Data Analysis For The Big Data

Course Intorduction

In a world that’s full of data, we have many questions: we assume that data conform to the normal distribution but it that always true and why does that matter? How are our sales growing: in a straight line, in a seasonal manner or in a rapid, non linear way? We have so much data that we can’t see the wood for the trees … now what?

The world is also full of data to help us answer those questions. This course will walk through the basics of statistical thinking: starting with an interesting question. Then, we’ll learn the correct statistical tool to help answer our question of interest – using Microsoft Excel and hands on Cases. Finally, we’ll learn how to interpret our findings and develop meaningful conclusions.

This course introductions to each major sub topic that will be followed by some guided questions to help your understanding of the topic which will be integrated with/followed by Excel demonstrations, which we’ll then apply using real world datasets. We’ll cover Descriptive Statistics as our first topic learning about visualising and summarising data. T opic two will be a Modelling Investigation where we’ll discuss linear, exponential and logistic growth functions. Finally in the third Unit, we’ll learn about Inferential Statistical T ests such as the t test, analysis of variance (ANOVA) and Chi square.

This course is designed to be sequential, with each new topic building on the previous topics. Once completed, delegates will feel comfortable using basic statistical techniques to answer their own questions about their own data, using Microsoft Excel.

Learning Outcomes

By the end of this course, delegates will be able to:

  • Answer such questions as why we might study statistics, what are variables, and what is data.
  • Appreciate the meaning and application of univariate descriptive statistics.
  • Extend their knowledge of bivariate distributions by using the scatterplot and correlation analysis.
  • Apply and evaluate the techniques required to analyze categorical bivariate distributions using such techniques as contingency tables, conditional probability, and by examining independence.
  • Review the need for and evaluate linear functions including regression analysis.
  • Consider exponential and logistic functions as they apply to nonlinear data.
  • Enhance their understanding of sampling in data analysis.
  • Carry out hypothesis testing on one and two group means on categorical data and using more than two group means.
  • Consider the nature and use of the analysis of variance, ANOVA table.

Who Should Attend

FINANCIAL ACCOUNTANTS

FINANCE STAFF

MANAGEMENT ACCOUNTANTS

COMMERCIAL BANKERS

CORPORATE FINANCIERS

BUSINESS ANALYSTS

INVESTMENT BANKERS

FINANCIAL ANALYSTS

FINANCIAL CONTROLLERS

STATISTICIANS

SALES AND MARKETING STAFF

PRODUCTION MANAGERS

AUDITORS

PROFESSORS AND OTHER TEACHERS