Classroom/ Online: Yes/ No
Scheduling Date(s):
1) Jan 21 - 22, 2025 (classroom - confirmed)
2) Apr 14 - 15, 2025 (classroom)
3) Jul 16 - 17, 2025 (classroom)
4) Sep 04 - 05, 2025 (classroom)
5) Dec 01 - 02, 2025 (classroom)
Note: Please click specific date for detailed venue and course fee etc.
Data Analytics in the age of AI ( 2 Days)
Today’s business demands swift, informed decision-making, yet the journey from raw data to actionable insights is often hindered by scattered data and complex statistical analysis, posing challenges for those new to quantitative methods. This fragmentation not only slows decision-making but also widens the gap between analytics potential and practical application, leading to missed opportunities.
Enter AI, particularly Large Language Models (LLMs) like ChatGPT, which revolutionize data analysis by making it accessible through their natural language interface, where analysis can be done without any coding knowledge. LLMs process vast amounts of data—structured and unstructured—providing faster insights and nuanced analyses. However, effective use requires knowing what questions to ask, testing hypotheses, and interpreting outcomes.
Our workshop dismantles these barriers. Over two immersive days, you’ll master crucial analytics skills: problem definition, hypothesis development, data preparation, hypothesis testing, and result interpretation. Discover how to harness AI effectively to bridge the gap between data potential and actionable insights in today’s competitive landscape.
Enter AI, particularly Large Language Models (LLMs) like ChatGPT, which revolutionize data analysis by making it accessible through their natural language interface, where analysis can be done without any coding knowledge. LLMs process vast amounts of data—structured and unstructured—providing faster insights and nuanced analyses. However, effective use requires knowing what questions to ask, testing hypotheses, and interpreting outcomes.
Our workshop dismantles these barriers. Over two immersive days, you’ll master crucial analytics skills: problem definition, hypothesis development, data preparation, hypothesis testing, and result interpretation. Discover how to harness AI effectively to bridge the gap between data potential and actionable insights in today’s competitive landscape.
Objective
• Understand the Analytics value chain and its application in solving problems objectively and quantifiably through data.
• Define problems and formulate hypotheses for Analysis.
• Apply technical data skills to prepare data for analytics, including managing data errors, integrating multiple data tables, and creating new calculated data fields.
• Evaluate when and how to use descriptive and inferential analyses to identify pivotal observations in data.
• Select and execute appropriate hypothesis tests for specific situations.
• Demonstrate proficiency in conducting analyses using MS Excel and ChatGPT.
• Recognize the limitations of descriptive and inferential analyses and analyse their implications for supporting decision-making.
• Define problems and formulate hypotheses for Analysis.
• Apply technical data skills to prepare data for analytics, including managing data errors, integrating multiple data tables, and creating new calculated data fields.
• Evaluate when and how to use descriptive and inferential analyses to identify pivotal observations in data.
• Select and execute appropriate hypothesis tests for specific situations.
• Demonstrate proficiency in conducting analyses using MS Excel and ChatGPT.
• Recognize the limitations of descriptive and inferential analyses and analyse their implications for supporting decision-making.
Outline
Day 1
• What is Analytics?
o Formally defines that data analytics is
o Appreciate why data analytics matters in today world and what it takes to build sustainable value
• An introduction to the Analytics value chain
o Understand how data creates value for organizations through data analytics, including a feature on the role that AI plays in it
o An introduction to the processes and capabilities required to deliver value through analytics; and the role that Data Analysis plays in the process
• Data Analytics doesn’t start with data, but with the right question
o Hands-on exercise – Problem definition and hypothesis development exercise
• Data Management: Preparation and Transformation
o With the problem defined and hypotheses identified, the data requirements can be drawn up; but data may not be ready for analysis
o A hands-on session with multiple computer-aided exercises on a wide range of data problems. Attendees will get to prepare sizable data sets for data analytics, which include
▪ Merging multiple data tables
▪ Finding and addressing data errors
▪ Understanding the use of system vs calculated data fields
▪ Includes the use and application of several key functions and tools within MS Excel as well as demonstration of how the same processes can be accomplished through ChatGPT
• Descriptive Statistics – Understanding your data
o Attendees will be introduced to statistics concepts to objectively and accurately describe data. A hands-on session with multiple computeraided exercises to build the requisite skills
▪ Data types, data errors, blanks and their various implications
▪ Describing data – Central tendency & Variation
▪ Interacting with and finding answers in Data using Pivot Tables in Excel as well as ChatGPT
Day 2
• Data visualization as another form of Descriptive Analysis
o Statistics is not everyone’s cup of tea; but visualizations can make data insights more accessible to most people
o This computer-aided session builds the key skills to design and build interactive dashboards with MS Excel as well as ChatGPT
▪ Principles of data visualization
▪ Design and build interactive data visualizations
• Inferential Analysis – distinguishing real vs coincidental relationships in data
o An introduction to Sampling, Probability and Hypothesis Testing
o Learn how to identify the right test for each hypothesis
o Attendees will try their hands on case example using MS Excel and ChatGPT
o Appreciating the limitations and capabilities of inferential analysis
• Group Exercise: Each group will be given a new data set and problem statement where they will need to conduct analysis to identify the likely issue and develop a recommendation based in the data insights in class.
• Data analytics in the Age of AI
o A brief history of AI, from Machine Learning to Generative AI
o Appreciate how LLMs really work and their limitations
o Understand when and how to deploy analytics for optimal outcomes
• Wrap up and Q&A
• What is Analytics?
o Formally defines that data analytics is
o Appreciate why data analytics matters in today world and what it takes to build sustainable value
• An introduction to the Analytics value chain
o Understand how data creates value for organizations through data analytics, including a feature on the role that AI plays in it
o An introduction to the processes and capabilities required to deliver value through analytics; and the role that Data Analysis plays in the process
• Data Analytics doesn’t start with data, but with the right question
o Hands-on exercise – Problem definition and hypothesis development exercise
• Data Management: Preparation and Transformation
o With the problem defined and hypotheses identified, the data requirements can be drawn up; but data may not be ready for analysis
o A hands-on session with multiple computer-aided exercises on a wide range of data problems. Attendees will get to prepare sizable data sets for data analytics, which include
▪ Merging multiple data tables
▪ Finding and addressing data errors
▪ Understanding the use of system vs calculated data fields
▪ Includes the use and application of several key functions and tools within MS Excel as well as demonstration of how the same processes can be accomplished through ChatGPT
• Descriptive Statistics – Understanding your data
o Attendees will be introduced to statistics concepts to objectively and accurately describe data. A hands-on session with multiple computeraided exercises to build the requisite skills
▪ Data types, data errors, blanks and their various implications
▪ Describing data – Central tendency & Variation
▪ Interacting with and finding answers in Data using Pivot Tables in Excel as well as ChatGPT
Day 2
• Data visualization as another form of Descriptive Analysis
o Statistics is not everyone’s cup of tea; but visualizations can make data insights more accessible to most people
o This computer-aided session builds the key skills to design and build interactive dashboards with MS Excel as well as ChatGPT
▪ Principles of data visualization
▪ Design and build interactive data visualizations
• Inferential Analysis – distinguishing real vs coincidental relationships in data
o An introduction to Sampling, Probability and Hypothesis Testing
o Learn how to identify the right test for each hypothesis
o Attendees will try their hands on case example using MS Excel and ChatGPT
o Appreciating the limitations and capabilities of inferential analysis
• Group Exercise: Each group will be given a new data set and problem statement where they will need to conduct analysis to identify the likely issue and develop a recommendation based in the data insights in class.
• Data analytics in the Age of AI
o A brief history of AI, from Machine Learning to Generative AI
o Appreciate how LLMs really work and their limitations
o Understand when and how to deploy analytics for optimal outcomes
• Wrap up and Q&A
Who should attend
• Managers, Analysts or Professionals with responsibilities in Data Analysis
• Managers, Analysts or Professionals with Analytics responsibilities who want to go beyond descriptive analyses to learn inferential and predictive analytics skills
• Managers responsible for managing and supervising a team of analysts
• All other professionals with a keen interest in developing data analytics skills
Logistics Requirements
Participants are required to bring their own internet accessible laptops with MS Excel and Powerpoint (version 2013 or later) to participate in the data exercises. The same laptops should have the ability to access ChatGPT to participate in the AI elements of the workshop.
• Managers, Analysts or Professionals with Analytics responsibilities who want to go beyond descriptive analyses to learn inferential and predictive analytics skills
• Managers responsible for managing and supervising a team of analysts
• All other professionals with a keen interest in developing data analytics skills
Logistics Requirements
Participants are required to bring their own internet accessible laptops with MS Excel and Powerpoint (version 2013 or later) to participate in the data exercises. The same laptops should have the ability to access ChatGPT to participate in the AI elements of the workshop.
Profile of Derrick Yuen
MBA, BEng, Founder and Principal Consultant
For more than a decade, Derrick has led engagements to help clients liberate their data for value creation; be it through workshops, consulting or data solutions. His engagements span multiple functions, sectors and geographies; including Pharmaceutical, Engineering, FMCG, F&B, Government, HR , & Finance across Asia Pacific
In a prior life, Derrick led the formation of the Global People Analytics Centre of Excellence (COE) for a MNC with >US$30 Billion revenue and helped several FYT clients established their own data analytics functions.
