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1. Data analytics
• Data analytics involves combing through massive datasets to
reveal patterns and trends, draw conclusions about
hypotheses, and support business decisions with data-based
insights. Data analysis attempts to answer questions such as,
“What is the influence of geography or seasonal factors on
customer preferences?” or “What is the likelihood a
customer will defect to a competitor?”. The practice of data
analytics encompasses many diverse techniques and
approaches and is also frequently referred to as data science,
data mining, data modeling, or big data analytics

2. data analysis
• data analysis is the process of collecting, analyzing, and reporting to
managers useful information to help them gain better insights, make
strategic decisions, achieve major goals, and solve complex problems.

3. A typical data analysis generally focuses on 6
special areas, such as:
1. Competitive intelligence is the gathering, analyzing, and use of
information collected on competitors, customers, and other market
factors that contribute to a business's competitive advantage. This data
is then used to improve marketing efforts, strategic planning, finance,
and operations.
2. Financial analysis involves using financial data to assess a company's
viability, stability, and profitability. This data is taken from reports and
financial statements including balance sheets, income statements, cash
flow statements, and statements of shareholders' equity.

4. 3. Market research involves a set of techniques used to gather
information and better understand a company's target market,
including their needs and demographics. This information is then used
to determine if a product or service is viable, spot potential gaps in the
market, and identify competitors.
4. Risk management aims to identify, evaluate, and manage potential
threats to a business, including financial uncertainty, legal liabilities,
strategic management errors, accidents, and more.

5. 5. Strategic analysis is the process of gathering data that helps a
company’s management decide on priorities and goals to determine a
long-term strategy for the business. The analysis examines a company’s
vision, mission statement, values, and external and internal
environment to devise this strategy.
6. Stakeholder analysis is a process for identifying, prioritizing, and
understanding potential stakeholders with the aim of winning their
support of a business. This analysis allows a company or team members
to determine whose interests should be taken into account when
developing and/or implementing a product, policy, or program.

6. Why Is Data Analytics Important?
• Data analytics is important because it helps businesses optimize their
performances. Implementing it into the business model means
companies can help reduce costs by identifying more efficient ways of
doing business. A company can also use data analytics to make better
business decisions and help analyze customer trends and satisfaction,
which can lead to new—and better—products and services.

7. Who Is Using Data Analytics?
• Data analytics has been adopted by several sectors, such as the travel
and hospitality industry, where turnarounds can be quick. This
industry can collect customer data and figure out where the
problems, if any, lie and how to fix them. Healthcare is another sector
that combines the use of high volumes of structured and
unstructured data and data analytics can help in making quick
decisions. Similarly, the retail industry uses copious amounts of data
to meet the ever-changing demands of shoppers.

8. Roles and responsibilities of a data analyst
• 1. Data Collection
• 2. Data Cleaning and Preprocessing
• 3. Data Analysis: Applying statistical methods and data visualization techniques to
analyze data sets and extract meaningful insights.
• 4. Data Interpretation and Reporting: Summarizing findings and presenting them to
stakeholders using reports, dashboards, or presentations.
• 5. Database Querying:
• 6. Data Visualization: Creating visual representations of data (charts, graphs,
dashboards) to facilitate understanding of trends and patterns.
• 7. Statistical Analysis: Using statistical tools and techniques to interpret data and test
hypotheses.
• 8. Machine Learning and Predictive Modeling: Developing and implementing
algorithms to make predictions and solve problems.

9. • 9. Collaboration: Working with cross-functional teams such as business analysts, engineers, and
stakeholders to understand data requirements and deliver actionable insights.
• 10.Continuous Learning: Keeping up-to-date with trends, tools, and techniques to improve
analytical capabilities and effectiveness.
• 11.Data Governance: Adhering to data privacy and security protocols, ensuring compliance with
regulations like GDPR or CCPA.
• 12.Problem Solving: Identifying and solving complex data-related problems using analytical and
critical thinking skills.
• 13. Communication: Clearly communicating findings, methodologies, and implications to non-
technical stakeholders.
• 14. Tool Proficiency: Being proficient in data analysis tools such as Excel, R, Python, Tableau,
Power BI, etc., depending on the organization's preferences and requirements.
• 15. Project Management: Managing timelines, priorities, and resources effectively to deliver
projects on time.
• These responsibilities may vary depending on the organization and the specific role within the
field of data analysis.

10. Data Analysis Steps
• 1. The first step is to determine the data requirements or how the
data is grouped. Data may be separated by age, demographic, income,
or gender. Data values may be numerical or be divided by category.
• 2. The second step in data analytics is the process of collecting it.
This can be done through a variety of sources such as computers,
online sources, cameras, environmental sources, or through personnel.
• 3. Once the data is collected, it must be organized so it can be
analyzed. This may take place on a spreadsheet or other form of
software that can take statistical data.
• 4. The data is then cleaned up before analysis. This means it is
scrubbed and checked to ensure there is no duplication or error, and
that it is not incomplete. This step helps correct any errors before it
goes on to a data analyst to be analyzed.

11. Types of Data Analytics
• 1. Descriptive analytics: This describes what has happened over a given
period of time. Have the number of views gone up? Are sales stronger this
month than last?
• 2. Diagnostic analytics: This focuses more on why something happened.
This involves more diverse data inputs and a bit of hypothesizing. Did the
weather affect beer sales? Did that latest marketing campaign impact sales?
• 3. Predictive analytics: This moves to what is likely going to happen in
the near term. What happened to sales the last time we had a hot summer?
How many weather models predict a hot summer this year?
• 4. Prescriptive analytics: This suggests a course of action. If the
likelihood of a hot summer is measured as an average of these five weather
models is above 58%, we should add an evening shift to the brewery and
rent an additional tank to increase output.

12. Data Analytics Techniques
• • Regression analysis entails analyzing the relationship between dependent
variables to determine how a change in one may affect the change in another.
• • Factor analysis entails taking a large data set and shrinking it to a smaller data
set. The goal of this maneuver is to attempt to discover hidden trends that would
otherwise have been more difficult to see.
• • Cohort analysis is the process of breaking a data set into groups of similar data,
often broken into a customer demographic. This allows data analysts and other users of
data analytics to further dive into the numbers relating to a specific subset of data.
• • Monte Carlo simulations model the probability of different outcomes happening.
Often used for risk mitigation and loss prevention, these simulations incorporate
multiple values and variables and often have greater forecasting capabilities than other
data analytics approaches.
• • Time series analysis tracks data over time and solidifies the relationship between
the value of a data point and the occurrence of the data point. This data analysis
technique is usually used to spot cyclical trends or to project financial forecasts.

13. Business analytics examples and tools
1. • Dundas BI, with automated trend forecasting and a user-friendly interface;
2. • Knime Analytics Platform, which has high-performance data pipelining and
machine learning;
3. • Qlik's QlikView with data visualization and automated data association
features;
4. • Sisense, known for its dynamic text-analysis features and data warehousing;
5. • Splunk, which has intuitive user interface and data visualization features;
6. • Tableau, which has advanced unstructured text analysis and natural language
processing capabilities; and
7. • Tibco Spotfire, which offers powerful, automated statistical and unstructured
text analysis

14. Benefits of Data-Driven Decision Making
• 1. Increasing transparency and accountability
• One of the benefits of the data-driven decision-making approach is
increased transparency and accountability of the organization. DDDM
aims to improve teamwork and employee engagement. This is how the
organization deals with threats and risks, improving overall
performance. It leads to making the right decisions about their
operations.
• There are fewer mistakes because misunderstandings are less likely to
occur. Employees know exactly what’s going on, and what their role
is, and are more likely to suggest improvements and changes. All
because they understand the current state of the business and long-
term goals.

15. • Objective data helps organizations collect data, use it for record-
keeping and compliance, and be accountable for managing it
correctly. Therefore, data-driven decision-making in business ensures
that every piece of information is prioritized and the goal is specific.

16. 2. Continuous improvement
• Data-driven decision-making leads to continuous improvement of the
organization. They gradually implement changes, monitor metrics,
and make further changes based on the results. This increases the
overall productivity and effectiveness of the organization.

17. 3. Increases consistency
• The use of data in decision-making processes ensures that the
business agrees on results. This approach helps people understand
how decisions are made. They can determine the implications of the
data being collected and analyzed, and take appropriate action. When
everyone participates in data-driven decision management, they gain
the necessary skills and thereby increase consistency. Practice plays a
vital role in every business. This is how workers can understand if
sales are up or down or if customers are happy. In this way the
company stays informed, constantly developing loyalty, engagement,
and accountability.

18. 4. Cost saving
5. Flexibility and quick adaptation
• Predicting market trends and responding quickly will give a business
an edge over its competitors. An organization that researches the
market and provides a marketable product is considered an industry
leader. Once a company receives and analyzes data, it makes
decisions. Truly agile organizations are more likely to achieve high
financial performance than the average business.

19. 6. Feedback for research
Data-driven decision-making provides feedback that gives insight into
what customers like and don’t like. It’s how organizations create new
products and services, plus it helps identify trends before they happen.
By studying data, companies learn what to expect shortly and what to
change to improve performance. In this way, companies maintain a
good relationship with their customers.

20. How to Use Data to Make Business Decisions
• 1. Define the goal
• 2. Data search and preparation
• 3. Data review and development plan creation
• 4. How can Data Analysis Influence Decision Make?

21. Examples of How Companies Use Analytics
• 1. Google
• Google is paying attention to what it calls “people analytics.” In one of
its people analytics initiatives, Project Oxygen, Google mined data
from 10,000 performance reviews and compared it to employee
retention rates. The company used the information to identify the
general behavior of effective managers and created training programs
to develop competencies.

22. 2. Amazon
• The company has access to a huge amount of its customers’ data, such as
names, addresses, payments, and search history. Amazon uses this
information to improve customer relationships and serve customers faster
and more efficiently.
3. Netflix
• The service has a lot of data and analytics to understand the viewing habits
of international consumers. They use the data to order original
programming content that appeals worldwide. Also to buy the rights to
movies and TV series.
• So actor Adam Sandler has proven to be unpopular in the U.S. and U.K. in
recent years. Netflix released four new movies with the actor in 2015, as
previous work with the actor was successful in Latin America.

24. Types of Data
• Qualitative Data Type
 Nominal
 Ordinal
• Quantitative Data Type
 Discrete
 Continuous

25. Qualitative Data Type
• Qualitative or Categorical Data describes the object under
consideration using a finite set of discrete classes. It means that this
type of data can’t be counted or measured easily using numbers and
therefore divided into categories. The gender of a person (male,
female) is a good example of this data type.
• These are usually extracted from audio, images, or text medium.
Another example can be of a smartphone brand that provides
information about the current rating, the color of the phone, category
of the phone, and so on. All this information can be categorized as
Qualitative data. There are two subcategories under this:

26. Nominal
• These are the set of values that don’t possess a natural ordering. Let’s
understand this with some examples. The color of a smartphone can be
considered as a nominal data type as we can’t compare one color with
others.
• It is not possible to state that ‘Red’ is greater than ‘Blue’. The gender
of a person is another one where we can’t differentiate between male,
female, or others. Mobile phone categories whether it is midrange,
budget segment, or premium smartphone is also nominal data type.
• Nominal data types in statistics are not quantifiable and cannot be
measured through numerical units. Nominal types of statistical data
are valuable while conducting qualitative research as it extends
freedom of opinion to subjects.

27. Ordinal
• These types of values have a natural ordering while maintaining their class
of values. If we consider the size of a clothing brand then we can easily sort
them according to their name tag in the order of small < medium < large. The
grading system while marking candidates in a test can also be considered as
an ordinal data type where A+ is definitely better than B grade.
• These categories help us deciding which encoding strategy can be applied to
which type of data. Data encoding for Qualitative data is important because
machine learning models can’t handle these values directly and needed to be
converted to numerical types as the models are mathematical in nature.
• For nominal data type where there is no comparison among the categories,
one-hot encoding can be applied which is similar to binary coding considering
there are in less number and for the ordinal data type, label encoding can be
applied which is a form of integer encoding.

28. Quantitative Data Type
• This data type tries to quantify things and it does by considering
numerical values that make it countable in nature. The price of a
smartphone, discount offered, number of ratings on a product, the
frequency of processor of a smartphone, or ram of that particular phone,
all these things fall under the category of Quantitative data types.
• Discrete
• The numerical values which fall under are integers or whole numbers are
placed under this category. The number of speakers in the phone,
cameras, cores in the processor, the number of sims supported all these
are some of the examples of the discrete data type.

29. • Continuous data is data that can take any value. Height,
weight, temperature and length are all examples of
continuous data. Some continuous data will change over
time; the weight of a baby in its first year or the temperature
in a room throughout the day.

30. Types of data sources:
• 1.Primary data:
• The data which is Raw, original, and extracted directly from the official
sources is known as primary data. This type of data is collected
directly by performing techniques such as questionnaires, interviews,
and surveys. The data collected must be according to the demand and
requirements of the target audience on which analysis is performed
otherwise it would be a burden in the data processing.

31. Few methods of collecting primary data:
• 1. Interview method:
• The data collected during this process is through interviewing the target
audience by a person called interviewer and the person who answers the
interview is known as the interviewee. Some basic business or product
related questions are asked and noted down in the form of notes, audio, or
video and this data is stored for processing. These can be both structured
and unstructured like personal interviews or formal interviews through
telephone, face to face, email, etc.
• 2. Survey method: The survey method is the process of research where a list
of relevant questions are asked and answers are noted down in the form of
text, audio, or video. The survey method can be obtained in both online and
offline mode like through website forms and email. Then that survey answers
are stored for analyzing data. Examples are online surveys or surveys through
social media polls.

32. • 3. Observation method:
• The observation method is a method of data collection in which the
researcher keenly observes the behavior and practices of the target
audience using some data collecting tool and stores the observed
data in the form of text, audio, video, or any raw formats. In this
method, the data is collected directly by posting a few questions on
the participants. For example, observing a group of customers and
their behavior towards the products. The data obtained will be sent
for processing.

33. 4. Experimental method:
• The experimental method is the process of collecting data through performing experiments,
research, and investigation. The most frequently used experiment methods are CRD, RBD, LSD,
FD.
• • CRD- Completely Randomized design is a simple experimental design used in data
analytics which is based on randomization and replication. It is mostly used for comparing the
experiments.
• • RBD- Randomized Block Design is an experimental design in which the experiment is
divided into small units called blocks. Random experiments are performed on each of the blocks
and results are drawn using a technique known as analysis of variance (ANOVA). RBD was
originated from the agriculture sector.
• • LSD – Latin Square Design is an experimental design that is similar to CRD and RBD blocks
but contains rows and columns. It is an arrangement of NxN squares with an equal amount of
rows and columns which contain letters that occurs only once in a row. Hence the differences
can be easily found with fewer errors in the experiment. Sudoku puzzle is an example of a Latin
square design.
• • FD- Factorial design is an experimental design where each experiment has two factors
each with possible values and on performing trail other combinational factors are derived.

34. 2. Secondary data:
• Secondary data is the data which has already been collected and reused again for some valid purpose. This
type of data is previously recorded from primary data and it has two types of sources named internal source
and external source.
• Internal source:
• These types of data can easily be found within the organization such as market record, a sales record,
transactions, customer data, accounting resources, etc. The cost and time consumption is less in obtaining
internal sources.
• External source:
• The data which can’t be found at internal organizations and can be gained through external third party
resources is external source data. The cost and time consumption is more because this contains a huge
amount of data. Examples of external sources are Government publications, news publications, Registrar
General of India, planning commission, international labor bureau, syndicate services, and other non-
governmental publications.

35. Other sources:
 Sensors data: With the advancement of IoT devices, the sensors of these
devices collect data which can be used for sensor data analytics to track the
performance and usage of products.
 Satellites data: Satellites collect a lot of images and data in terabytes on daily
basis through surveillance cameras which can be used to collect useful
information.
 Web traffic: Due to fast and cheap internet facilities many formats of data
which is uploaded by users on different platforms can be predicted and collected
with their permission for data analysis. The search engines also provide their
data through keywords and queries searched mostly.

36. What is data quality?
• Data quality measures how well a dataset meets criteria for accuracy,
completeness, validity, consistency, uniqueness, timeliness, and
fitness for purpose, and it is critical to all data governance initiatives
within an organization. Data quality standards ensure that companies
are making data-driven decisions to meet their business goals. If data
issues, such as duplicate data, missing values, outliers, aren’t properly
addressed, businesses increase their risk for negative business
outcomes. According to a Gartner report, poor data quality costs
organizations an average of USD 12.9 million each year1. As a result,
data quality tools have emerged to mitigate the negative impact
associated with poor data quality.

37. A list of popular data quality characteristics
and dimensions include:
1. Accuracy
2. Completeness
3. Consistency
4. Integrity
5. Reasonability
6. Timeliness
7. Uniqueness/Deduplication
8. Validity
9. Accessibility

38. Data collection methods
1. Surveys and Questionnaires: Gathering data directly from individuals or organizations
through structured questions designed to elicit specific information.
2. Interviews: Conducting one-on-one or group interviews to collect qualitative data,
opinions, or detailed insights.
3. Observational Data: Collecting data by observing behaviors, actions, or events in real-
time or through recorded observations.

39. 4. Transaction Data: Capturing data generated through transactions, such as sales
records, financial transactions, or customer interactions.
5. Web Scraping: Extracting data from websites using automated tools or scripts to
gather information not readily available through APIs.
6. Sensor Data: Collecting data from sensors embedded in devices or equipment, such
as IoT devices, to monitor and analyze physical or environmental conditions.
7. Social Media Data: Gathering data from social media platforms to analyze trends,
sentiment analysis, or customer feedback.

40. 8. Publicly Available Data: Utilizing data from publicly accessible sources such as
government databases, open data repositories, or research publications.
9. APIs (Application Programming Interfaces): Retrieving data from web services or
databases using APIs provided by software applications or platforms.
10. Internal Databases and Data Warehouses: Accessing structured data stored in
organizational databases or data warehouses that consolidate information from multiple
sources.
11. Mobile Apps and Devices: Collecting data from mobile applications or devices, such
as geolocation data, usage patterns, or health-related metrics.