This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.

1. [BIG DATA ANALYTICS]

2. ASSIGNMENT
1. What is Business Analytics? And explain its different types.
The combination of skills, technologies, and practices used to analyze an organization's data and results in
order to obtain knowledge and make data-driven decisions in the future using predictive analysis is
known as business analytics (BA). BA's aim is to determine which datasets are useful and which can
boost sales, productivity, and performance.
When used correctly, BA can be used to reliably forecast future events involving customer behavior,
industry dynamics, and the development of more effective processes that could result in increased
revenue.
Basically business analytics used to:
 Analyse data from a variety of sources. This could be anything from cloud applications to
marketing automation tools and CRM software.
 Use advanced analytics and statistics to find patterns within datasets. These patterns can help you
predict trends in the future and access new insights about the consumer and their behaviour.
 Monitor KPIs and trends as they change in real-time. This makes it easy for businesses to not
only have their data in one place but to also come to conclusions quickly and accurately.
Information can be organized, dissected, and absorbed in a way that allows you to come up with solutions
to whatever problems you're facing.

3. Descriptive analytics is the process of interpreting historical data and KPIs in order to spot trends and
patterns. Using data aggregation and data mining methods, this allows for a big picture view of what
occurred in the past and what is happening now.
Many businesses use descriptive analytics to gain a better understanding of consumer behaviour and how
to tailor marketing campaigns to those customers.
Diagnostic analytics: Looks back at past results to see what factors affect those patterns. Drill-down, data
discovery, data mining, and inference are used to uncover the cause of particular incidents. Algorithms for
classification and regression are used until an understanding of the probability of an occurrence and why
it might happen has been achieved.
Predictive analytics employs mathematical models and machine learning methods to predict and
determine potential outcomes. The findings of descriptive analytics are often used to construct models
that predict the probability of particular outcomes.
Sales and marketing teams often use this type to forecast consumer views based on social media data.
Prescriptive analytics: Recommends how to manage similar scenarios in the future based on past
performance results. This form of business analytics can not only predict outcomes, but it can also suggest
concrete steps that must be taken in order to achieve the best possible outcome. Deep learning and
complex neural networks are often used to accomplish this.

4. 2. Give an overview of Cognitive Analytics.
Cognitive Analytics
Cognitive analytics is a data-driven technique that begins and ends with the knowledge itself.
This one-of-a-kind approach to all forms of data (at any scale) uncovers relations, trends, and
collocations that provide unparalleled, even unforeseen insight.
Cognitive analytics makes use of intelligent technology to put all of these data sources within
scope of decision-making and business intelligence analytics processes.
Cognitive analytics applies human-like knowledge to such activities, such as comprehending not
only the words in a document, but the entire meaning of what is written or spoken, or identifying
objects in a picture inside vast volumes of data. To do this, cognitive analytics combines a range
of intelligent technologies, including semantics and artificial intelligence algorithms.
Cognitive analytics, when used in the enterprise, can help to bridge the gap between vast
amounts of data and the need to make decisions in real time. A broad understanding of
information enables businesses to draw on a wide range of information sources in their
knowledge base to enhance enterprise knowledge, strategic positioning, and provide a deep and
personalized experience.
The cognitive analytics method follows this sequence of procedures, according to Xenonstack
Insights' Quick Guide to Cognitive Analytics Tools and Architecture

5.  It searches the entire available “knowledge base” to locate real-time data.
 It collects and makes real-time data sources such as text, images, audio, and video
available to advanced analytics tools for decision-making or business intelligence (BI).
 It mimics the human brain to study and learn from the available data to extract actionable
insights, hidden behind “data patterns.”
 While following this process, the system often combines techniques of AI, ML, DL,
neural network, and semantics.

6. 3. Short Note
 Sentiment Analytics
 Data Visualization
Sentiment Analytics
Sentiment analysis (also known as opinion mining) is a natural language processing technique for
determining the positive, negative, or neutral nature of data. Sentiment analysis is often used on textual
data to assist companies in tracking brand and product sentiment in consumer reviews and better
understanding customer needs.
Basically sentiment analysis is becoming an important method to track and appreciate consumer
sentiment as they share their thoughts and feelings more freely than ever before.
Models of sentiment analysis concentrate on polarity (positive, negative, and neutral), as well as feelings
and emotions (angry, happy, sad, etc.), urgency (urgent, not urgent), and even intentions (interested v. not
interested). You can identify and customize your categories to suit your sentiment analysis needs,
depending on how you want to view consumer reviews and questions.

7. Fine-grained Sentiment Analysis
Unless polarity precision is essential to your company, you should consider adding the following polarity
categories to your polarity categories:
 Very positive
 Positive
 Neutral
 Negative
 Very negative
Basically referred as fine-grained sentiment analysis, and could be used to interpret 5-star ratings in a
review, for example:
 Very Positive = 5 stars
 Very Negative = 1 star
Emotion detection
In this sentiment analysis is used to identify emotions such as happiness, annoyance, rage, sorrow, and so
on. Many emotion recognition systems rely on lexicons (lists of words and the emotions they evoke) or
sophisticated machine learning algorithms.
Aspect-based Sentiment Analysis
While evaluating text sentiments, such as product reviews, you'll want to know which specific features or
aspects people are referencing in a positive, neutral, or negative light. That's where aspect-based
sentiment analysis can help. For example, in the sentence "This camera's battery life is too short," an
aspect-based classifier might decide that the sentence expresses a negative sentiment.
Multilingual sentiment analysis
Sentiment analysis in several languages can be challenging. It necessitates a significant amount of pre-
processing and energy. The majority of these tools are available online (for example, sentiment lexicons),
while others must be developed (for example, noise detection algorithms), so you'll need to know how to
code to use them.
Therefore, sentiment analysis is important since it allows companies to easily consider their consumers'
overall views. We can make quicker and more insightful decisions by automatically sorting the sentiment
behind ratings, social media conversations, and more.
Benefits of sentiment analysis include:
 Sorting Data at Scale
Can you imagine going through thousands of tweets, customer service conversations, or survey
responses by hand? There is just too much business data to manually process. Sentiment analysis
aids companies in efficiently and cost-effectively processing large volumes of data.

8.  Real-Time Analysis
Sentiment analysis can detect crucial issues in real time, such as if a social media PR crisis is
worsening. Is a disgruntled customer going to leave? Sentiment analysis models will assist you in
quickly identifying these types of circumstances so that you can take decisive action.
 Consistent criteria
When it comes to assessing the sentiment of a document, it's estimated that only 60-65 percent of
the time people agree. Text emotion tagging is a highly subjective process that is informed by
personal perceptions, emotions, and beliefs. Companies may apply the same criterion to all of
their data by using a centralized sentiment analysis framework, which helps them increase
consistency and obtain deeper insights.
Data Visualization
The virtual representation of your data is known as data visualisation. These tools offer an easy and
comprehensible way to clearly see and quickly discover insights and patterns in your data using charts,
maps, and other graphical elements.
Data-driven graphics draw in more focus, are easier to comprehend, and help you get the message across
quickly to your audience. Even the most complex details can be made transparent and understandable
with the aid of informative graphics and dashboards. Visual learners make up the majority of the
population. So, if anyone wants the majority of his or her partners, friends, and clients to be able to
connect with the data, he or she should.

9. The advantages and benefits of data visualization:
 Fast decision-making. Summing up data is easy and fast with graphics, which let you quickly
see that a column or touch point is higher than others without looking through several pages of
statistics in Google Sheets or Excel.
 More people involved. Most people are better at perceiving and remembering information
presented visually.
 Higher degree of involvement. Beautiful and bright graphics with clear messages attract
readers’ attention.
 Better understanding. Perfect reports are transparent not only for technical specialists, analysts,
and data scientists but also for CMOs and CEOs, and help each and every worker make decisions
in their area of responsibility.
The most popular types of charts and the goals they can help you achieve.
Line chart
A line graph depicts how one or more variables shift over time. This form of graph is useful for
comparing changes over time within data sets.
Bar Chart

10. The bar chart is another diagram that’s perfectly suited for comparing data sets. Horizontal bar charts are
often used when you need to compare lots of data sets or to visually emphasize the distinct advantage of
one of the data sets. Vertical bar charts display how data points change over time.
For example, how the annual company profit has changed over the past few years.
Histogram
A histogram is often mistaken for a bar chart due to their visual similarities, but the goals of these charts
are different. A histogram shows the distribution of a data set across a continuous interval or a definite
time period. On the vertical axis of this chart, you can see frequency, whereas on the horizontal you can
see time intervals.
Pie chart

11. The pie chart displays shares of each value in a data set. It’s used to show the components of any
data set. For instance, what percentage of general sales is attributed to each product category?
Scatter plot
The scatter plot shows the connection between data points. For example, with the help of a
scatter plot, you can find out how the conversion rate changes depending on the size of the
product discount.
Bubble chart

12. This is an interesting chart that allows you to compare two parameters by means of a third. Let’s
take the conversion rate and discount size from the previous example, add to them revenue
(indicated by circle size), and we’ll get something like the following chart.
Geo chart
The geo chart is a simple one. It’s used when you need to demonstrate a certain distribution
across regions, countries, and continents.

13. 4. Define Artificial intelligence with some examples.
It is the science and engineering of making intelligent machines, particularly intelligent computer
programmers," writes John McCarthy in a 2004 article. It's close to the challenge of using machines to
grasp human intelligence, except AI doesn't have to be limited to biologically measurable methods.
There are four potential goals or definitions of AI, which differentiates computer systems on the basis of
rationality and thinking vs. acting:
Human approach:
 Systems that think like humans
 Systems that act like humans
Ideal approach:
 Systems that think rationally
 Systems that act rationally
Artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-
solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently
mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms
which seek to create expert systems which make predictions or classifications based on input data.

14. Types of artificial intelligence: weak AI vs. strong AI
 Weak AI - also called Narrow AI or Artificial Narrow Intelligence (ANI)
It is artificial intelligence that has been programmed to perform complex tasks. The majority of
today's AI is powered by weak AI. This form of AI is anything but weak; it enables some very
robust applications, such as Apple's Siri, Amazon's Alexa, IBM Watson, and self-driving cars..
 Strong AI
Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI) are two types of
artificial intelligence (ASI). Artificial general intelligence (AGI), also known as general AI, is a
hypothetical type of AI in which a computer has the same intelligence as humans and has the
ability to solve problems, understand, and prepare for the future. Artificial Super Intelligence
(ASI) is a term used to describe artificial intelligence.
Applications of AI Systems ( Examples)
 Speech Recognition is a capability that uses natural language processing (NLP) to convert human
speech into a written format. It is also known as automatic speech recognition (ASR), machine
speech recognition, or speech-to-text. Many mobile devices have speech recognition built in to
perform voice searches (e.g., Siri) or to improve messaging usability.
 Customer service: During the customer journey, online chat bots are replacing human agents.
They provide personalized advice, cross-selling goods, and recommending sizes for users,
changing the way we think about customer interaction across websites and social media
platforms. They address frequently asked questions (FAQs) about topics like delivery, or provide
personalized advice, cross-selling products, or suggesting sizes for users, changing the way we
think about customer engagement across websites and social media platforms. Message bots on e-
commerce sites are one example.
 AI-driven high-frequency trading systems, designed to optimize stock portfolios, make thousands
or even millions of trades every day without human intervention.
 AI algorithms can help uncover data patterns that can be used to create more successful cross-
selling strategies by using past consumption behavior data. This is used by online retailers to
make specific add-on suggestions to consumers during the checkout process.
 Computer vision is an AI technology that allows computers and systems to extract useful
information from digital images, videos, and other visual inputs and take action based on that
information. It differs from image recognition tasks in that it can make suggestions.

15. 5. Explain “Data Driven Decision Making”.
Data-driven decision making, also known as data-driven decision management or data-directed decision
making, is a method of making decisions based on data. Data-driven decision making (DDDM) entails
making decisions that are supported by hard data rather than being intuitive or based solely on
observation. Data-driven decision making has become even more important as business technology has
progressed rapidly in recent years.
Decisions should be extrapolated from key data sets that show their predicted effectiveness and how they
could turn out, according to the concept of data-driven decision making. Businesses typically use a
variety of enterprise resources to gather this information and present it in ways that support their
decisions. This is in stark contrast to how commercial e-commerce decision-making has traditionally been
conducted.
In order to serve this booming demand, companies have come out with self-service data analytics
products – the idea is that self-service products lead to more egalitarian data collection and transfer. In
other words, without self-serve tools, only a skilled data scientist can crunch the numbers and come up
with the data supporting decisions, where with decision support tools that are self-serve, executives and
others who are further from the IT department can do their own analysis and present their own decisions
backed up with the data in question.
Steps for Implementing Data-Driven Decision-Making
Determine Business Questions or Issues
 What does the company want to accomplish?
 Identify the area’s most important to achieving its overall strategy.
 Is the company trying to assess an opportunity or diagnose a problem?
Strategize and Identify Goals
Determine what you can realistically accomplish with data. It’s essential to have a clear analytical
objective.
 Who will oversee the collection and analysis?
 What personnel will you need for the project?

16.  Can in-house employees do the analysis, or will you hire consultants?
Target Data
Identify what data should be collected and how to acquire it. That is
 What specific data is needed to answer the original questions?
Collect and Analyze Data
You will need to put in place the processes and personnel to gather and manage the data. Your company
may already have some of the data it needs in-house. In some cases, it might be possible to purchase
access to an existing data set. Although data is becoming ever cheaper and more plentiful, the cost of
acquiring the right data can still add up. Data can be generated from a variety of sources, such as
computer software, online sources, cameras and imaging platforms, environmental sources, or personnel.
Once the data has been collected, it must be analyzed to glean strategic insights. Common types of
analytical methods include text, speech, and video/image analytics. Many available platforms for big data
analysis are available.
Make Decisions Regarding Findings
 Key decision-makers can then turn analytical insights into actionable ideas and projects.
Basically, Decision support software will now assist in determining how a specific product can perform in
a market, what a consumer will think of a slogan, or where to deploy business capital. As a result, demand
for data-driven decision-making solutions has skyrocketed. According to TechTarget, a report from the
MIT Center for Digital Business shows that companies that use data-driven decision-making are more
effective.