Statistical Data Analysis

Track: Enterprise Data Analyst (EDA)
With the overwhelming volume of data available to organizations and businesses, it is important to correctly interpret its implications and have the ability to sort through all this information.

Communicate Statistical Results Effectively

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This course will equip participants with the knowledge to develop data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, data wrangle and data visualization.

Learning outcome:

Upon completion, participants should be able to demonstrate each of the following;
  1. Comprehend the principles, techniques, and practices relevant to Statistics
  2. Conduct Hypothesis Testing

Who should attend:

Professionals that work with data

3 days of in depth learning

Face to face with experienced Data Scientist.

Course Methodology

This course will utilize a combination of Presentations and Workshops.

Pre-requisite:
Python Programming II or R Programming II

HRDF Claimable

HDRF claimable. EPP option available for selected credit card.

CADS Certification​

Earn certification upon completion.

Minimum Qualification:
Undergraduate Degree

Training Track

Enterprise Data Analyst (EDA)

Statistical Data Analysis is one of the modules under our Enterprise Data Analyst (EDA) program. EDA is a 28-day training program that provides analysts with the tools to be immediate contributors to a data science team. They will assist to frame business requirements as analytics models.

Details of Subject

Day 1
  1. Introduction to Probability and statistics –
    • Expectation, variance, quantiles
    • Sampling
    • Simulation
    • Distribution
    • Probability distribution function
Day 2
  1. Main distributions (incl. what are they used for) –
    • Discrete/continuous distributions
    • Binomial, geometric and Poisson distribution
    • Exponential and normal distribution
    • Central limit theorem
Day 3
  1. Hypothesis/statistical testing –
    • Test of hypothesis on one and two population parameters
    • Chi-square test of independence
    • Type I and type II error

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

EDA CADS Certified Enterprise Data Analyst

Certification information for this module & track will be made available soon.

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Statistical Data Analysis