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Approaching data analysis .pdf


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Approaching data analysis: How to interpret data? – Beginners Guide
With more and more companies using big data, demand for
professional data analysts has witnessed exponential growth in the recent years.
“Learners of data analysis and interpretation need to have an in-depth understanding of the
subject along with the statistical acumen and working knowledge of tool sets to make
significant progress in the field of data analytics” – Dr. Venkatesh, Statistician.
To gain good expertise in Data analysis and interpretation
, the beginner learner
requires good foundation in basic statistics, and better skills of how to input, process and
analyze data by employing a range of data analytical and visualization tools
The objectives of data analysis and interpretation are to manipulate the data, both
qualitative as well as quantitative, to obtain utilizable information that is critical to making
informed decisions. It involves the usage of many different methods and procedures that can
 Describe and summarize data
 Evaluate and enhance quality of data
 Break a macro problem into micro parts for the easy understanding and problem
solving
 Understand the relationships between different variables (independent and dependent)
 Facilitate comparison of variables
 Identify the difference between the variables (e.g. Ordinal, nominal, categorical)
 Evaluate impact or the significance (e.g. Clinical/meaningful in a real time and
Statistical)
 Forecast possible outcomes

#1. Data Analysis in Research
Research is an important tool to discover new events and understands new phenomena and
the Data analysis in the research facilitates the researcher to structure the results from
survey research and allows the researcher to reach a conclusion. The purpose of the data
analysis in research studies is to find answers to the research question and to facilitate the
researcher to determine the trends and inter-relationship of different variables included in the
study.
Data analysis in research methodology refers to a variety of specific procedures and
methods used in the manipulation of the research data to obtain logical inferences. Important
stages of data analysis include
 Data Cleaning and preparation
 Descriptive analysis of data
 Statistical models and test of hypothesis

© 2017-2018 All Rights Reserved, No part of this document should be modified/used without prior consent
Statswork ™ - www.statswork.com
INDIA: Nungambakkam, Chennai – 600 034
UK: The Portergate, Ecclesall Road, Sheffield, S11 8NX

A beginner in the field of Data Analytics needs to develop a good understanding of the
qualitative and quantitative data, their important differences and the various methods used in
their analysis. Qualitative research study uses tools like interviews surveys, focus groups and
experiments to collect important data for the study and their analysis process involves
recognizing common patterns within the responses to achieve research objectives.
Data analysis in quantitative research studies involves the process of critically analyzing
and interpreting figures and numbers, with the purpose of obtaining the underlying principle
behind the emergence of principal findings of the study.

#2. Data Interpretation
Interpretation of Data analysis research forms the critical feature of the research report
and is often considered the most difficult section in research writing because of its least
structured nature.
Data interpretation is the process of putting together specific meaning to the data, as mere
data themselves do not carry and convey any importance to the decision-making process.
“Beginners in the discipline of data analysis and interpretation need to understand that often
the same set of data can have different interpretations. Interpretation of data necessitates fair
and careful judgments from the data interpreters to arrive at the valid and meaningful
information from the available data” – Dr. Venkatesh, Statistician.
Important skills of Data interpretation for beginners include
 Proficiency in mathematical reasoning
 Ability to see bigger picture
 Competency in interpreting numerical data and non-numerical data like texts and
images
 Sufficient working knowledge of data visualization tools
 Domain specific knowledge (e.g. Healthcare, Regulatory, Finance)
Overall, an effective Data analysis and interpretation in research methodology require the
budding researchers to possess creativeness and perceptiveness to integrate the findings of the
study in both theory and application relating to the method, unexpected results, and finally
considering the limitations of the research study.

© 2017-2018 All Rights Reserved, No part of this document should be modified/used without prior consent
Statswork ™ - www.statswork.com
INDIA: Nungambakkam, Chennai – 600 034
UK: The Portergate, Ecclesall Road, Sheffield, S11 8NX


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