This document discusses the four main types of analytics: descriptive, diagnostic, predictive, and prescriptive. Descriptive analytics answers the question "What happened?" by summarizing past data. Diagnostic analytics answers "Why did this happen?" by analyzing data to determine causes of trends. Predictive analytics answers "What might happen in the future?" by using statistics and modeling to predict outcomes. Prescriptive analytics answers "What should we do next?" by recommending actions based on predictive analytics. The document provides examples of each type.

1. UNIT 5
Descriptive, Diagnostic, Predictive and Prescriptive analytics
 Descriptive, which answers the question, ―What happened?‖
 Diagnostic, which answers the question, ―Why did this happen?‖
 Predictive, which answers the question, ―What might happen in the future?‖
 Prescriptive, which answers the question, ―What should we do next?‖
I. Descriptive Analytics
The first type of data analysis is descriptive analysis. It is at the foundation of all data insight.
It is the simplest and most common use of data in business today. Descriptive analysis
answers the ―what happened‖ by summarizing past data, usually in the form of dashboards.
The biggest use of descriptive analysis in business is to track Key Performance Indicators
(KPIs). KPIs describe how a business is performing based on chosen benchmarks.
Business applications of descriptive analysis include:
 KPI dashboards
 Monthly revenue reports
 Sales leads overview
Descriptive analytics is the process of using current and historical data to identify trends and
relationships. It‘s sometimes called the simplest form of data analysis because it describes
trends and relationships but doesn‘t dig deeper.
Descriptive analytics is relatively accessible and likely something your organization uses
daily. Basic statistical software, such as Microsoft Excel or data visualization tools, such as
Google Charts and Tableau, can help parse data, identify trends and relationships between
variables, and visually display information.
Descriptive analytics is especially useful for communicating change over time and uses
trends as a springboard for further analysis to drive decision-making.
EXAMPLES OF DESCRIPTIVE ANALYTICS
1. Traffic and Engagement Reports
One example of descriptive analytics is reporting. If your organization tracks engagement in
the form of social media analytics or web traffic, you‘re already using descriptive analytics.
These reports are created by taking raw data—generated when users interact with your
website, advertisements, or social media content—and using it to compare current metrics to
historical metrics and visualize trends.

2. For example, you may be responsible for reporting on which media channels drive the most
traffic to the product page of your company‘s website. Using descriptive analytics, you can
analyze the page‘s traffic data to determine the number of users from each source. You may
decide to take it one step further and compare traffic source data to historical data from the
same sources. This can enable you to update your team on movement; for instance,
highlighting that traffic from paid advertisements increased 20 percent year over year.
The three other analytics types can then be used to determine why traffic from each source
increased or decreased over time, if trends are predicted to continue, and what your team‘s
best course of action is moving forward.
2. Financial Statement Analysis
Another example of descriptive analytics that may be familiar to you is financial statement
analysis. Financial statements are periodic reports that detail financial information about a
business and, together, give a holistic view of a company‘s financial health.
There are several types of financial statements, including the balance sheet, income
statement, cash flow statement, and statement of shareholders‘ equity. Each caters to a
specific audience and conveys different information about a company‘s finances.
Financial statement analysis can be done in three primary ways: vertical, horizontal, and
ratio.
Vertical analysis involves reading a statement from top to bottom and comparing each item to
those above and below it. This helps determine relationships between variables. For instance,
if each line item is a percentage of the total, comparing them can provide insight into which
are taking up larger and smaller percentages of the whole.
Horizontal analysis involves reading a statement from left to right and comparing each item
to itself from a previous period. This type of analysis determines change over time.
Finally, ratio analysis involves comparing one section of a report to another based on their
relationships to the whole. This directly compares items across periods, as well as your
company‘s ratios to the industry‘s to gauge whether yours is over- or underperforming.
Each of these financial statement analysis methods are examples of descriptive analytics, as
they provide information about trends and relationships between variables based on current
and historical data.
3. Demand Trends
Descriptive analytics can also be used to identify trends in customer preference and behavior
and make assumptions about the demand for specific products or services.

3. Streaming provider Netflix‘s trend identification provides an excellent use case for
descriptive analytics. Netflix‘s team—which has a track record of being heavily data-
driven—gathers data on users‘ in-platform behavior. They analyze this data to determine
which TV series and movies are trending at any given time and list trending titles in a section
of the platform‘s home screen.
Not only does this data allow Netflix users to see what‘s popular—and thus, what they might
enjoy watching—but it allows the Netflix team to know which types of media, themes, and
actors are especially favored at a certain time. This can drive decision-making about future
original content creation, contracts with existing production companies, marketing, and
retargeting campaigns.
4. Aggregated Survey Results
Descriptive analytics is also useful in market research. When it comes time to glean insights
from survey and focus group data, descriptive analytics can help identify relationships
between variables and trends.
For instance, you may conduct a survey and identify that as respondents‘ age increases, so
does their likelihood to purchase your product. If you‘ve conducted this survey multiple times
over several years, descriptive analytics can tell you if this age-purchase correlation has
always existed or if it was something that only occurred this year.
Insights like this can pave the way for diagnostic analytics to explain why certain factors are
correlated. You can then leverage predictive and prescriptive analytics to plan future product
improvements or marketing campaigns based on those trends.
5. Progress to Goals
Finally, descriptive analytics can be applied to track progress to goals. Reporting on progress
toward key performance indicators (KPIs) can help your team understand if efforts are on
track or if adjustments need to be made.
For example, if your organization aims to reach 500,000 monthly unique page views, you can
use traffic data to communicate how you‘re tracking toward it. Perhaps halfway through the
month, you‘re at 200,000 unique page views. This would be underperforming because you‘d
like to be halfway to your goal at that point—at 250,000 unique page views. This descriptive
analysis of your team‘s progress can allow further analysis to examine what can be done
differently to improve traffic numbers and get back on track to hit your KPI.
2. Diagnostic 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).

4. There are several concepts to understand before diving into diagnostic analytics: hypothesis
testing, the difference between correlation and causation, and diagnostic regression analysis.
Hypothesis testing is the statistical process of proving or disproving an assumption.
Having a hypothesis to test can guide and focus your diagnostic analysis.
Hypotheses can be future-oriented (for example, ―If we change our company‘s logo, more
people in North America will buy our product.‖), but these aid predictive or prescriptive
analytics. When conducting diagnostic analytics, hypotheses are historically-oriented (for
example, ―I predict this month‘s decline in sales was caused by our product‘s recent price
increase.‖). The hypothesis directs your analysis and serves as a reminder of what you‘re
aiming to prove or disprove.
Correlation vs. Causation
When exploring relationships between variables, it‘s important to be aware of the distinction
between correlation and causation. If two or more variables are correlated, their directional
movements are related. If two variables are positively correlated, it means that as one goes up
or down, so does the other. Alternatively, if two variables are negatively correlated, one
variable goes up while the other goes down.
The key in diagnostic analytics is remembering that just because two variables are
correlated, it doesn‘t necessarily mean one caused the other to occur.
If your organization is able to dedicate resources to running controlled experiments, you
may be able to determine causation between variables. While determining causation is
ideal, correlation can still offer the insight needed to make sense of your data and use it to
make impactful decisions.
Diagnostic Regression Analysis
Some relationships between variables are easily discerned, but others require more in-depth
analysis, such as regression analysis, which can be used to determine the relationship
between two variables (single linear regression) or three or more variables (multiple
regression). The relationship is expressed by a mathematical equation that translates to the
slope of a line that best fits the variables‘ relationship.
―Regression allows us to gain insights into the structure of that relationship and provides
measures of how well the data fit that relationship,‖ says Harvard Business School
Professor Jan Hammond, who teaches the online course Business Analytics, one of the
three courses that make up the Credential of Readiness (CORe) program. ―Such insights
can prove extremely valuable for analyzing historical trends and developing forecasts.‖

5. When regression analysis is used to explain the relationships between variables in a
historical context, that‘s an example of diagnostic analytics. The regression can then be
used to develop forecasts for the future, which is an example of predictive analytics.
Diagnostic analytics can be leveraged to understand why something happened and the
relationships between related factors. With the basics under your belt, consider these four
examples of diagnostic analytics in action and how they can apply to your company.
EXAMPLES OF DIAGNOSTIC ANALYTICS
1. Examining Market Demand
One use case of diagnostic analytics is determining the reasons behind product demand.
For example, take meal kit subscription company ―HelloFresh‖. The company gathers
millions of data points from global users, including information about geographic
location, disclosed demographic data, meal type, flavor preferences, and typical order
cadence and timing.
HelloFresh‘s team uses this data to identify relationships between trends in customer
attributes and behavior. As a hypothetical example, imagine the HelloFresh team
identifies a spike in fish-based recipe orders. After conducting diagnostic analysis, they
find that the attributes most highly correlated with ordering fish recipes are identifying as
female and living in the northeastern United States.
From there, the team could conduct market research with that specific demographic to
learn more about the demand for fish recipes. Was it caused by a recent scientific study
touting the health benefits of fish for women? Perhaps people who live in the northeastern
United States have a refined palate for seafood because they live relatively close to the
Atlantic Ocean. Their reasoning could provide impactful insights to HelloFresh.
Dipping into the other types of analytics, the team could also consider whether the trend
is expected to continue (predictive analytics) and if it‘s worth the effort and money to
create more fish-based recipes to cater to this audience‘s preference (prescriptive
analytics).
2. Explaining Customer Behavior
For companies that collect customer data, diagnostic analytics is the key to understanding
why customers do what they do. These insights can be used to improve products and user
experience (UX), reposition brand messaging, and ensure product-audience fit.
Continuing with the HelloFresh example, consider the value of customer retention to the
company, which operates on a subscription model. Keeping customers is more cost-

6. effective than obtaining new ones, so the HelloFresh uses diagnostic analytics to
determine why departing customers choose to cancel subscriptions.
During the cancellation process, departing customers must provide their reason for
canceling. Options range from ―doesn‘t fit my budget‖ to ―doesn‘t fit my schedule or
dietary needs,‖ and there‘s also an option to write in an answer. By gathering this data,
HelloFresh can analyze the most cited reasons for losing customers among specific
regions and demographics and use diagnostic analytics to answer the question, ―Why are
people cancelling their subscriptions?‖
These insights can help improve HelloFresh‘s product and user experience to avoid losing
more customers to those reasons.
3. Identifying Technology Issues
One example of diagnostic analytics that requires using a software program or proprietary
algorithm is running tests to determine the cause of a technology issue. This is often
referred to as ―running diagnostics‖ and may be something you‘ve done before when
experiencing computer difficulty.
Some of these algorithms are constantly at work in the background of your machine,
while others need to be initiated by a human. One type of diagnostic test you may be
familiar with is solution-based diagnostics, which detects and flags symptoms of known
issues and conducts a scan to determine the root cause. This can allow you to address the
issue and escalate it if the cause is serious.
4. Improving Company Culture
Diagnostic analytics can also be leveraged to improve internal company culture. Human
resource departments can gather information about employees‘ sense of physical and
psychological safety, issues they care about, and qualities and skills that make someone
successful and happy. Many of these insights come from running internal, anonymous
surveys and conducting exit interviews to identify factors that contributed to employees‘
desire to stay or leave.
Gathering information about employees‘ thoughts and feelings allows you to analyze the
data and determine how areas like company culture and benefits could be improved. This
can include anything from wishing the company made more corporate social
responsibility (CSR) contributions to feeling discriminated against at work. In these
cases, the data presents a case for allocating more resources to CSR and diversity, equity,
inclusion, and belonging efforts.

7. Insights from surveys and interviews can also enable hiring managers to determine which
qualities and skills make someone successful at your company or on your specific team,
and thus help attract and hire better candidates for open roles.
Diagnostic analytics can help boost employee happiness, safety, and retention, as well as
lead to more effective hiring processes.
3. Predictive Analytics
The term predictive analytics refers to the use of statistics and modeling techniques to make
predictions about future outcomes and performance. Predictive analytics looks at current and
historical data patterns to determine if those patterns are likely to emerge again. This allows
businesses and investors to adjust where they use their resources to take advantage of
possible future events. Predictive analysis can also be used to improve operational
efficiencies and reduce risk.
Predictive analytics is a form of technology that makes predictions about certain unknowns
in the future. It draws on a series of techniques to make these determinations,
including Artificial Intelligence, Data Mining, machine learning, modeling, and
statistics. For instance, data mining involves the analysis of large sets of data to detect
patterns from it. Text analysis does the same, except for large blocks of text. Predictive
models are used for all kinds of applications, including:
 Weather forecasts
 Creating video games
 Translating voice to text for mobile phone messaging
 Customer service
 Investment portfolio development
Uses of Predictive Analytics
Predictive analytics is a decision-making tool in a variety of industries.
Forecasting
Forecasting is essential in manufacturing because it ensures the optimal utilization of
resources in a supply chain. Critical spokes of the supply chain wheel, whether it is
inventory management or the shop floor, require accurate forecasts for functioning.
Predictive modeling is often used to clean and optimize the quality of data used for such
forecasts. Modeling ensures that more data can be ingested by the system, including from
customer-facing operations, to ensure a more accurate forecast.
Credit

8. Credit scoring makes extensive use of predictive analytics. When a consumer or business
applies for credit, data on the applicant's credit history and the credit record of borrowers
with similar characteristics are used to predict the risk that the applicant might fail to
perform on any credit extended.
Underwriting
Data and predictive analytics play an important role in underwriting. Insurance companies
examine policy applicants to determine the likelihood of having to pay out for a
future claim based on the current risk pool of similar policyholders, as well as past events
that have resulted in payouts. Predictive models that consider characteristics in comparison
to data about past policyholders and claims are routinely used by actuaries.
Marketing
Individuals who work in this field look at how consumers have reacted to the overall
economy when planning on a new campaign. They can use these shifts in demographics to
determine if the current mix of products will entice consumers to make a purchase.
Active traders, meanwhile, look at a variety of metrics based on past events when deciding
whether to buy or sell a security. Moving averages, bands, and breakpoints are based on
historical data and are used to forecast future price movements.
Predictive Analytics vs. Machine Learning
A common misconception is that predictive analytics and machine learning are the same
things. Predictive analytics help us understand possible future occurrences by analyzing the
past. At its core, predictive analytics includes a series of statistical techniques (including
machine learning, predictive modeling, and data mining) and uses statistics (both historical
and current) to estimate, or predict, future outcomes.
Machine learning, on the other hand, is a subfield of computer science that, as per the 1959
definition by Arthur Samuel (an American pioneer in the field of computer gaming and
artificial intelligence) means "the programming of a digital computer to behave in a way
which, if done by human beings or animals, would be described as involving the process of
learning."7
The most common predictive models include decision trees, regressions (linear and logistic),
and neural networks, which is the emerging field of deep learning methods and
technologies.
Types of Predictive Analytical Models
There are three common techniques used in predictive analytics: Decision trees, neural
networks, and regression. Read more about each of these below.
Decision Trees
If you want to understand what leads to someone's decisions, then you may find decision
trees useful. This type of model places data into different sections based on certain variables,

9. such as price or market capitalization. Just as the name implies, it looks like a tree with
individual branches and leaves. Branches indicate the choices available while individual
leaves represent a particular decision.
Decision trees are the simplest models because they're easy to understand and dissect.
They're also very useful when you need to make a decision in a short period of time.1
Regression
This is the model that is used the most in statistical analysis. Use it when you want to
determine patterns in large sets of data and when there's a linear relationship between the
inputs. This method works by figuring out a formula, which represents the relationship
between all the inputs found in the dataset. For example, you can use regression to figure out
how price and other key factors can shape the performance of a security.
Neural Networks
Neural networks were developed as a form of predictive analytics by imitating the way the
human brain works. This model can deal with complex data relationships using artificial
intelligence and pattern recognition. Use it if you have several hurdles that you need to
overcome like when you have too much data on hand, when you don't have the formula you
need to help you find a relationship between the inputs and outputs in your dataset, or when
you need to make predictions rather than come up with explanations.1
If you've already used decision trees and regression as models, you can confirm your
findings with neural networks.1
How Businesses Can Use Predictive Analytics
As noted above, predictive analysis can be used in a number of different applications.
Businesses can capitalize on models to help advance their interests and improve their
operations. Predictive models are frequently used by businesses to help improve
their customer service and outreach.
Executives and business owners can take advantage of this kind of statistical analysis to
determine customer behavior. For instance, the owner of a business can use predictive
techniques to identify and target regular customers who could defect and go to a competitor.
Predictive analytics plays a key role in advertising and marketing. Companies can use
models to determine which customers are likely to respond positively to marketing and sales
campaigns. Business owners can save money by targeting customers who will respond
positively rather than doing blanket campaigns.
Benefits of Predictive Analytics
There are numerous benefits to using predictive analysis. As mentioned above, using this
type of analysis can help entities when you need to make predictions about outcomes when
there are no other (and obvious) answers available.

10. Investors, financial professionals, and business leaders are able to use models to help reduce
risk. For instance, an investor and their advisor can use certain models to help craft an
investment portfolio with minimal risk to the investor by taking certain factors into
consideration, such as age, capital, and goals.
There is a significant impact to cost reduction when models are used. Businesses can
determine the likelihood of success or failure of a product before it launches. Or they can set
aside capital for production improvements by using predictive techniques before
the manufacturing process begins.
Criticism of Predictive Analytics
The use of predictive analytics has been criticized and, in some cases, legally restricted due
to perceived inequities in its outcomes. Most commonly, this involves predictive models that
result in statistical discrimination against racial or ethnic groups in areas such as credit
scoring, home lending, employment, or risk of criminal behavior.
4. Prescriptive Analytics
Prescriptive analytics is a process that analyzes data and provides instant recommendations
on how to optimize business practices to suit multiple predicted outcomes. In essence,
prescriptive analytics takes the ―what we know‖ (data), comprehensively understands that
data to predict what could happen, and suggests the best steps forward based on informed
simulations.
Prescriptive analytics is the third and final tier in modern, computerized data processing.
These three tiers include:
Descriptive analytics: Descriptive analytics acts as an initial catalyst to clear and concise data
analysis. It is the ―what we know‖ (current user data, real-time data, previous engagement
data, and big data).
Predictive analytics: Predictive analytics applies mathematical models to the current data to
inform (predict) future behavior. It is the ―what could happen."
Prescriptive analytics: Prescriptive analytics utilizes similar modeling structures to predict
outcomes and then utilizes a combination of machine learning, business rules, artificial
intelligence, and algorithms to simulate various approaches to these numerous outcomes. It
then suggests the best possible actions to optimize business practices. It is the ―what should
happen.‖
Prescriptive analytics is the natural progression from descriptive and predictive analytics
procedures. It goes a step further to remove the guesswork out of data analytics. It also
saves data scientists and marketers time in trying to understand what their data means and
what dots can be connected to deliver a highly personalized and propitious user experience to
their audiences.
Benefits of prescriptive analytics

11. If you’re a senior executive, looking to further optimize the efficiency and success of your
organization’s operations is always top of mind. Prescriptive analytics is the smartest and most
efficient tool available to scaffold any organization’s business intelligence. Prescriptive analytics
affords organizations the ability to:
Effortlessly map the path to success. Prescriptive analytic models are designed to pull together data
and operations to produce the roadmap that tells you what to do and how to do it right the first
time. Artificial intelligence takes the reins of business intelligence to apply simulated actions to a
scenario to produce the steps necessary to avoid failure or achieve success.
Inform real-time and long-term business operations. Decision makers can view both real-time and
forecasted data simultaneously to make decisions that support sustained growth and success. This
streamlines decision making by offering specific recommendations.
Spend less time thinking and more time doing. The instant turnaround of data analysis and outcome
prediction lets your team spend less time finding problems and more time designing the perfect
solutions. Artificial intelligence can curate and process data better than your team of data engineers
and in a fraction of the time.
Reduce human error or bias. Through more advanced algorithms and machine learning processes,
predictive analytics provides an even more comprehensive and accurate form of data aggregation
and analysis than descriptive analytics, predictive analytics, or even individuals.
EXAMPLES OF PRESCRIPTIVE ANALYTICS
1. Venture Capital: Investment Decisions
Investment decisions, while often based on gut feelings, can be strengthened by algorithms
that weigh risks and recommend whether to invest.
One example in the venture capital space is an experiment—explained in the Harvard
Business Review—that tested the effectiveness of an algorithm‘s decisions about which
startups to invest in as compared to angel investors' decisions.
The findings were nuanced. The algorithm outperformed angel investors who were less
experienced at investing and less skilled at controlling their cognitive biases; however, angel
investors outperformed the algorithm when they were experienced in investing and able to
control their cognitive biases.
This experiment sheds light on the complementary role prescriptive analytics must play in
making decisions and its potential to aid decision-making when experience isn‘t present and
cognitive biases need flagging. An algorithm is only as unbiased as the data it‘s trained with,
so human judgment is required whether using an algorithm or not.
2. Sales: Lead Scoring
Prescriptive analytics plays a prominent role in sales through lead scoring, also called lead
ranking. Lead scoring is the process of assigning a point value to various actions along the

12. sales funnel, enabling you, or an algorithm, to rank leads based on how likely they are to
convert into customers.
Actions you can assign value to include:
 Page views
 Email interactions
 Site searches
 Content engagement, such as attending webinars, downloading e-books, or watching
videos
When assigning each action a point value, assign the highest number of points to those that
imply purchase intent (for instance, visiting a product page) and negative points to those that
reveal non-purchase intent (for instance, viewing job postings on your site). This can help
prioritize outreach to leads most likely to convert into customers, potentially saving your
organization time and money.
3. Content Curation: Algorithmic Recommendations
If you‘ve ever scrolled through a social media platform or dating app, you‘ve likely
experienced prescriptive analytics firsthand through algorithmic content recommendations.
Businesses‘ algorithms gather data based on your engagement history on their platforms (and
potentially others, too). The combinations of your previous behaviors can act as triggers for
an algorithm to release a specific recommendation. For instance, if you regularly watch shoe
review videos on YouTube, the platform‘s algorithm will likely analyze that data and
recommend you watch more of the same type of video or similar content you may find
interesting.
On social media, TikTok‘s ―For You‖ feed is one example of prescriptive analytics in action.
The company‘s website explains that a user‘s interactions on the app, much like lead scoring
in sales, are weighted based on indication of interest.
―For example,‖ TikTok‘s website says, ―if you finish a video, that‘s a strong indicator that
you‘re interested. Videos are then ranked to determine how likely you‘ll be interested in each
video and delivered to each unique ‗For You‘ feed.‖
This prescriptive analytics use case can make for higher customer engagement rates,
increased customer satisfaction, and the potential to retarget customers with ads based on
their behavioral history.
4. Banking: Fraud Detection
Another algorithmic use of prescriptive analytics is the detection and flagging of bank fraud.
With the sheer volume of data stored in a bank‘s system, it would be nearly impossible for a
person to manually detect any suspicious activity in a single account. An algorithm—trained
using customers‘ historical transaction data—analyzes and scans new transactional data for

13. anomalies. For instance, perhaps you typically spend $3,000 per month, but this month,
there‘s a $30,000 charge on your credit card.
The algorithm analyzes patterns in your transactional data, alerts the bank, and provides a
recommended course of action. In this example, the course of action may be to cancel the
credit card, as it could have been stolen.
5. Product Management: Development and Improvement
Prescriptive analytics can also inform product development and improvements. Product
managers can gather user data by surveying customers, running tests with a product‘s beta
versions, conducting market research with people who aren‘t current product users, and
collecting behavioral data as current users interact. All this data can be analyzed—either
manually or algorithmically—to identify trends, discover the reasons for those trends, and
predict whether the trends are predicted to recur.
Prescriptive analytics can help determine which features to include or leave out of a product
and what needs to change to ensure an optimal user experience.
6. Marketing: Email Automation
Email automation is a clear-cut example of prescriptive analytics at work. Marketers use
email automation to sort leads into categories based on their motivations, mindsets, and
intentions and deliver email content to them based on those categories. Any interactions leads
have with emails can put them in another category, resulting in a different set of messages
being triggered.
While this is pure algorithmic prescriptive analysis, a person should plan, create, and oversee
automation flows. Email automation allows companies to provide personalized messaging at
scale and increase the chance of converting a lead into a customer using content that applies
to their motivations and needs.
Data Exploration
Data exploration is an initial approach to data analysis to understand the data set and identify
the characteristics of the data collected including its size, accuracy, initial patterns in the data
and other attributes. It is commonly conducted by data analysts using visual analytics tools,
but it can also be done in more advanced statistical software like ‗R‘.
Data Exploration also known as Exploratory Data Analysis (EDA). It provides a set of simple
tools to obtain some basic understanding of the data. The result of data exploration is
extremely powerful in grasping the structure of the data, the distribution of values and the

14. presence of extreme values. Data exploration also provides guidance on applying the right
kind of further statistical and data mining treatment to the data. All the data analysis software
packages include the tools for data exploration such as R, Rapidminer, SAS, IBM SPSS etc.
Simple pivot table functions, computing statistics like mean and standard deviation and
plotting data as a line chart, bar chart and scatter chart are part of data exploration techniques
that are used in everyday business settings.
Data Visualisation
The old adage ― A picture is worth 1000 words‖ is probably truer in today‘s information rich
environment than ever before. Data visualisation is the core of modern business analytics. It
is the process of displaying data (often in large quantities) in a meaningful way to provide
insight that will support better decisions.
Tools and software for Data Visualisation: Data visualisation ranges from simple excel
charts to more advanced interactive tools and software that allow users to easily view and
manipulate data with a few clicks.
Examples: Creating charts in Microsoft Excel – Column chart, Bar Chart, Line Chart, Pie
chart, Area chart, scatter chart, Bubble chart, Stock chart, Surface chart, Doughnut chart and
radar chart.
Researchers have observed that data visualisation improves decision making, provides
managers with better analysis, capabilities that reduce reliance on IT professional and
improves collaboration and information sharing.
Dashboards: Making data visible and accessible to employees at all levels is a hallmark of
effective modern organisations. A Dashboard is a visual representation of a set of key
business measures. It provide important summaries of key business information to help
manage a business process or function. It include tabular as well as visual data to allow
managers to quickly locate key data.
Introduction to R Programming

15. R is an open-source programming language that is widely used as a statistical software and
data analysis tool. R generally comes with the Command-line interface. R is available
across widely used platforms like Windows, Linux, and macOS. Also, the R programming
language is the latest cutting-edge tool.
It was designed by Ross Ihaka and Robert Gentleman at the University of Auckland,
New Zealand, and is currently developed by the R Development Core Team. R
programming language is an implementation of the S programming language. It also
combines with lexical scoping semantics inspired by Scheme. Moreover, the project
conceives in 1992, with an initial version released in 1995 and a stable beta version in
2000.
R is a programming language and software environment for statistical analysis, graphics
representation and reporting. R was created by Ross Ihaka and Robert Gentleman at the
University of Auckland, New Zealand, and is currently developed by the R Development
Core Team. The core of R is an interpreted computer language which allows branching and
looping as well as modular programming using functions. R allows integration with the
procedures written in the C, C++, .Net, Python or FORTRAN languages for efficiency. R is
freely available under the GNU General Public License, and pre-compiled binary versions are
provided for various operating systems like Linux, Windows and Mac. R is free software
distributed under a GNU-style copy left, and an official part of the GNU project called GNU
S.
Evolution of R
R was initially written by Ross Ihaka and Robert Gentleman at the Department of Statistics of
the University of Auckland in Auckland, New Zealand. R made its first appearance in 1993.
 A large group of individuals has contributed to R by sending code and bug reports.
 Since mid-1997 there has been a core group (the "R Core Team") who can modify the R
source code archive.
Why R Programming Language?

16.  R programming is used as a leading tool for machine learning, statistics, and data
analysis. Objects, functions, and packages can easily be created by R.
 It‘s a platform-independent language. This means it can be applied to all operating
system.
 It‘s an open-source free language. That means anyone can install it in any organization
without purchasing a license.
 R programming language is not only a statistic package but also allows us to integrate
with other languages (C, C++). Thus, you can easily interact with many data sources
and statistical packages.
 The R programming language has a vast community of users and it‘s growing day by
day.
 R is currently one of the most requested programming languages in the Data Science
job market that makes it the hottest trend nowadays.
Features of R Programming Language
Statistical Features of R:
 Basic Statistics: The most common basic statistics terms are the mean, mode, and
median. These are all known as ―Measures of Central Tendency.‖ So using the R
language we can measure central tendency very easily.
 Static graphics: R is rich with facilities for creating and developing interesting static
graphics. R contains functionality for many plot types including graphic maps, mosaic
plots, biplots, and the list goes on.
 Probability distributions: Probability distributions play a vital role in statistics and by
using R we can easily handle various types of probability distribution such as Binomial
Distribution, Normal Distribution, Chi-squared Distribution and many more.

17.  Data analysis: It provides a large, coherent and integrated collection of tools for data
analysis.
Programming Features of R:
 R Packages: One of the major features of R is it has a wide availability of libraries. R
has CRAN(Comprehensive R Archive Network), which is a repository holding more
than 10, 0000 packages.
 Distributed Computing: Distributed computing is a model in which components of a
software system are shared among multiple computers to improve efficiency and
performance. Two new packages ddR and multidplyr used for distributed
programming in R were released in November 2015.
Programming in R:
Since R is much similar to other widely used languages syntactically, it is easier to code
and learn in R. Programs can be written in R in any of the widely used IDE like R Studio,
Rattle, Tinn-R, etc. After writing the program save the file with the extension .r. To run
the program use the following command on the command line:
R file_name.r
Advantages of R:
 R is the most comprehensive statistical analysis package. As new technology and
concepts often appear first in R.
 As R programming language is an open source, you can run R anywhere and at any
time.
 R programming language is suitable for GNU/Linux and Windows operating system.
 R programming is cross-platform which runs on any operating system.
 In R, everyone is welcome to provide new packages, bug fixes, and code enhancements.
Disadvantages of R:
 In the R programming language, the standard of some packages is less than perfect.
 Although, R commands give little pressure to memory management. So R programming
language may consume all available memory.
 In R basically, nobody to complain if something doesn‘t work.
 R programming language is much slower than other programming languages such as
Python and MATLAB.
Applications of R:
 We use R for Data Science. It gives us a broad variety of libraries related to statistics. It
also provides the environment for statistical computing and design.
 R is used by many quantitative analysts as its programming tool. Thus, it helps in data
importing and cleaning.
 R is the most prevalent language. So many data analysts and research programmers use
it. Hence, it is used as a fundamental tool for finance.
 Tech giants like Google, Facebook, bing, Twitter, Accenture, Wipro and many more
using R nowadays.

18. Python Language
Despite starting out as a hobby project named after Monty Python, Python is now one of the
most popular and widely used programming languages in the world. Besides web and
software development, Python is used for data analytics, machine learning, and even design.
We take a closer look at some of the uses of Python, as well as why it‘s such a popular and
versatile programming language. We‘ve also picked out some of our top courses for learning
Python, and some ideas for Python projects for beginners.
Python is an object-oriented (based around data), high-level (easier for humans to
understand) programming language. First launched in 1992, it‘s built in a way that it‘s
relatively intuitive to write and understand. As such, it‘s an ideal coding language for those
who want rapid development.
If you‘re wondering who uses Python, you‘ll find that many of the biggest organisations in
the world implement it in some form. NASA, Google, Netflix, Spotify, and countless more
all use the language to help power their services.
Why is Python so popular?
According to the TIOBE index, which measures the popularity of programming languages,
Python is the third most popular programming language in the world, behind only Java and
C. There are many reasons for the ubiquity of Python, including:
 Its ease of use. For those who are new to coding and programming, Python can be an
excellent first step. It‘s relatively easy to learn, making it a great way to start building
your programming knowledge.
 Its simple syntax. Python is relatively easy to read and understand, as its syntax is
more like English. Its straightforward layout means that you can work out what each
line of code is doing.
 Its thriving community. As it‘s an open-source language, anyone can use Python to
code. What‘s more, there is a community that supports and develops the ecosystem,
adding their own contributions and libraries.
 Its versatility. As we‘ll explore in more detail, there are many uses for Python.
Whether you‘re interested in data visualisation, artificial intelligence or web
development, you can find a use for the language.
Why learn Python?
So, we know why Python is so popular at the moment, but why should you learn how to use
it? Aside from the ease of use and versatility mentioned above, there are several good reasons
to learn Python:

19.  Python developers are in demand. Across a wide range of fields, there is a demand
for those with Python skills. If you‘re looking to start or change your career, it could
be a vital skill to help you.
 It could lead to a well-paid career. Data suggests that the median annual salary for
those with Python skills is around £65,000 in the UK.
 There will be many job opportunities. Given that Python can be used in many
emerging technologies, such as AI, machine learning, and data analytics, it‘s likely
that it‘s a future-proof skill. Learning Python now could benefit you across your
career.
How long does it take to learn Python?
As we‘ve mentioned already, Python is a relatively straightforward programming language
compared to many others. As such, it‘s possible to learn the basics over just a few weeks.
Many of our short courses, such as Getting Started with Python, take 6-8 weeks to complete,
with only a few hours of learning required each week.
If you‘re looking for a more detailed exploration, there are also options available. Our deep
learning and Python programming ExpertTrack takes 21 weeks to complete, with 5-6 hours
of study needed every week.
What is Python used for?
Clearly, Python is a popular and in-demand skill to learn. But what is python programming
used for? We‘ve already briefly touched on some of the areas it can be applied to, and we‘ve
expanded on these and more Python examples below. Python can be used for:
1. AI and machine learning
Because Python is such a stable, flexible, and simple programming language, it‘s perfect for
various machine learning (ML) and artificial intelligence (AI) projects. In fact, Python is
among the favourite languages among data scientists, and there are many Python machine
learning and AI libraries and packages available.
If you‘re interested in this application of Python, our Deep Learning and Python
Programming for AI with Microsoft Azure ExpertTrack can help you develop your skills in
these areas. You can discover the uses of Python and deep learning while boosting your
career in AI.
2. Data analytics
Much like AI and machine learning, data analytics is another rapidly developing field that
utilises Python programming. At a time when we‘re creating more data than ever before,
there is a need for those who can collect, manipulate and organise the information.

20. Python for data science and analytics makes sense. The language is easy-to-learn, flexible,
and well-supported, meaning it‘s relatively quick and easy to use for analysing data. When
working with large amounts of information, it‘s useful for manipulating data and carrying out
repetitive tasks.
You can learn about data analytics using Python with our ExpertTrack, which will help you
develop practical data analytics skills.
3. Data visualisation
Data visualisation is another popular and developing area of interest. Again, it plays into
many of the strengths of Python. As well as its flexibility and the fact it‘s open-source,
Python provides a variety of graphing libraries with all kinds of features.
Whether you‘re looking to create a simple graphical representation or a more interactive plot,
you can find a library to match your needs. Examples include Pandas
Visualization and Plotly. The possibilities are vast, allowing you to transform data into
meaningful insights.
If data visualisation with Python sounds appealing, check out our 12-week ExpertTrack on
the subject. You‘ll learn how to leverage Python libraries to interpret and analyse data sets.
4. Programming applications
You can program all kinds of applications using Python. The general-purpose language can
be used to read and create file directories, create GUIs and APIs, and more. Whether it‘s
blockchain applications, audio and video apps, or machine learning applications, you can
build them all with Python.
We also have an ExpertTrack on programming applications with Python, which can help to
kick-start your programming career. Over the course of 12 weeks, you‘ll gain an introduction
on how to use Python, and start programming your own applications using it.
5. Web development
Python is a great choice for web development. This is largely due to the fact that there are
many Python web development frameworks to choose from, such as Django, Pyramid, and
Flask. These frameworks have been used to create sites and services such as Spotify, Reddit
and Mozilla.
Thanks to the extensive libraries and modules that come with Python frameworks, functions
such as database access, content management, and data authorisation are all possible and
easily accessible. Given its versatility, it‘s hardly surprising that Python is so widely used in
web development.

21. 6. Game development
Although far from an industry-standard in game development, Python does have its uses in
the industry. It‘s possible to create simple games using the programming language, which
means it can be a useful tool for quickly developing a prototype. Similarly, certain functions
(such as dialogue tree creation) are possible in Python.
If you‘re new to either Python or game development, then you can also discover how to make
a text-based game in Python. In doing so, you can work on a variety of skills and improve
your knowledge in various areas.
7. Language development
The simple and elegant design of Python and its syntax means that it has inspired the creation
of new programming languages. Languages such as Cobra, CoffeeScript, and Go all use a
similar syntax to Python.
This fact also means that Python is a useful gateway language. So, if you‘re totally new to
programming, understanding Python can help you branch out into other areas more easily.
8. Finance
Python is increasingly being utilised in the world of finance, often in areas such as
quantitative and qualitative analysis. It can be a valuable tool in determining asset price
trends and predictions, as well as in automating workflows across different data sources.
As mentioned already, Python is an ideal tool for working with big data sets, and there are
many libraries available to help with compiling and processing information. As such, it‘s one
of the preferred languages in the finance industry.
9. SEO
Another slightly surprising entry on our list of Python uses is in the field of search engine
optimisation (SEO). It‘s an area that often benefits from automation, which is certainly
possible through Python. Whether it‘s implementing changes across multiple pages or
categorising keywords, Python can help.
Emerging technologies such as natural language processing (NLP) are also likely to be
relevant to those working in SEO. Python can be a powerful tool in developing these NLP
skills and understanding how people search and how search engines return results.
10. Design

22. When asking ‗what is Python used for?‘ you probably weren‘t expecting design to feature on
the list. However, Python can be used to develop graphic design applications. Surprisingly,
the language is used across a range of 2D imaging software, such as Paint Shop Pro and
Gimp.
Python is even used in 3D animation software such as Lightwave, Blender, and Cinema 4D,
showing just how versatile the language is.
Python projects for beginners
So, if you were wondering what to do with Python and who uses Python, we‘ve given plenty
of ideas for how it‘s used. But what about if you‘re just starting out with the language and
want to become a Python developer?
Below, we‘ve outlined some Python project ideas for beginners. These can help you develop
your knowledge and challenge your abilities with the programming language:
 Build a guessing game
 Design a text-based adventure game
 Create a simple Python calculator
 Write a simple, interactive quiz
 Build an alarm clock
Once you‘ve mastered the basics of Python, each of these can challenge you and help you
hone the skills you‘ve already learned.
IBM SPSS
What is SPSS?
While Alchemer has powerful built-in reporting features that are easy to use and present for
most online surveys, NPS survey, and employee satisfaction surveys, when it comes to in-
depth statistical analysis most researchers consider SPSS the best-in-class solution.
SPSS is short for Statistical Package for the Social Sciences, and it‘s used by various kinds of
researchers for complex statistical data analysis. The SPSS software package was created for
the management and statistical analysis of social science data. It was originally launched in
1968 by SPSS Inc., and was later acquired by IBM in 2009.
Officially dubbed IBM SPSS Statistics, most users still refer to it as SPSS. As the world
standard for social-science data analysis, SPSS is widely coveted due to its straightforward
and English-like command language and impressively thorough user manual.

23. SPSS is used by market researchers, health researchers, survey companies, government
entities, education researchers, marketing organizations, data miners, and many more for
processing and analyzing survey data, such as you collect with an online survey platform like
Alchemer.
Most top research agencies use SPSS to analyze survey data and mine text data so that they
can get the most out of their research and survey projects.
The Core Functions of SPSS
SPSS offers four programs that assist researchers with your complex data analysis needs.
Statistics Program
SPSS‘s Statistics program provides a plethora of basic statistical functions, some of which
include frequencies, cross-tabulation, and bivariate statistics.
Modeler Program
SPSS‘s Modeler program enables researchers to build and validate predictive models using
advanced statistical procedures.
Text Analytics for Surveys Program
SPSS‘s Text Analytics for Surveys program helps survey administrators uncover powerful
insights from responses to open-ended survey questions.
Visualization Designer
SPSS‘s Visualization Designer program allows researchers to use their data to create a wide
variety of visuals like density charts and radial boxplots from their survey data with ease.
In addition to the four programs mentioned above, SPSS also provides solutions for data
management, which allow researchers to perform case selection, create derived data, and
perform file reshaping.
SPSS also offers data documentation, which allows researchers to store a metadata
dictionary. This metadata dictionary acts as a centralized repository of information pertaining
to the data, such as meaning, relationships to other data, origin, usage, and format.
There are a handful of statistical methods that can be leveraged in SPSS, including:

24.  Descriptive statistics, including methodologies such as frequencies, cross-tabulation, and
descriptive ratio statistics.
 Bivariate statistics, including methodologies such as analysis of variance (ANOVA),
means, correlation, and nonparametric tests.
 Numeral outcome prediction such as linear regression.
 Prediction for identifying groups, including methodologies such as cluster
analysis and factor analysis.
The Benefits of Using SPSS for Survey Data Analysis
Thanks to its emphasis on analyzing statistical data, SPSS is an extremely powerful tool for
manipulating and deciphering survey data.
The data from any online survey collected can be exported to SPSS for detailed analysis.
Exporting survey data from Alchemer to SPSS‘s proprietary .SAV format makes the process
of pulling, manipulating, and analyzing data clean and easy. Using the .SAV format, SPSS
automatically sets up and imports the designated variable names, variable types, titles, and
value labels, making the process much easier on researchers.
Once survey data is exported to SPSS, the opportunities for statistical analysis are practically
endless.
In short, remember to use SPSS when you need a flexible, customizable way to get super
granular on even the most complex data sets. This gives you, the researcher, more time to do
what you do best — identifying trends, developing predictive models, and drawing informed
conclusions.
SPSS AMOS
IBM SPSS Amos is powerful structural equation modelling software that enables you to
support your research and theories by extending standard multivariate analysis methods,
including regression, factor analysis, correlation, and analysis of variance. With SPSS Amos
you can build attitudinal and behavioural models that reflect complex relationships more

25. accurately than with standard multivariate statistics techniques using either an intuitive
graphical, or programmatic user interface.
Quickly build graphical models using IBM SPSS Amos‘ simple drag-and-drop drawing tools.
Models that used to take days to create are just minutes away from completion. And once the
model is finished, simply click your mouse and assess your model‘s fit. Then make any
modifications and print a presentation-quality graphic of your final model.
What is structural equation modelling?
When you conduct research, you‘re probably already using factor and regression analyses in
your work. Structural equation modelling (SEM) can take your research to the next level.
SEM (sometimes called path analysis) helps you gain additional insight into causal models
and explore the interaction effects and pathways between variables. SEM lets you more
rigorously test whether your data supports your hypothesis. You create more precise models –
setting your research apart and increasing your chances of getting published.
IBM SPSS Amos makes structural equation modelling (SEM) easy and accessible
 IBM SPSS Amos builds models that more realistically reflect complex relationships
because any numeric variable, whether observed (such as non-experimental data from
a survey) or latent (such as satisfaction and loyalty) can be used to predict any other
numeric variable.
 Quickly build graphical models using IBM SPSS Amos‘ simple drag-and-drop
drawing tools. Models that used to take days to create are just minutes away from

26. completion. And once the model is finished, simply click your mouse and assess your
model‘s fit. Then make any modifications and print a presentation-quality graphic of
your final model.
 Its approach to multivariate analysis encompasses and extends standard methods –
including regression, factor analysis, correlation and analysis of variance. New
capabilities include bootstrapping of user-defined functions of the model parameter
for increased model stability.
 A non-graphical, programmatic approach, introduced with SPSS Amos 20, improves
accessibility for those who can benefit by specifying models directly. Its scripting
capabilities improve the productivity of users who need to run large, complicated
models, and make it easy to generate many similar models that differ slightly.
What can IBM SPSS Amos do for you?
 On-screen model to results – Create path diagrams of your analysis using drawing
tools, rather than by writing equations or by typing commands.
 Models that best fit your data – Offers exploration techniques, such as structural
equation model specification search, to help choose a model from a large number of
candidates.
 Non-graphical modelling – Provides easy ways for programmers and non-
programmers to specify a structural equation model without drawing a path diagram.
 Find unexpected relationships – After you fit a model, the SPSS Amos path
diagram shows the strength of the relationship between variables.
 Support your research – Extends standard multivariate analysis methods, including
regression, factor analysis, correlation, and analysis of variance