* All fees are exclusive of vat
** PREMIUM - Customize your learning experience
Many organizations are working on big data to ensure business performance and increased productivity are apparent for the streamlining of business operations. This is vital in the business environment today as data increases and organizations are finding it cumbersome to manage it manually. To increase the awareness of how Big Data could be analyzed and effectively implemented, this training workshop looks at the several aspects of Big Data and its business impact and significance in measuring business success and performance
What Do Participants Learn?
The impact of Big Data in the business environment today and why the need for it?
Fact-based decision-making models in the business environment
Understand the challenges companies face in optimizing their data and consolidating it
How to apply the five methods to use data visualization techniques
How to demonstrate using Charts and Graphs to communicate Big Data analysis to both internal and external players in my organization for a clearer understanding
Who Should Attend?
CEOs and Managers,
Financial and Data Analysts,
Sales and Administration Managers,
Systems Analysts and Engineers
What Will the Learning Experience Include?
Comprehensive pre-program activities include:
Web-based information forms & surveys completed by attendee.
Direct consultation with the attendee about the expectations.
During the training, participants engage in data, activities, and conversations that lead to insight and knowledge.
Participants learn from expert trainers who have both academic and business experiences.
Highly applicable training content & instructive activities for adding depth to training topics.
**A half-day site visit for integrating the experience & plan next steps. Opportunities to provide connections, ideas & support.
Explore & Practice
Apply & sustain the learning experience by using this ongoing support:
To ensure participant has new skills or behavior progress.
Optional, fee-based mentoring & coaching with the trainer.
Training materials & additional documents (e-books, pdf files, presentations and articles)
Evaluate your training experience by giving us feedbacks and help us to reach our organizational goals.
DAY 1: 9:00AM – 5:00PM
What is Business Intelligence and
Definitions of Business
History of Business Intelligence
How is Business Intelligence is
used to help businesses to monitor growth and performance
Definition of Data Analytics
Relationship between Business
Intelligence and Data Analytics
Data Here, There, and Everywhere!
Oracle study on business data
Overview of Study
Findings-overwhelmed by volume of data and inability to utilize data effectively
Solutions to data overflow
Got Data? The Unique Role of the
Role of a Data Analyst
Skillsets required to be an
effective Data Analyst
Channeling Your Inner Analyst - Participants are told to imagine receiving a memo from their
supervisor explaining that the company is downsizing. They are expected to take
on additional responsibilities including doing data analysis. They must rewrite
their current job description to include the new data analyst duties.
Facts or Feelings: Your Choice
- As data becomes more widely available, businesses are finding more
success in adopting a fact-based decision model rather than relying on
traditional intuition alone. In this module, we examine more closely the two
types of decision modeling businesses use as well as the benefits of the
fact-based model. We cover the steps of the Rational Decision
Model, a fact-based method for
Fact-Based Decision-Making Process
The two types of Decision Models
The Benefits of Fact-Based
Rational Decision Model: Six- Step
Pal's Diner: An Example of how the
Rational Model is used in practice
Exercise: Who’s the Boss? – Participants
are divided into groups; Imagine that they are the CEO of their own company.
They define a business-related decision that they need to make and then apply
the steps of the Rational Decision Model to arrive at the conclusion.
Big Data Anatomy - In this
module, we visit the Big Data trend with a more detailed focus. We begin by
defining the buzz word-"BIG DATA", examining its core attributes, and
outlining the factors that contribute to data being 'big'. We explore how
businesses collect structured and unstructured data, and the challenges they
face in storing and effectively using both types of data.
Day 2: 9.00am - 5.00pm
Big Data Anatomy
The Attributes of Big Data
Definition of Big Data
The 4 V's of Big Data
Structured versus Unstructured
The Challenges of Big Data
Exercise: Camp Data –
Participants are asked to describe some of the big data challenges that their
companies face and to outline what steps are being taken to address the
Getting to Know Your Data -
To better understand how to analyze data, we must first comprehend its depth.
This requires drilling deep beneath the server it is located on and understanding
its composition. Assume we are given a structured data set with labeled
columns and completed rows. There are plenty of ways to summarize the story
behind the data, but we cannot dive in without first getting to understand its
We begin by classifying the
collected data as quantitative or qualitative. Then we further classify our
column variables according to the way data is measured: nominal, ordinal, interval,
or ratio. It is only after understanding this classification that we are able
to proceed to the next step of choosing the appropriate analysis techniques
which correspond to nominal, ordinal, interval or ratio variables.
Getting to Know Your Data
Data Types: Qualitative versus
Taking a Closer Look: Data
Four Types of Data Variables:
Definition and examples of Nominal
Variables: Name only
Definition and examples of Ordinal
Variables: Order Matters
Definition and examples of
Definition and examples of Ratio
Summary of Statistics/Operations
that can be performed on each type
Exercise: Marketing to Low
Renters – Participants are told to put on their data analyst thinking caps.
They have been employed as a junior data analyst for a Marketing Company whose
goal is to make a marketing campaign for a client who plans on targeting the
Participants are given a public
housing data set and told to classify each variable according to its measurement.
Data Visualization - A
picture is worth a thousand words, and there is definitely no exception when it
comes to summarizing data. This module is dedicated to highlighting the
importance of visualizing data, and how the human eye depends on visual
representation to get a quick sense of data relevance. Visual representation is
the audience's first impression of the data and forms a crucial step in
inviting and maintaining a genuine interest in a subject matter. We demonstrate
how to create colorful, easy to understand tables, charts, and graphs that aid
in helping us convey the story behind the data set being analyzed.
The Fundamental Ways we use data
The five ways we use data
Displaying Tabular Data in Excel
How to create custom tables in
How to Sort/Filter tabular data
How to create and manipulate pivot
Using Charts and Graphs to
How to create Pie, Column, and
Line charts using Excel
Communicating effectively using
different chart types
How to choose the correct chart to
display the correct data type
Exercise: Table Mining –
Participants develop tables to summarize trends in a data set related to low
Exercise: Charting Poverty -
Participants develop charts and graphs to summarize the poor
housing epidemic in a public
housing data set.
Numerical Data Summaries -
Another way that data analysts summarize data is by providing a single number
or summary statistic, that has meaning. This module explores how the mean, median,
and mode can be used to summarize the centre of discrete and continuous grouped
data. The range, standard deviation, and inter-quartile range measure the
dispersion in the data set and provide information about how data points are
Origin of Probability
Probability: Examples of Business
Traditional definition of Probability
Simple Computation: The Top Bottom
How to calculate probabilities
from contingency tables
How to Calculate conditional
probability from contingency tables
Applying probability to calculate
Applying probability to calculate
Using Expected Value in Decision
Exercise: Pocket Probability -
Participants practice calculating basic probabilities using the change in their
Correlation and Regression
Definition of Correlation and
Relationship between Correlation
Correlation Coefficient: Values
Examples of Correlation
Interpretation of a Regression
Exercise: Paid Sickouts -
Participants use correlation and regression to help a company determine the
relationship between the number of sick days’ employees took and the wages they