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Introduction to Data Analytics
*MODULE 1

?DATAANALYTICS
Data analytics is a science of analyzing raw data in order to make
conclusions about that information. Many of the techniques and
processes of data analytics have been automated into mechanical
processes and algorithms that work over raw data for human
consumption.
Some of the top applications for data analysis:
1) Automatically analyze survey responses with text analysis
2) Analyze customer support tickets and automatically route them
3) Examine the success of marketing campaigns
4) Predict customer churn

INTRODUCTION TO DATA ANALYTICS -MODULE 1.pptx

? DATA
Data can be defined as a systematic record of a particular
quantity. It is the different values of that quantity
represented together in a set. It is a collection of facts
and figures to be used for a specific purpose such as a
survey or analysis. When arranged in an organized form,
can be called information. The source of data ( primary
data, secondary data) is also an important factor.
? TYPES OF DATA
Data may be qualitative or quantitative. Once you know the difference
between them, you can know how to use them.

? Qualitative Data:
They represent some characteristics or attributes. They depict
descriptions that may be observed but cannot be computed or
calculated. For example, data on attributes such as intelligence, honesty,
wisdom, cleanliness, and creativity collected using the students of your
class a sample would be classified as qualitative. They are more
exploratory than conclusive in nature.
? Quantitative Data:
These can be measured and not simply observed. They can be
numerically represented and calculations can be performed on them. For
example, data on the number of students playing different sports from
your class gives an estimate of how many of the total students play
which sport. This information is numerical and can be classified as
quantitative.

?Quality of Data
Data quality is a measure of the condition of data based on factors such
as accuracy, completeness, consistency, reliability and whether it's up to
date. Measuring data quality levels can help organizations identify data
errors that need to be resolved and assess whether the data in their IT
systems is fit to serve its intended purpose.
?SOURSES OF DATA
Sources of data are of two types;
1) Statistical data
2) Non statistical data
.

o Statistical Data
This type of data source refers to collection of data which are used for
official purposes, such as population census, official surveys, etc.,
o Non-Statistical Data
This type of data source refers to collection of data which are used for
various administrative purposes, mainly in the private sectors.
? Different Sources of Data
Sources of data can also be classified based on its collection methods,
which are,
? Internal Sources of Data
In several cases for a certain analysis, data is collected from records,
archives, and various other sources within the organization itself. Such
sources data are termed as internal sources data.

Example:
A school is performing an analysis to figure out the highest marks
achieved in class 8 science subjects for the last 10 years.
? External Sources of Data
Data may also be collected from various sources outside the
organization for analytical purposes.
Example:
As a patient you are analysing the price chart of your nearby hospitals
for treatment of ulcer.

?DATA COLLECTION
Depending on the source, it can classify as primary data or secondary
data. Let us take a look at them both.
? Primary Data
These are the data that are collected for the first time by an investigator
for a specific purpose. Primary data are ‘pure’ in the sense that no
statistical operations have been performed on them and they are
original. An example of primary data is the Census of India.
? Secondary Data
They are the data that are sourced from someplace that has originally
collected it. This means that this kind of data has already been collected
by some researchers or investigators in the past and is available either
in

published or unpublished form. This information is impure as statistical
operations may have been performed on them already. An example is
an
information available on the Government of India, the Department of
Finance’s website or in other repositories, books, journals, etc.
? DATA PROCESSING
Data processing is generally, "the collection and manipulation of items
of data to produce meaningful information." In this sense it can be
considered a subset of information processing. In simple words, Data
processing is a series of operations that use information to produce a
result.
Example:
Telecom Billing System-It calculates the monthly charges based on

customer’s service plan and the usage. This whole process is automated
using a billing team tracking it.
?DATA MANAGEMENT
Data management is how you extract answers and insights from raw
data to meet your information needs. The proliferation of electronic
data-collection methods in recent years has created the impression data
management is all about technology, but its roots are firmly in
accounting, statistics, and planning.
Data management includes storage, data security, data sharing, data
governance, data architecture, database management, and records
management. Once your data management strategy is in place, you’ll
get important insights by using your data to its full potential.

For example, A data management platform could collect customer data
From multiple sources, then analyze and organize it to segment your
Customers by purchase history. Data management platforms can be
Housed onsite.
? DATA EXPLORATION
Data exploration refers to the initial step in data analysis in which data
Analysts use data visualization and statistical techniques to describe
Dataset characterizations, such as size, quantity, and accuracy, in order
To better understand the nature of the data.
Example: Using a pie chart, Bar graph, Line graph, and another
visualizers etc.

? DATA FIX
A data fix is a change made to data involving the development team.
Data fixes only impact individual instances versus the product as a
Whole.
? BIG DATA STORAGE
Big data storage is a compute-and-storage architecture that collects and
manages large data sets and enables real-time data analytics. Companies
apply big data analytics to get greater intelligence from metadata. In
most cases, big data storage uses low-cost hard disk drives, although
moderating prices for flash appear to have opened the door to using
flash in servers and storage systems as the foundation of big data
storage. These systems can be all-flash or hybrids mixing disk and flash
storage.

The data itself in big data is unstructured, which means mostly file-
based and object storage.
?Basic Statistical Descriptions of Data
For data preprocessing to be successful, it is essential to have an overall
picture of your data. Basic statistical descriptions can be used to
identify
properties of the data and highlight which data values should be treated
as noise or outliers. This section discusses the areas of basic statistical
descriptions. We start with measures of central tendency, which
measure
the location of the middle or center of a data distribution. In particular,
we discuss the mean, median, mode, and midrange.

In addition to assessing the central tendency of our data set, we also
would like to have an idea of the dispersion.
? DESCRIPTIVE STATISTICS
Descriptive statistics also named Univariate, in short, help describe and
understand the features of a specific dataset, by giving short numeric
summaries about the sample and measures of the data. Descriptive
statistics are mere exploration as they do not allows us to make
conclusions beyond the data we have analyzed or reach conclusions
regarding any hypotheses we might have made. Numerical and
categorical variables, as we will see shortly, have different descriptive
statistics approaches.

?DATA SIMILARITY AND DISSIMILARITY
We consider similarity and dissimilarity in many places in data
science.
?SIMILARITY MEASURE
It is a numerical measure of how alike two data objects are.
higher when objects are more alike.
often falls in the range [0,1]
Similarity might be used to identify,
? Duplicate data that may have differences due to typos.
? equivalent instances from different data sets. E.g. names and/or
addresses that are the same but have misspellings.
? groups of data that are very close (clusters).

Data analysis is a comprehensive method of inspecting, cleansing,
transforming, and modeling data to discover useful information, draw
conclusions, and support decision-making. It is a multifaceted process
involving various techniques and methodologies to interpret data from
various sources in different formats, both structured and unstructured.
Data analysis is not just a mere process; it's a tool that empowers
organizations to make informed decisions, predict trends, and improve
operational efficiency. It's the backbone of strategic planning in businesses,
governments, and other organizations.
What is Data Analysis?

The data analysis process is a systematic approach that involves
several stages, each crucial to ensuring the accuracy and usefulness
of the results.

STEP 1: DEFINING OBJECTIVES AND QUESTIONS
1.Define Objectives:
1. Clarify the goals of the analysis.
2. Formulate clear, specific questions to answer.
2.Understand the Problem:
1. Analyze the situation or issue.
2. Identify key aspects that need exploration.
3.Identify Data Needs:
1. Determine what data is necessary.
2. Ensure data aligns with objectives.
4.Define Metrics:
1. Establish metrics or indicators.
2. Measure outcomes and success criteria.
This step sets the foundation and direction for the entire data analysis
process.

STEP 2: DATA COLLECTION
?Gather Relevant Data:
Use methods like surveys, interviews, and observations.
Extract data from existing databases.
?Data Types:
Quantitative: Numerical data.
Qualitative: Non-numerical data.
?Alignment with Objectives:
Ensure data collected is relevant to the questions and objectives.
Confirm data quality and reliability.

1.Identify Errors and Inconsistencies:
? Detect inaccuracies or anomalies in the data.
2.Handle Missing Values:
? Impute missing data or remove incomplete
records.
3.Remove Duplicates:
? Eliminate repeated entries.
4.Correct Errors:
? Fix incorrect data points or inconsistencies.
5.Ensure Data Quality:
? Validate and standardize formats.
STEP 3: DATA CLEANING

1.Apply Analytical Techniques:
?Use statistical and mathematical methods.
2.Discover Patterns:
?Identify trends and relationships in the data.
3.Use Tools and Software:
?Utilize Python, R, Excel, SPSS, SAS, etc.
Data analysis helps uncover insights that inform decision-making and strategy.
STEP 4: DATA ANALYSIS

STEP 5: DATA INTERPRETATION AND VISUALIZATION
?Interpret Results:
Understand and explain the analysis outcomes.
?Create Visualizations:
?charts, graphs, and other visuals to represent data.
?Simplify Complexity:
Make complex data more understandable.
?Communicate Findings:
?Provide a clear picture of insights for decision-making.

The final step in the data analysis process is data storytelling. This
involves presenting the findings of the analysis in a narrative form that
is engaging and easy to understand. Data storytelling is crucial for
communicating the results to non-technical audiences and for making
data-driven decisions.
STEP 6: DATA STORYTELLING

Types of Data Analytics
There are four major types of data
analytics:
? Predictive (forecasting)
? Descriptive (business intelligence and data mining)
? Prescriptive (optimization and simulation)
? Diagnostic analytics

? Improved Decision Making
? Reduced Risk
? Increased Productivity
? Less Disasters
? Better Decisions

?Descriptive analysis, also known as descriptive analytics or descriptive
statistics, is the process of using statistical techniques to describe or
summarize a set of data. As one of the major types of data analysis,
descriptive analysis is popular for its ability to generate accessible
insights from otherwise uninterpreted data. They are measures of
frequency, central tendency, dispersion or variation, and position. t's
often conducted before diagnostic or predictive analysis, as it simply
aims to describe and summarize past data.
DESCRIPTIVE ANALYSIS

Predictive analytics is a branch of advanced analytics
that makes predictions about future outcomes using
historical data combined with statistical modeling, data
mining techniques, and machine learning.
Predictive analytics models are designed to assess
historical data, discover patterns, observe trends, and
use that information to predict future trends. Popular
predictive analytics models include classification,
PREDICTIVE
ANALYTICS

Banking: Uses machine learning to predict loan defaults, assess
customer risk, target lucrative clients, and detect fraudulent spending.
Healthcare: Helps manage chronic illnesses and track infections like
sepsis. For example, Geisinger Health created a model predicting sepsis
outcomes, improving diagnosis and treatment.
Human Resources: Uses analytics and surveys to match candidates, reduce
turnover, and boost engagement, cutting recruitment costs and enhancing
satisfaction.

? Security
? Risk reduction
? Improved decision making
? Operational efficiency
BENEFITS OF PREDICTIVE ANALYTICS

Diagnostic analytics is the process of using data to determine the causes of trends and
correlations between variables. It can be viewed as a logical next step after using descriptive
analytics to identify trends. Diagnostic analysis can be done manually, using an algorithm, or
with statistical software (such as Microsoft Excel).
Examining Market Demand
Identifying Technology Issues
Improving Company Culture
WHAT IS DIAGNOSTIC ANALYTICS?

Prescriptive Analytics:
Prescriptive analytics involves analyzing data to recommend actions by
considering possible scenarios, available resources, and past and current
performance. It helps in making decisions across various time frames, from
immediate to long-term. Unlike descriptive analytics, which reviews
outcomes post-event, prescriptive analytics proactively suggests strategies
for future actions.

?Evaluate whether a local fire department should require residents to
evacuate a particular area when a wildfire is burning nearby
?Predict whether an article on a particular topic will be popular with
readers based on data about searches and social shares for related topics
?Adjust a worker training program in real time based on how the worker is
responding to each lesson

? Descriptive Analytics: What happened in the past.
? Diagnostic Analytics: Examines why it might have happened.
? Predictive Analytics: Forecasts what is likely to happen in the future.
? Prescriptive Analytics: Determines the best course of action based on
predictions.

Data preprocessing is crucial in the data analysis and machine learning. It
involves transforming raw data into a clean and usable format. Here are
the main steps involved in data preprocessing

?GRAPHICAL REPRESENTATION OF DATA
It is a way of analysing numerical data. It exhibits the relation between
data, ideas, information and concepts in a diagram. It is easy to
understand and it is one of the most important learning strategies. It
always depends on the type of information in a particular domain. There
are different types of graphical representation. Some of them are as
follows:
? Line Graphs –
Line graph or the linear graph is used to display the continuous data and
it
? Bar Graphs –
Bar Graph is used to display the category of data and it compares the
data using solid bars to represent the quantities.is useful for predicting
future events over time.

? Histograms –
The graph that uses bars to represent the frequency of numerical data
that are organized into intervals. Since all the intervals are equal and
continuous, all the bars have the same width.
? Frequency Table –
The table shows the number of pieces of data that falls within the given
interval.
? Circle Graph –
Also known as the pie chart that shows the relationships of the parts of
the whole. The circle is considered with 100% and the categories
occupied is represented with that specific percentage like 15%, 56%,
etc.

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