • Power Pivot and Data Model
  • Dynamic Array Functions
  • Excel + AI (Copilot & Formula Generation)
  • Creating Dashboards with Slicers
  • 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
  • Data Governance and Responsible AI
  • IT's About Business: Data Privacy in AI Systems
  • Summary
  • Discussion Questions
  • Problem-Solving Activities
  • Closing Case 1
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
  • From Statistics to Machine Learning
  • 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
  • Data-Driven Blending Models for Product Consistency
  • Modeling Risk
  • Predictive Analytics using AI models
  • It is important to understand that
  • Predicting customer Needs 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
  • Machine Learning for Forecasting
  • Summary
  • Formulas
  • Supplementary Problems
  • Analyzing the Databases
  • Case - Caterpillar, Inc.
6

Programming and AI Tools for Analytics

  • Getting Up and Running with Python and 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
  • Interactive Dashboards (Power BI / Tableau)
  • AI-Generated Insights and Storytelling
  • Ethical Visualization in the AI Era
  • Summary
8

The Future of Data Analytics

  • Augmented Analytics
  • Human + AI Collaboration
  • Real-Time Decision Systems
  • Prompt Engineering 
  • Skills for Modern Analysts
  • Summary

1

The Value of Data

  • Creating a Scenario Summary Report for Forecast Analysis
2

Working with Data

  • Using Relative, Absolute, and Mixed Cell References
  • Preparing Sales Data for Analysis
  • Retrieving Sales Data Using the OFFSET Function
  • Analyzing Sales Data Using SUM, AVERAGE, MIN, and MAX Functions
  • Using MATCH and XLOOKUP for Efficient Data Analysis
  • Analyzing Sales Data Using FILTER, SORT, and UNIQUE Functions in Excel
  • Cleaning Sales Data Using Power Query
  • Building a Data Model for Sales Analysis
  • Creating Data Visualizations in Microsoft Excel with Copilot
  • Creating an Interactive Worksheet Using Slicer
3

Data Typologies and Governance

  • Understanding the Data Hierarchy
  • Understanding Primary and Foreign Keys
  • Observing Performance Issues with Large Data
4

Business Statistics

  • Applying Probability Techniques for Marketing Analytics
  • Hypothesis Testing for Product Performance Analysis
  • Analyzing Customer Preferences Using Set Operations
5

Optimization and Forecasting

  • Using the IF and SUMIF Functions to Analyze and Categorize Sales Data
  • Calculating Total Cost Using SUMPRODUCT
  • Building a Regression Line and Making Predictions
6

Programming and AI Tools for Analytics

  • Exploring Data Structures and Basic Functions in R
  • Performing Mathematical and Matrix Operations in R
  • Analyzing and Manipulating Data in R
  • Generating Predictions and Summarizing Results in R
  • Creating and Accessing DataFrames
  • Implementing Random Forest Regression
  • Exploring CSV Data
  • Performing Logistic Regression for Binary Classification
7

Data Visualization

  • Visualizing Sales Data Using Conditional Formatting and Column Charts
  • Analyzing Sales Trends Using a Line Chart
8

The Future of Data Analytics

  • Exploring Different Prompt Styles

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Absolutely. While we move fast, this Foundation of Data Analytics for beginners course starts with core concepts like the value of data and basic manipulation. We assume no prior deep analytics experience, but a willingness to engage with technical concepts and 31 hands-on labs is crucial. Expect to put in the work; there are no shortcuts to mastery.

You'll gain practical exposure to Python and R for actual data science tasks. We also cover business analytics tools, including techniques like Solver examples and pivot tables, to ensure you're proficient in a range of applications. The focus is on practical application, not just theoretical understanding, recognizing that each tool has its strengths and weaknesses.

The 31 hands-on labs are where the real learning happens. They force you to apply concepts immediately, from data cleaning to building predictive models. This isn't passive learning; it's about getting your hands dirty, making mistakes, and understanding the practical constraints of real-world data. Expect to encounter messy data and imperfect solutions – that's the reality.

This Foundation of Data Analytics certification and career paths lead to roles like Data Analyst, Business Intelligence Analyst, or even entry-level Data Scientist. You'll develop critical Foundation of Data Analytics skills for data analyst roles, understanding how to translate data into actionable insights, a non-negotiable skill in today's market. However, a certification is a starting point, not a guarantee of employment without practical application.

The value of any certification lies in the skills you acquire. This Foundation of Data Analytics certification is worth it if you commit to mastering the material, especially the practical labs. It validates your foundational understanding of data analytics, making you a more competitive candidate for data-driven roles. However, it's a stepping stone, not a finish line; continuous learning is essential.

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