Excel Analytics

Track: Enterprise Data Practitioner (EDP)
Excel for Data Analytics introduces participants with little-to-no statistical or software expertise to basic statistical and visualisation tools using the intuitive and popular features in Microsoft Excel, such as Functions, Charts and PivotTables.

Use Excel to Make The Most of Your Data

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Using Microsoft Excel, participants will learn the various powerful features of spreadsheets before using them in combination with statistical techniques to explore and discover relationships present in their data. This module will enable professionals looking to unlock latent value in their data and make the most of a powerful, familiar and intuitive software package.

Learning outcome:

Upon completion, participants should be able to demonstrate each of the following;
  1. Use PivotTables and Functions effectively to summarise and augment Data in Microsoft Excel
  2. Visualize Data & create dashboards for reporting with Microsoft Excel
  3. Assess Data distributions and statistical relationships with Microsoft Excel

Who should attend:

Professionals that work with Data & Excel

3 days of in depth learning

Face to face with experienced Data Scientist.

Course Methodology

This course will utilize a combination of Lectures and Workshops.

CADS Certification​

Earn certification upon completion.

Minimum Qualification:
Undergraduate Degree

Training Track

Enterprise Data Practitioner (EDP)

Excel Analytics is one of the modules under our Enterprise Data Practitioner (EDP) programme. EDP is a nine-day training program that super-charges Business Intelligence analysts with new skills to analyse and communicate insights effectively.

Details of Subject

Day 1
  1. Spreadsheet Fundamentals –  All students start with basics like Navigation, The Tool Bar, and Comparison Operators. References and Data types prepare students use Excel like a pro.
  2. Predefined Functions –  Functions are a powerful tool to simplify operations in Excel. This section will introduce time-saving functions such as VLOOKUP, INDEX MATCH, COUNTIF.
  3. Normalisation –  Many of us work with Data, but good analysis begins with an organized dataset. Participants learn how to organize spreadsheets and lay the foundation for easier Data analysis.
  4. Pivot Tables –  Pivot Tables are an important function for aggregating large amounts of data at once. We use Pivot Tables to quickly understand large datasets but also as a pre-cursor to Data Visualisation techniques in Excel.
  5. Data Visualization in Excel –  We can use Visualization techniques to quickly understand large Data at a glance. Learn how to create and use sparklines, boxplots and line graphs for analysis towards decision- making.
  6. Dashboarding –  Dashboards are a versatile tool frequently used in the realm of Business Intelligence. Participants create strategic dashboards for decision-makers to track KPI’s at a glance.
Day 2
  1. Data Profiling –  The first task for many Data analysts when approaching a new dataset is to perform data profiling. Understanding where the center of the Data lies (mean, median, mode) and how spread out the Data is (quartiles, std. dev., variance) provides insights into the kind of analytical techniques that are effective for that particular dataset. This saves time, energy, and resources.
Day 3
  1. Categorical Explanatory and Response Variables –  Moving beyond single variable students move on to Cross Tabulations and Conditional Percentages.
  2. Categorical Explanatory and Quantitative Response Variables –  This chapter examines distributions of different subgroups. For example, which products are performing better in the market? How are the sales of each product affected by the customer quality rating? We will use simple statistical techniques to answer these questions.
  3. Quantitative Explanatory and Quantitative Response Variables –  Examine linear relationships through the Pearson Correlation Coefficient via a combination of scatterplots and Excel formulae. Correlation is not causation but can often hint at it providing a starting point for further investigation.

Lead Instructor

Dr. Vinod A. R. Ramachandran
Dr. Vinod Ramachandran is a certified Data Science specialist with 10 years of Data Modelling experience. His expertise also covers Statistics and Computational Intelligence where he researched the implementation of computational algorithms and techniques in the medical field (A full list of his publications is available at www.goo.gl/S2CC2i). On top of that, Dr. Vinod has also been granted a patent for his work on applying Genetic Algorithms in modelling Epileptic Seizures. Currently, his interests revolve around not only Data Science but also Geometric Modelling and Topological Data analysis.

CADS Certification

EDP CADS Certified Excel Analytics

Excel is everywhere and often used for quick descriptive and diagnostic analyses. This certification test covers the following topics: Pivot Tables, Dashboards, Analytical Functions, Distributions, and Statistical Relationships. If you know the ins and outs of using Excel for analytics, stand out from the crowd with a certification that will attract the right attention.

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Excel Analytics