Training on Effective Data Analysis Techniques




15th to 19th Jan 2024


12th to16th Feb 2024


11th to 15th March 2024


8th to 12th April 2024


13th to 17th May 2024


1oth to 14th June 2024


15th to 20th July 2024


12th to 16th Aug 2024


9th to 13th Sept 2024


14th to 18th Oct' 2024


11th to 16th Nov' 2024


9th to 13th Dec 2024


Data analysis is very important in every business. Many processes can be enhanced, and many risks can be avoided by simply correctly analyze the available data with the most suitable tool. Training on effective data analysis tools helps in analyzing data for making meaningful business decisions, improve efficiency and profits.
Appropriate interpretation of numerical data will strengthen the ability to make a significant impact in providing, using or evaluating data for improved business performance.

Training Objectives

  • Understand some of the key terminology used in statistical analysis
  • Understand the meaning of variable data, and the difference between common and special causes of variation
  • Explain and give examples of the data validation, grouping and graphing
  • Explain what is meant by dashboards
  • Explain what is meant by qualitative data analysis
  • Understand the KPI concepts
  • Understand the benefits of control charts
  • Summarise the five stages of DMAIC


  • Engineers, technologists, and professionals whose jobs involve the manipulation, representation, interpretation and analysis of data
  • Professionals who need to use data analysis in their job role, including strategists, business analysts, etc.
  • Professionals who wish to gain knowledge of Data Analysis in order to improve their analytical skills and understanding of data
  • Personnel moving into roles where they will need to produce data and/or use data to make decisions
  • All professionals & leaders who need to have in-depth knowledge of data interpretation and finding trends in data.
  • Key data officers who are interested in maintaining organization data and enhance the business processes through data analysis.


Module 1 -Introduction, Overview and Data Preparation

Introduction and overview

  • Introductions
  • Overview of contents
  • The need and the process of data analysis

How to prepare Data before analysis?

  • Ensuring that data conforms to quality criteria, e.g. ensuring uniformity of measurement, removal of repeated data, etc.
  • Checklist for ensuring that the data is “clean”
  • Data Sampling
  • Data sorting and filtering

Module 2 - Variance, Standard Deviation and Graphs

Variance and standard deviation manually and using Excel

  • How to calculate variance and standard deviation manually
  • How to calculate variance and standard deviation in Excel
  • Other basic and important statsitics like mode, median, etc

Creating and using graphs

  • Histogram/ Pareto Analysis
  • Scatter plot
  • Normal probability plot
  • Maximum-minimum-average chart

Module 3 -Control Charts and Data Representation

Control charts for variable data

  • X bar and R
  • X bar and S
  • Individuals and moving range chart
  • Explanation of the median and range
  • The importance of distinguishing between causes of variation
  • Examples of common cause and special cause variation

Advanced Charts and data visualization

  • X-Y charts
  • Bar and Pie charts
  • Examples of common cause and special cause variation

Module 4 - Data Datamining Techniques & Regression Analysis

Introduction to Datamining Techniques

  • The association pattern analysis
  • Clustering
  • Classification
  • Decision Tree analysis
  • KNN method to visually classify new data

Simple Regression Analysis

  • What is regression analysis and what is it used for?
  • Regression analysis examples

Module 5 -Usage of Tables, Reports and Process Improvements

Cross Tables, Pivot Tables, and Chi Test

  • Pivot Tables and Pivot Charts
  • Chi-test – calculation, and interpretation
  • Dashboards
  • Slicers
  • Timeline

Define, Measure, Analyze, Improve, Control

  • An introduction to DMAIC
  • Fishbone Diagram (FBD)
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