FDN-DA.AE2

Foundation of Data Analytics

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

  • Practice in 31 Hands-On Labs — nothing to install
  • 8 Interactive Lessons and 107 topics mapped to the official exam objectives

Beginner Self-paced · 1 year access

31 Hands-On LiveLabs

Practice real IT tasks in guided environments.

  • Real environments
  • Auto-graded
  • No installation
8Interactive Lessons
107Topics
31LiveLab
8Videos
112Flashcards
112Glossary of terms

01 / Skills you'll get

What you will be able to do

Try Free → No credit card required
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.
  • 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.

Course Highlights

  • 8 Structured Lessons Comprehensive coverage of core course objectives
  • 31 Hands-On LiveLabs Interactive guided scenarios with instant evaluation
  • 1 Year Full Access Self-paced learning accessible anytime on all devices

02 / Lessons & labs

See exactly what you will learn and practice

Download outline (PDF)

Lessons

8 Interactive Lessons · 107 topics
01 The Value of Data 12 topics · 1 LiveLab
  • 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

1 LiveLab in this lesson — see the labs panel →

02 Working with Data 20 topics · 10 LiveLab
  • 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

10 LiveLab in this lesson — see the labs panel →

03 Data Typologies and Governance 13 topics · 3 LiveLab
  • 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

3 LiveLab in this lesson — see the labs panel →

04 Business Statistics 19 topics · 3 LiveLab
  • 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

3 LiveLab in this lesson — see the labs panel →

05 Optimization and Forecasting 25 topics · 3 LiveLab
  • 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.

3 LiveLab in this lesson — see the labs panel →

Hands-On Labs Our edge

31 LiveLabs
  • Creating a Scenario Summary Report for Forecast Analysis
  • 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
Labs run in your browser — nothing to install.

03 / FAQs

Questions before you start

Contact us ↗
Is the Foundation of Data Analytics course suitable for beginners?
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.
What specific tools and programming languages will I learn?
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.
How do the 31 hands-on labs enhance learning?
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.
What career opportunities does this certification open up?
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.
Is the Foundation of Data Analytics certification worth it?

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.

Keep going

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Turn Raw Data Into Real Business Impact

Learn data analysis, visualization, Python, R, statistics, and AI-driven analytics for smarter business decisions.

  • 1 year of full access
  • 31 LiveLab included
  • Certificate of completion
Buy Now — $239.99 Try Free

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