Descriptive Statistics

Track: Associate Enterprise Data Analyst (AEDA)
The Descriptive Statistics module empowers students to use data for effective decision-making and measuring business impact. With the overwhelming volume of data available to organizations and businesses, it is important to correctly transform data to information, then knowledge and finally wisdom.

Gain Insight From Raw Data Using Descriptive Statistics

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Descriptive Statistics teaches students to collect sample data, engage in exploratory data analysis, compare statistical distributions, and to communicate statistical results both correctly and effectively. In this module, students will learn to collect, summarize, visualize and explore raw data to transform it into actionable business insights. The skills of this module are the standard for business data analytics.

Learning outcome:

Upon completion, participants should be able to demonstrate each of the following;
  1. Sample data using an appropriate sampling method
  2. Identify basic properties of common statistical distributions
  3. Describe and visualize variables and their relationships in a data sample

Who should attend:

Analysts and business professionals who want to jump start their abilities in analyzing data

2 days of in depth learning

Face to face with experienced Data Scientist.

Course Methodology

This course will utilize a combination of Lecture and Hands-on Lab sessions.

Pre-requisite:
N/A

HRDF Claimable

HDRF claimable. EPP option available for selected credit card.

CADS Certification​

Earn certification upon completion.

Minimum Qualification:
Undergraduate Degree

Training Track

Associate Enterprise Data Analyst (AEDA)

Descriptive Statistics is one of the modules under our Associate Enterprise Data Analyst (AEDA) programme. AEDA is a 15- day training program that provides analysts with the tools required for efficient Data Analysis.

Details of Subject

Day 1
  1. Exploratory Data Analysis –  Participants will learn the statistical concepts they need to explore, understand, summarize and visualize data. Students will be able to use information derived from quantifying and visualizing distributions and relationships in data to generate business insights. They will be able to understand and analyze business objects and their impacts on business key performance indicators (KPIs).
Day 2
  1. Statistical Distributions –  Participants will learn about various statistical distributions and their differences. Students will be able to use statistical distributions to compare data variables and make predictions in business use cases where distribution parameters are known. Statistical distributions help business analysts to understand and estimate business objects.
  2. Sampling –  Participants will learn how to select representative samples from a population as well as methods to design sampling methods to generate reliable statistical estimates. Students will learn how to generate reliable datasets from business objects to be used for further analysis in business.

Lead Instructor

Laleh Asadzadeh Esafahani
Laleh received her MSc. in Computer Science in 2016 from Southern Illinois University. Her research focused on the modelling and analysis of social network users’ activities. Laleh then was a data scientist at Potentia Analytics Inc. and was in charge of developing and implementing several research projects that enhance the quality of service in hospitals. Before that, she received her MSc. In Mathematics in 2001 from Sharif University of Technology. Her Masters thesis was on defining sets in combinatorial structures and their applications in cryptography. After graduation, Laleh was a Mathematics instructor, researcher, and research mentor at Isfahan Mathematics House. She specializes in Mathematical Modelling, Statistical Analysis, Machine Learning, and programming languages, such as Python and R.

CADS Certification

EDP CADS Certified Descriptive Statistics

Descriptive Statistics takes data in samples and evaluates data-driven decisions. This certification shows mastery of sampling, distributions, relational variables, and various graphical representations. Do keep in mind that the test is in R code. If you work from clear data-driven insights instead of assumptions, get certified.

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Descriptive Statistics