Data Analytics A Practical Guide To Data Analytics For Business, Beginner To Exper

51 Pages·2017·0.42 MB·English

by  James Fahl

Data Analysis
Table of Contents
Chapter 1 what is data analysis?
Chapter 2 Why use data analysis?
Chapter 3 The significance of data analysis for your business:
Chapter 4 Types of and explanation of data analytics
Descriptive analytics
Diagnostic analytics
Predictive analytics
Prescriptive analytics
Chapter 5 Collecting Data
Chapter 6 Mistakes to avoid
Conclusion
Introduction

I want to congratulate you and thank you that you have downloaded this book.
Data analysis is the most common buzzword in the world of internet today and it
is very difficult to understand what it is. There are so many definition of data
analysis. The most important definition is data analysis is the procedure in which
data is transferred, modeled, cleared and checked by business and the ultimate
goal is to use in decision making procedure.
The fields of data analysis and predictive analytics are so vast and have so many
sub-branches, which are even more extensive. One of these branches is
prescriptive analysis, which I will briefly cover in this Book. I have covered only
the fundamentals of these fields in this book. This method is being used by large
industries to determine what will happen in the future, and they use the
information to prevent or make things happen in the future.
I think you will enjoy this book. It will motivate you to manage your data and
execute predictive analysis to get maximum profits.
Thank you again as you have download this book, I think you like it.
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Chapter 1 what is data analysis?

In data analysis procedure raw data is collected and transformed into info.
Stakeholder can use this for making good decision
Stages Involved Into Data Analysis
Below stages are common for all companies though data needs might not be
similar for each firm.

Phase 1: Choose on the Objective
In this process you have to set clear objective which should be precise and
measurable which is in the shape of question. For example owing to competitors’
product it is difficult for your company’s product to get off shelves. The queries
that you might enquire are, “Is my product high-priced?” “What is exclusive
about the contestant’s product?” “What is the target listeners for the contestant’s
product?” “Is my technology or process redundant?”
Why is enquiring these queries upfront significant? This is since your data
assemblage depends on the kinds of queries you ask. For example, if your
question is, “What is exclusive about the contestant’s product?” you would have
to do an analysis on the spec of the product and collect feedback from the
customers about what they like around the product. Alternatively, if your
question is, “Is my procedure or technology redundant?” you will need to as
conduct a survey near the technology used by others in the similar industry and
perform an audit of the current processes and skills used at your establishment,
also. Notably, the kind of questions you ask will affect the nature of data
collected. Given that data investigation is a tedious procedure, it is essential that
you do not waste the time of your data science team in gathering useless data.
Ask your queries right!
Phase 2: Set Measurement Priority
You need to establish measurement priority next As you have decided on your
objectives. This is done over the following two phases:
Choose on whatever to measure
You have to decide what type of data you have to answer your query. For instance, if your query pertains to
dropping the number of works without compromising the excellence of your product otherwise service, then
the data that you requisite in hand right away are as follows:
- The price of employing the current number of team members

The number of workforce members hired by the company
-
- The proportion of time and efforts spent by the present workforce

members on current processes

When you have the above data, you would have to ask other queries ancillary to the main question, for
example, “Is there any procedure that can be altered toward improve the efficiency of the staff?” “Are my
staff not being used to their completest potential?” “Will the company meet increased demands in the future
in spite of the reducing of manpower?”
The data collected in association with these auxiliary questions will aid you make better verdicts. These
ancillary questions are as significant as the main objective question.
Choose on how to measure
Decide on the parameters that will be used to measure data before you start collecting it is highly important.
This is because in analyzing the collected data how you measure your data plays an important role in the
advanced stages. At this stage some of the questions you require to ask yourself are as follows:
- What is the unit of measure? For instance, you need to reach at the base

price using a definite currency if your product has worldwide markets and
you are obligatory to determine the pricing of your product, as well as then
extrapolate it hence. In this case, selecting that base money is the solution.
- What is the time frame of completing the analysis?

- What are the factors that you requisite to include? This might again

depend on the query you have asked in stage 1. You need to decide on what
issues you need to take into consideration through respect to the price of
employment In the case of the staff reducing question. Whether you will be
taking the gross salary package into consideration otherwise the net yearly
salary drawn by the worker, you need to decide on.
Phase 3: Gathering of Data
it will be easier for you toward collect data in a phased way as you have already
set your priority plus measurement parameters,. Here are a few pointers that you
requisite to bear in mind before you gather data:

we saw the diverse sources of data in the preceding chapter. Take stock
of the data accessible before you collect data. For example, you can just stare
at the payroll to know the amount of employees available in the case of the
reducing of the workforce question. This might save you the time necessary
for collecting this specific data again. Similarly, all accessible info need to be
organized.

spend a good amount of time in determining on the questions that you
want toward ask if you intend to collect info from external sources in the
shape of a questionnaire. You should you circulate it simply when you are
pleased with the questionnaire plus believe that it serves your main
objective. You will have varied data in hand which would not be possible to
compare if you keep circulating diverse questionnaires.

you have to Ensure that you have appropriate logs when you enter the
data collected. This might help you evaluate the trends in the market. For
example, assume you are conducting a survey concerning your product over a
time of two months. The shopping habits of people change drastically
throughout holiday seasons, more than any other time of the year. You will
end up with surplus figures, when you do not comprise the date plus time in
your data record, and this will influence your decisions considerably.

For the purpose of data gathering Check the budget assigned. You will be
able to recognize the approaches of data collection that are cost effective
Based on the existing budget. For instance, if you have a tight budget, you
can opt for free online survey tools as opposite to the published
questionnaires that are involved in the packages. Similarly, to conduct mini
surveys and collect the data required you can make the finest use of social
networking sites. You can go for printed and smart questionnaires that can be
distributed along with the package otherwise that can be circulated at retail
outlets, if you have a sufficient budget. While marketing your product in one
go you can also organize competitions to collect data. For the clienteles to
drop these filled out surveys you can set up drop boxes at adjacent cafes and
malls.
Phase 4: Data Cleaning
Data cleaning is crucial in this procedure in terms of ensuring that worthless data
does not find its means into the analysis phase. For instance, when you correct