- Data analytics is the process of extracting meaningful insights from raw data through analysis. It involves collecting, cleaning, and transforming data from various sources into useful information that can be understood by humans. - Data analysts collect and process large datasets to identify patterns and relationships that can help solve business problems. Their responsibilities include understanding organizational goals, gathering and cleaning data, analyzing trends, and preparing summary reports using data visualization tools. - The field of data analytics is growing rapidly as more companies generate vast amounts of data. Skilled data analysts are in high demand to help organizations make meaningful insights and decisions from their data.

1. DATA ANALYTICS

2. DATA ANALYTICS

3. WHAT IS DATA ANALYTICS ?
• Analyze means of scrutinized something to find our meaningful conclusion from it.
• Data analytics also work similar. It is process by which useful insight are extracted from raw
data.
• By studying and examining carefully these insight are important for business trends, market
innovation and market trends profit loss report etc.
• Data analytics is the term as the process of extracting meaningful insight such as hidden
patterns, market trends, and customer preferences are done by the study of procured
data.Example converting jig saw puzzle into beautiful pictures.

5. • A data can be structured unstructured or semi
unstructured.
• The process of data analytics incorporate and
collecting data from various sources and cleaning
it and finally transforming it into something
meaningful. That can be understand by human.

6. • This information can be converted into graph and chats which provides precise
result of the analysis.
• Various technologies tools and frame work are used in the analysis process.
• Organization take the benefit of data analytics to convert the raw data into
meaningful insight.
• Therefore there is high requirement of skill data analytics.

7. RAW DATA TO MEANINGFUL INSIGHT

8. JOB ROLE IN DATA ANALYTICS
• •There are many job roles that can be taken up by fresh candidates.
• It is lucrative field as role of data analytics only going to continue to blossom in
the years to come.

10. WHO IS DATA ANALYST ?
• •A data analyst is a professional who works on collecting, processing and
analyzing a large dataset.
• Statical analysis is done on various data.
• Every business generate data be it marketing ,sales ,research ,customer
feedback, customer behavior, logistic and transportation.
• A data analyst will take this data and take various measures such as how to price
new product, how to cost cutting ,how to innovate better products.

11. DATA ANALYST DEAL WITH
• Data handling
• Data modelling
• Data reporting

12. DATA HANDLING
• Data handling is the process of ensuring that research data is stored,
archived or disposed off in a safe and secure manner during and after the
conclusion of a research project

13. DATA MODELING
• Data modeling is the process of creating a simplified diagram of a software
system and the data elements it contains, using text and symbols to
represent the data and how it flows.

14. DATA REPORTING
• Data reporting is the process of collecting and formatting raw data and
translating it into a digestible format to assess the ongoing performance of
your organization.

15. RESPONSIBILITIES OF DATA ANALYTICS
• Mining
• Data mining is the process of sorting through large data sets to identify patterns
and relationships that can help solve business problems through data analysis.
• Data is mine from various sources and then organized in order to obtain a new
information from it.It is vital role of data analyst.
• Data analyst collect data from various sources and work on it.
• Now with this data we can use model for it and reduce complexity and increase the
efficiency of whole system.

16. RESPONSIBILITIES OF DATA ANALYTICS
Understanding organization goal
• Discovering and identify the company goal by working closely with various other
team.
• It help streamlining and planning the analysis process accordingly.
• Data analytics assist the available resources and understand the business
problem and gather the right data this step is done by collaborating with data
scientist ,programmers and team members.

17. • Gather information
• Gather information form querying and also maintain and design database.
• Data analytics write complex SQL queries and script to gather and extract
information
• From several databases and data warehouses.
RESPONSIBILITIES OF DATA ANALYTICS

18. RESPONSIBILITIES OF DATA ANALYTICS
• Filter and clean data
• This step includes data cleaning and data wrangling .
• Data wrangling is the process of cleaning and unifying messy and complex
data sets for easy access and analysis.
• The data collected is generally unstructured and it has lot of missing values.
• It is important to clean data to ready for analysis.

19. TOOLS
• Use various statical tools and programming language for analytical and logical
examination of data.
• Using different libraries and packages data analysis discover trends and patten
from complex data set.This help them to find more unseen insight from the data to
make business predictions.

20. PREPARER SUMMARY REPORT
• Prepare summary report for the leadership team.
• This is done with the help of data visualization.
• So that they can make timely decisions.
• Data analytics use multiple data visualization tools.

21. INTERACT
• Interact with management team development team and data scientist for process
improvement plan.

22. SKILL REQUIRED
• Hold a degree in any relevant field and domain expertise.
• Knowledge of language of language like R ,Python and java script.
• This will help you solve complex problem
• You should have experience with data bases and data analysis tools.
• Knowledge of MS Excel ,Matlab SQL queries etc.
• Should have understanding of statistics and machine learning.
• Experience of using several data visualization tools.
• Good presentation skills.

23. AVERAGE ANNUAL SALARY
• In US 45000 $ - 83000$
• In India Rs 100000 – Rs 1000000

24. COMPANIES HIRING DATA ANALYTICS
• Amazon
• Microsoft
• Wall mart
• Paytm
• Google
• Facebook
• apple

25. GROWTH OF DATA
• With the rise of various social media platform and multinational companies across
the globe the generation of data has increased
• Data has grown vastly on the last decade and expected to reach 175 zeta bite in
2025 according to international data corporation.

26. GROWTH OF DATA
• Organization across the world generate countless data every second
• This data can be in the form of financial report ,customer data and sales report
and more.
• Companies utilized this data in wise way they use all of this information to make
crucial decision.
• As you have heard data is new oil but its only possible it they use data very well.

27. GROWTH OF DATA
• Companies are on the lookout of professional who turns raw data into crucial
insight. Hence there is and there will be a constant demand for professional in
this field.
• Organization are lookout for such candidates.

28. TO HELP WE HAVE DATA ANALYTICS

29. DATA NEVER SLEEPS
• The limitless generation of data is only going to increase in the future the data
analytics is the integral part of every company.
• This process is going to increase in future with invent of new technologies.

30. ROLES OF DATA ANALYTICS

31. DATA ANALYST
• A data analyst collect, processes and perform analysis on large datasets. They
deal with data handling modelling and reporting.
• Responsibilities
• Recognized and understand the organization goal.
• Gathering information from dataset through queries.
• Filter and clean data.
• Identified analyze and interpret trends in complex datasets.this is done with the
help of various statistical tools.

32. DATA ANALYST
• Prepare summarize report to the leadership team.this is done with the help of
data visualization.

33. SKILLS TO BECOME DATA ANAYLIS
• A bachelor degree
• Understanding of programming languages pyhton R
• Understanding of tools like excel ,Tablueau,Power Bi
• Basic knowledge of machine learning
• Good working knowledge of various data visualization tools along with
presentation skills.

34. AVERAGE SALARY
• Rs 523000 in India
• $62000 in USA.

35. BUSINESS ANALYST
• A business analyst help guide business improving product service and software
through data driven solutions.
• A business analyst are responsible for creating new models that support business
decision and help to optimize cost.
• A business analyst analyze the business domain and understand the business
problem. They provide technology based solutions.

36. RESPONSIBILITIES
• Understand and clarify the business objective.
• Interact with the development team to design the layout of the software
application.
• Run meeting with the stakeholders and other authorities.
• Ensure that the project is running as per the design user accepted testing.They
ensure that the project run smoothly as per design.
• Ensure that all the feature are incorporated in the software.

37. SKILLS OF BA
• A bachelor degree
• Good in writing SQL queries.
• Statistics analysis and predictive modelling
• Understanding of programming languages python R
• Good working knowledge of various data visualization tools along with
presentation skills.

38. SALARY OF BA
• Rs 700000 in India
• $ 72000 in USA

39. DATABASE ADMINISTRATOR
• DBA are the professional responsible for storing and organizing company’s data.
• They do this with the help of various technologies.
• Keep the organization data security.

40. RESPONSIBILITIES
• DBA work on database design and development.
• DBA maintain the integrity of database.
• Run test and modify the existing databases. Inform end user for the changes.
• Liaising with programmer and other IT staff.
• DBA responsible for data backup

41. SKILL
• Bachelor degree in computer science .
• 3 to 5 database management system.
• Knowledge of database design queries.
• Understanding of operating system and storage.

42. SALARY
• Rs 497000 in India
• $78000 in USA.

43. DATA ENGINEER
• A data engineer build and test scalable system .Big data ecosystem for business
.A data engineer is intermediatory between data analyst and data scientist.
• The data engineer transfer data into useful format for analysis.

44. RESPONSIBILITIES
• Develop test and maintain architecture .
• Align architecture with the business environment.
• Managing optimizing and monitoring data retrieval and storage and distribtion
through out the organization.
• They discover opportunities data acquisition ,find trends in data set and develop
algorithm to make help raw data useful.
• Create large data warehouse using ETL.
• Recommending way to improve data efficiency
• Mostly they work with BIG data and submit report to data scientist and data
analytics..

45. SKILL
• Bachelor degree
• Good hand of python R and Java.
• Well versed with Big data analytics Hadoop ,Apache Spark Scala and Mongo DB.
• Basic knowledge of statistics and good knowledge of operating system.

46. SALARY
• Rs. 885000 in India
• $. 103000 in USA

47. DATA SCIENTIST
• A data scientist understand the challenges in businesses and comes up with the
best solutions using modem tools and techniques analyze and visualize to make
business decision.
• Data scientist are the professional who arrives at business conclusions by using
advance level data technique .They are usually the senior most in the team.

48. ROLE AND RESPONSIBILITIES
• Data scientist clean process and manipulate data with several data analytics
tools.
• They perform data mining ,collect large set structured and unstructured data from
different sources.
• Data scientist design and evaluate advance statistical model on big data.
• They also create automated normally deducted systems and constant track of
there performance.
• .

49. • Interpret the analysis big data for solutions and opportunities.
• A data scientist take input from data analytics and engineers to formulate the
result T
• they use visualization tools for reports and dashboard for relevant stakeholders.
• They regular build predictive models and machine learning algoritm.

50. • Bachelor degree in computer science or relevant degree in domain.
• Master degree hold the major advantage.
• Proficient in programming language python java Perl.
• Similarity with Apache hive and Apache Hadoop
• Proficiency in SQL knowledge of machine learning and deep learning.
• Data visualization skills
• Communication to present idea

51. SALARY DATA SCIENTIST
• Rs 1000000 per year in India
• $1130000 per year in USA

52. COMPANIES WHERE DATA SCIENTIST CAN
WORK

53. MACHINE LEARNING ENGINEERS
• Machine learning engineers are professional who develop intelligent machine
that can learn from vast amount of data without human intervention.
• They use different algorithm and statistical modelling to make sense of data .they
work for toward designing self running software.
• They design machine learning and deep learning algorithms.
• Objective to running self running software.

54. RESPONSIBILITIES MACHINE LEARNING
ENGINEERS
• Research design and develop machine learning system.
• Use exceptional mathematical skills.
• Create sophisticated models.
• Perform A/B testing and build machine learning algorithm.
• Use data modelling to discover pattern.
• Machine learning engineers work closely with data engineers to build data
pipeline and interact with stakeholders to get a clarify on the requirements.

55. RESPONSIBILITIES MACHINE LEARNING
ENGINEERS
• Analyze complex dataset to verify data quality, for model test and experiment
chose to implement right machine learning algorithm and select the right training
data set

56. SKILL MACHINE LEARNING ENGINEERS
• Machine learning engineer have degree in computer science or advance degree.
• They should have experience in the same domain.
• Proficient in programming language such as python R
• Knowledge of statistics math's and leaner algebra and calculus.
• Understand various machine learning libraries such numby,panda cycitlearn etc.
and data manipulation .
• Oral and written communication skills

57. SALARY OF MACHINE LEARNING ENGINEERS
• 800000 per year in India
• $122000 per year in USA.

58. COMPANIES HIRING

59. RESUME
• Common to have a professional photo graph
• Name in Bold.
• Contact details like email di,phone number address.
• Write a summary explain your current job role and looking for future.
• Having a linken profile and Github profile link is common these days.
• It is impressive your resume is just second read.
• Experience : company and tools you have worked with

60. • Mention your data delivery
• Your education : degree and certification
• Skills : depend at the begging or at the end.
• Languages and database and data visualization tools
• Language you know such as German EnglishLT

62. TOP 10 TOOLS IN DATA ANALYTICS
• Microsoft Power BI.
• Tableau.
• Python and its libraries
• Maths and statistics
• SQL
• Microsoft excel

63. APPLICATION OF DATA ANALYTICS
•

64. POLICING/SECURITY
• Several cities all over the world have employed predictive analysis in predicting
areas that would likely witness a surge in crime with the use of geographical data
and historical data.

65. TRANSPORTATION
• Train operators made use of data analytics to ensure the large numbers of
journeys went smoothly. They were able to input data from events that took place
and forecasted a number of persons that were going to travel; transport was
being run efficiently and effectively so that athletes and spectators can be
transported to and from the respective stadiums.

66. FRAUD AND RISK DETECTION
• This has been known as one of the initial application of data science which was
extracted from the discipline of Finance. So many organizations had very bad
experiences with debt and were so fed up with it. Since they already had data that
was collected during the time their customers applied for loans, they applied data
science which eventually rescued them from the losses they had incurred.
• This led to banks learning to divide and conquer data from their customers’
profiles, recent expenditure and other significant information that were made
available to them. This made it easy for them to analyze and infer if there was any
probability of customers defaulting.

67. MANAGE RISK
• In the insurance industry, risk management is the major focus. What most people
aren’t aware of is that when insuring a person, the risk involved is not obtained
based on mere information but data that has been analyzed statistically before a
decision is made. Data analytics gives insurance companies information on
claims data, actuarial data and risk data covering all important decision that the
company needs to take. Evaluation is done by an underwriter before an individual
insured then the appropriate insurance is set.

68. DELIVERY LOGISTICS
• Well, data science and analytics have no limited applications. There are several
logistic companies working all over the world such as UPS, DHL, FedEx, etc. that
make use of data for improving their efficiency in operations. From data analytics
applications, these companies have found the most suitable routes for shipping,
the best delivery time, most suitable means of transport to select so as to gain
cost efficiency and many others.

69. WEB PROVISION
• There is this general belief that “Smart Cities” have fast internet speed provided
either by their government or companies present there, therefore declaring them
smart. Well, just because people can access Facebook or YouTube at the speed
of lightning does not necessarily make a city smart.

70. PROPER SPENDING
• Another issue with Smart Cities is the large amount of money spent on little work.
Small changes or landmark remodeling which one could dismiss as unnecessary
projects consume so much money. Data analytics applications would target
where taxpayers’ money would have a major impact on and the kind of work that
would be adequate for it. The targeting of where this money should be spent
would lead to the entire city’s infrastructure getting a facelift with a reduction of
excess money spent.

71. CUSTOMER INTERACTIONS
• This is another one of the applications of data analytics in insurance. Insurers can determine a lot
about their services by conducting regular customer surveys mainly after interacting with claim
handlers. They could use this to know which of their services are good and the ones that would need
improvement. Various demographics may desire diverse methods of communication like in person
interactions, websites, phone or just email. Taking the analysis of customer demographics with
feedback can help insurers improve on customer experience depending on customer behavior and
proven insights.

72. CITY PLANNING

73. CITY PLANNING
• One big mistake being made in many places is that analytics is not considered
when pursuing city planning. As a matter of fact, web traffic and marketing are still
being used instead of the creation of spaces and buildings. This really causes a
lot of issues to power over data due to its influence on things like building zoning
and amenity creation. Models that are built will maximize the accessibility of
specific areas or services while the risk of overloading significant elements of the
infrastructure in the city is minimized. This implies that it creates efficiency.

74. HEALTHCARE

75. HEALTHCARE
• One challenge most hospitals face is coping with cost pressures in treating as
many patients as possible, considering the quality of healthcare’s improvement.
Machine and instrument data use has risen drastically so as to optimize and track
treatment, patient flow as well as the use of equipment in hospitals. There is an
estimation that a 1% efficiency gain will be achieved and would result to over $63
billion in worldwide health care services.

76. TRAVEL

77. TRAVEL
• Data analytics applications help in the optimization of traveler’s buying
experience via social media and mobile/weblog data analysis. This is because
customers’ preferences and desires can be obtained from this, therefore, making
companies sell products from the correlation of the current sales to recent
browse-to-buy conversion through customized offers and packages. Data
analytics applications can also deliver personalized travel recommendations
depending on the outcome from social media data.

78. Energy Management

79. ENERGY MANAGEMENT
• We are in an era where firms make apply data analytics to energy management
and cover areas like energy optimization, smart-grid management, distribution of
energy and building automation for utility companies. Data analytics application
here focuses mainly on monitoring and controlling of dispatch crew, network
devices and make sure service outages are properly managed. Utilities get the
ability to integrate as much as millions of data points within the performance of
the network which allows the engineers make use of the analytics in monitoring
the network.

80. Internet/Web Search

81. INTERNET/WEB SEARCH
• Well, apart from Google, there are several other search engines such as Bing,
Yahoo, Duckduckgo, AOL, Ask, etc. Each of these search engines is as a result of
data science applications because they use algorithms to deliver the best results
for any search query directed at them in just a split second. In respect to this,
Google is known to process over 20 petabytes of data daily. Of course, without
analytics and data science, this feat wouldn’t have been possible.

84. WHY VISTA ACADEMY
• Dedicated training in Data science and Data analytics
• Experience faculty
• Place supports
• Projects and interview preparation
• Vistajobplacements.com