Data Analytics Foundation & Advanced

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Course Fees: Full Course Fee: €2695

Network Members Fee: €1610

Course Objectives/Learning Outcomes

At the end of the course participants will know what the data analytics methodology is.

At the end of the course participants will have a fundamental understanding of how to use the various tools listed below. Even though we will practice as much as possible, it is advised that the participants keep practicing.

The knowledge acquired will provide an understanding of how Data Analysis can be integrated into the company’s reporting and decision
structure.

Who Should Attend

This course is recommended for all those in an organisation who want to get a good understanding of Data Analysis, the methodology, the main tools and tests and when to use them and when not.
In addition, we will spend some time looking at various common errors you must be aware to avoid wrong results.

Course Content

Introductory Section – first 2 days of the course
Day 1

Data Analysis Methodology – CRISP-DM (Cross-Industry Standard
Process for Data Mining)

  • Business Understanding
  • Data understanding
  • Data Preparation
  • Modeling
  • Evaluation

Deployment
Introduction to Minitab and Excel (History in Minitab & Change settings
in Excel)

Distributions

  • Types of data: Continuous, Attribute (Ordinal, Discrete and
    categorical)
  • Normal Distribution
  • Binomial distribution
  • Poisson distribution

Day 2
Hypothesis testing – Part I

When you want to compare averages or medians of some sample of
data to decide if they are statistically different.

When you want to compare the standard deviation of some sample of
data to decide if their variation is statistically different.

When you want to compare proportions or percentages that came
from different samples of data to decide if they are statistically
different.

  • Principles of Hypothesis testing: What is it and why and when
    do we use hypothesis testing
  • 1 Sample T-Test: for comparing the averages of one sample
    against a specific target or historical average
  • 2 Sample T-Test: for comparing the averages of two samples
    against each other.
  • One way ANOVA: for comparing the averages of 3 or more
    samples against each other
  • Pared T-Test: for comparing the averages of two samples that
    contain data that is linked in pairs.

Exploratory data analysis via graphical tools

  • Time series
  • Scatter
  • Pareto
  • Box and Whiskers
  • Advanced Section – Continued for another 2 days

Days 3 & 4

Measurement System Analysis (MSA)

This is a technique for understanding the quality of data by challenging
its sources and their potential errors.

  • Gage R&R (Repeatability and Reproducibility) study for
    continuous data
  • Attribute R&R study for attribute data

Hypothesis Testing – Part II

What is it and why and when do we use hypothesis testing

Levenes Test: for comparing the standard deviations of 2 or more
samples that are not normally distributed

F-Test: for comparing the standard deviations of 2 samples that are
normally distributed

Bartletts Test: for comparing the standard deviations of 3 or more
samples that are normally distributed.

1 Proportion Test: for comparing a proportion against a specific target
or historical proportion

2 Proportions Test: for comparing 2 proportions against each other

Chi-Square Test: for comparing 3 or more proportions against each
other.

Graphical Tools – Part II

Histogram: The data is summarised into bars with the most frequent
values being represented by the higher bars. The overall shape of the
distribution can be assessed.

Probability Plot: used to decide if a sample data fits a specific
distribution

Matrix plot: produces and array of scatter plots

Box Plot: Shows the distribution of a sample data as a box and whiskers
Individual value Plot: for comparing the distribution of several samples
against each other

Individual value Plot: for comparing the distribution of several samples
against each other

Fitted line Plot: is a scatter plot in which the relationship between the
input and the output is represented mathematically by a single line (a
regression line) can be linear or curved.

Statistical Process Control (SPC)

I-MR chart for analysing individual data points of continuous data

U- Chart: for analysing the count or defects per unit

Xbar R Chart: for analysing the averages of small sub-groups (2 to 5)

P chart: for analysing the proportions or percentages

Xbar S Chart: for analysing the averages of large sub-groups (more than
6)

Regression

  • Simple regression: model the relationship between one X
    variable and a response variable Y
  • Multiple Regression: to model the relationship between two to
    five X variables and a response variable Y
  • Optimise response: using multiple regression to model the
    relationship between two to five X variables and a response
    variable Y and identify X values that optimise Y.

Capability Analysis

  • Capability Analysis: determine whether the process is capable
    of producing output that meets customer requirements.
  • Binomial Capability: determine whether the % defective meets
    customer requirements
  • Poison Capability: determine whether the defect rate (DPU)
    meets customer requirements

Trainer Profile

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