Statistics

Statistics Course Overview

This Statistics course aims to address the growing need for a practical and systematic introduction to statistics at work. It is designed to give delegates a strong foundation in classical statistics, both descriptive and inferential, that will allow them not only to accurately interpret statistical results but also equip them with the requisite knowledge to design and carry out powerful statistical tests. The foundations will be laid to enable delegates to characterize the data they have, decide on the best summary and visual representations and, most appropriate tests to carry out. These are vital skills in the age of data.

Content in General

With the statistical content we cover in this course, delegates will acquire a general framework that will enable them to decide-on and perform a relatively wide range of common statistical procedures and tests on data. We will start by identifying different categories of data and the various descriptive statistical measures we can perform on them. This will be followed by inferential statistics which will include population and sampling distributions, confidence intervals and hypothesis testing. We will also cover concepts of probability, including distributions, on a just-in-time basis as required by our principal focus, statistical and inference.

Approach:

We believe in learning-by-doing, so we have taken a problem-solving approach to delivering our training. The course is broken into sessions, each centred on a few related core concepts and skills. The relevant theory is discussed at the beginning of the session, in a just-in-time approach. This is followed by an illustrative example. For the second half, which will generally be most of the session, the delegates are expected to solve relevant problems of graduated difficulty. Immediate practice helps delegates consolidate their understanding of concepts on which we build gradually. Example solutions will be available for the delegates to take away at the end of the course. We use the statistical language R and Python (with various relevant packages). So, delegates are expected to be proficient in one of the programming languages.

Course Objectives

This course aims to provide the delegate with the knowledge to be able to:

• Calculate basic descriptive statistical measures such as
  • Mean, Median, Mode
  • Variance, Standard Deviation and Quartiles
• Understand the framework of classical statistical inference
• Understand and use discrete and continuous probability distributions including
  • Binomial, Bernoulli, Poisson, Normal, t-Distribution, Chi-Square, F-Distribution
• Understand and/or calculate
  • Sampling bias and strategies
  • Population and sample distributions
  • Point estimates and confidence intervals
• Design, choose and perform a hypothesis test
• Understand and perform basic linear regression
• Produce various visual representation (or plots) of data

Who will the Course Benefit?

This course will benefit anyone who works or intends to work with data. This course also offers a solid statistical foundation to professional or aspiring Data Scientists.