Placement Preparation Kit -

IT, Systems & Analytics

Internal NavKiran
 Definition of IT
Information Technology (IT) can be defined as the utilization of hardware,
software, services and infrastructure to create, store, exchange and
leverage information in its various forms to accomplish multiple business
objectives. Additionally, the term encompasses the workers that develop,
implement, maintain and utilize information technology directly and
indirectly.
❖ IT Hardware – computers, tablets, mobile phones, printers, servers, etc.
❖ Software – productivity applications, network applications, security
applications, etc.
❖ IT Services – integration, maintenance, repair, application development,
managed services, etc.
❖ IT Infrastructure – the Internet backbone, fiber optic networks, data
centers, etc. Information – text data, documents, voice, video and
images, etc.
❖ Business Objectives – migration, communication, collaboration,
efficiency, insight, production, e-commerce, etc.

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 NASSCOM and It’s Role
About the Industry
NASSCOM is the premier trade body and the chamber of commerce of the IT-
BPO industries in India. NASSCOM is a global trade body with more than 2000
members, which include both Indian and multinational companies that have a
presence in India. NASSCOM's member and associate member companies are
broadly in the business of software development, software services, software
products, consulting services, BPO services, e-commerce & web services, 6
engineering services off shoring and animation and gaming.

• NASSCOM’s membership base constitutes over 95% of the industry revenues

in India and employs over 3 million professionals.

• It is a not-for-profit organization, registered under the Indian Societies Act,

1860.
• Currently, NASSCOM is headquartered in New Delhi, India with regional
offices in the cities of Mumbai, Chennai, Hyderabad, Bangalore, Kolkata and
Pune.

Chairman - Keshav Murugesh

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 NASSCOM and It’s Role
❑ Objectives
Following are their main objectives and services:
✓ Encourage members to provide world-class quality products, services and
solutions in India and overseas and help build brand equity for the Indian IT
software and services industry.
✓ Taking effective steps to campaign against software piracy.
✓ Provide an ideal forum for overseas and domestic companies to explore the
vast potential available for Joint Ventures, Strategic Alliances, Marketing
Alliances, Joint Product Development, etc., by organizing business Meets
with delegations of various countries.
✓ Work actively with Overseas Governments, Embassies to make the Visa and
Work Permit Rules more "India Industry Friendly".
✓ Disseminate various policies, market information and other relevant statistics
by sending more than 200 circulars (annually) to all members.
✓ Involve membership participation in various forums of NASSCOM on subjects
such as HRD,
✓ Technology, Exports, Domestic Market, E-Governance, IT Enabled Services,
IPR, Finance, Government Policies, Quality etc.

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 NASSCOM and It’s Role
❑ Other Initiatives

✓ Domestic Market Initiative: It provides the platform to

link the non IT sector with the IT industry and to
develop a vision for sustainable growth of the domestic
IT market.
✓ Education Initiative: Under this initiative, NASSCOM
aims to enhance graduate employability through
appropriate skill enrichment and act as bridge between
industry, academia and government
✓ Women in Leadership-IT Initiative: NASSCOM has
launched the Women in Leadership-IT Initiative to
enhance participation of women into the workforce and
create more women leaders in the IT-BPO industry.
✓ Security Initiative: Aim towards creating an enabling
environment in the country for information security and
compliance through awareness and conducive legal
framework

Internal NavKiran
 Cloud Computing
The practice of using a network of remote servers hosted on the Internet to
store, manage, and process data, rather than a local server or a personal
computer.
Advantages :
▪ It reduces upfront infrastructure cost
▪ It relies on sharing resources and focuses
on maximizing the effectiveness of the
shared resources. Cloud resources are
usually not only shared by multiple users
but as well as dynamically re-allocated as
per demand.
▪ This can work for allocating resources to
users in different time zones.
▪ For new installations, it enables the
organizations to get their solutions up and
running in less time.
▪ The maintenance and scalability issues
can be relatively easily addressed in
cloud based solutions.
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 Cloud Computing
❑ Service Models
Cloud computing providers offer their services according to several fundamental
models: infrastructure as a service (IaaS), platform as a service (PaaS), and
software as a service (SaaS) Infrastructure as a Service: Cloud provider

Platform as a Service: Cloud providers

deliver a computing platform, typically
including operating system, programming
language execution environment,
database, and web server.
E.g.: Microsoft Azure, force.com
Software as a Service: Cloud providers
allow access to application software and
databases.
E.g.: Google Apps, Salesforce, Citrix
GoToMeeting
Infrastructure as a Service: Cloud
providers provide virtualized computing
resources over the internet
E.g.: Amazon AWS, Microsoft Azure
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 Cloud Computing
➢ Challenges
1.) Customers do not have full visibility and control since the server is not within
their own network.
2.) Security concerns if sensitive data lands on public cloud servers.
3.) There are capacity concerts since users may use an unpredictable amount
of resources at any given time and then disappear.
4.) Budget concerns around overuse of storage or bandwidth.
5.) Managing Multi-Cloud Environments

➢ Deployment models
The most popular deployment models are:
1.) Private cloud: Cloud infrastructure operated solely for a single organization.
2.) Public cloud: When the services are opened for public use.
3.) Community Cloud: When infrastructure is shared between several
organizations from a specific community with common concerns (security,
compliance, jurisdiction, etc.)
4.) Hybrid cloud: It is a composition of two or more clouds.
5.) Application of cloud computing for individuals similar to a Personal Computer
Personal cloud:

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 Cloud Computing
Some of the big names in the FinTech cloud computing sector are:
• Amazon Web Services
• Google Virtual Cloud
• Microsoft Azure
• Aliyun
• IBM Bluemix
Application in Banking and Fintech
▪ Cloud computing increases the efficiency by digitalizing the services will allow the
banks and financial institutions to build up an infrastructure to provide the best and
appropriate service to the customers.

▪ Data centers generally go through many attacks from the hackers which corrupt and
led to the loss of very crucial information in the bank. Such attacks can be eliminated
by authenticating the data centers which are very easy through cloud computing.

▪ Cloud computing ensures secure transactions and smooth customer experience in

banks.

▪ Payment Gateways, digital wallets, online fund transfer, and secure online payments
are among the best examples of the cloud computing service.

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 Internet of Things (IOT)
The internet of things, or IoT, is a system of interrelated computing devices,
mechanical and digital machines, objects, animals or people that are provided
with unique identifiers ( UIDs ) and the ability to transfer data over a network
without requiring human-to-human or human-to-computer interaction.

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 Internet of Things (IOT)
How IOT Works?
An IoT ecosystem consists of web-enabled smart devices that use embedded
processors, sensors and communication hardware to collect, send and act on
data they acquire from their environments. IoT devices share the sensor data
they collect by connecting to an IoT gateway or other edge device where data is
either sent to the cloud to be analysed or analysed locally. Sometimes, these
devices communicate with other related devices and act on the information they
get from one another. The devices do most of the work without human
intervention, although people can interact with the devices -- for instance, to set
them up, give them instructions or access the data.
Benefits of IoT
The internet of things offers a number of benefits to organizations, enabling them
to:
• monitor their overall business processes;
• improve the customer experience;
• save time and money;
• enhance employee productivity;
• integrate and adapt business models;
• make better business decisions
• generate more revenue.
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 Internet of Things (IOT)
Consumer and Enterprise IoT
➢ In the consumer segment, for example, smart homes that are equipped with smart
thermostats, smart appliances and connected heating, lighting and electronic devices can
be controlled remotely via computers, smartphones or other mobile devices.
➢ Wearable devices with sensors and software can collect and analyze user data, sending
messages to other technologies about the users with the aim of making users' lives easier
and more comfortable. Wearable devices are also used for public safety -- for example,
improving first responders' response times during emergencies by providing optimized
routes to a location or by tracking construction workers' or firefighters' vital signs at life
threatening sites.
➢ In healthcare, IoT offers many benefits, including the ability to monitor patients more closely
to use the data that's generated and analyse it. Hospitals often use IoT systems to
complete tasks such as inventory management, for both pharmaceuticals and medical
instruments .
➢ Smart buildings can, for instance, reduce energy costs using sensors that detect how many
occupants are in a room. The temperature can adjust automatically -- for example, turning
the air conditioner on if sensors detect a conference room is full or turning the heat down if
everyone in the office has gone home.
➢ In agriculture, IoT-based smart farming systems can help monitor, for instance, light,
temperature, humidity and soil moisture of crop fields using connected sensors. IoT is also
instrumental in automating irrigation systems.
➢ In a smart city, IoT sensors and deployments, such as smart streetlights and smart meters,
can help alleviate traffic, conserve energy, monitor and address environmental concerns,
and improve sanitation.
Internal NavKiran
 Artificial Intelligence
Artificial intelligence (AI) is the simulation of human intelligence processes by
machines, especially computer systems. These processes include learning (the
acquisition of information and rules for using the information), reasoning (using
rules to reach approximate or definite conclusions) and self-correction.
Particular applications of AI include expert systems, speech recognition and
machine vision.

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 Artificial Intelligence
Types of Artificial Intelligence
o Type 1: Reactive machines. An example is Deep Blue, the IBM chess
program that beat Garry Kasparov in the 1990s. Deep Blue can identify
pieces on the chess board and make predictions, but it has no memory and
cannot use past experiences to inform future ones. It analyzes possible
moves -- its own and its opponent -- and chooses the most strategic move.
Deep Blue and Google's AlphaGO were designed for narrow purposes and
cannot easily be applied to another situation.
o Type 2: Limited memory. These AI systems can use past experiences to
inform future decisions. Some of the decision-making functions in self-driving
cars are designed this way. Observations inform actions happening in the
not-so-distant future, such as a car changing lanes. These observations are
not stored permanently.
o Type 3: Theory of mind. This psychology term refers to the understanding
that others have their own beliefs, desires and intentions that impact the
decisions they make. This kind of AI does not yet exist.
o Type 4: Self-awareness. In this category, AI systems have a sense of self,
have consciousness. Machines with self-awareness understand their current
state and can use the information to infer what others are feeling. This type
of AI does not yet exist
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 Artificial Intelligence
Examples of AI Technology
1. Automation, 2.Machine Learning, 3.Natural Language Processing
4. Robotics, 5. Self-driving Cars
AI Applications :
• AI in Healthcare - The biggest bets are on improving patient outcomes and
reducing costs. One of the best known healthcare technologies is IBM
Watson. The system mines patient data and other available data sources to
form a hypothesis, which it then presents with a confidence scoring schema.
Other AI applications include chatbots, a computer program used online to
answer questions and assist customers, to help schedule follow-up
appointments or aid patients through the billing process, and virtual health
assistants that provide basic medical feedback.
• AI in Business - Machine learning algorithms are being integrated into
analytics and CRM platforms to uncover information on how to better serve
customers.
• AI in Education - AI can automate grading, giving educators more time. AI
can assess students and adapt to their needs, helping them work at their
own pace.
• AI in Finance - AI in personal finance applications, such as Mint or Turbo
Tax, is disrupting financial institutions.
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 Augmented Reality
Augmented reality is the technology that expands our physical world, adding
layers of digital information onto it. Unlike Virtual Reality (VR), AR does not
create the whole artificial environments to replace real with a virtual one. AR
appears in direct view of an existing environment and adds sounds, videos,
graphics to it.

There are 4 types of augmented reality today:

1.) Markerless AR
2.) Marker-based AR
3.) Projection-based AR
4.) Superimposition-based AR

Augmented Reality Devices

1. Mobile devices (smartphones and tablets) ,
2. Special AR devices (head-up displays - HUD)
3. AR glasses (or smart glasses) – Google Glasses, Meta 2 Glasses, Laster
See-Thru, Laforge AR eyewear, etc.
4. AR contact lenses (or smart lenses)
5. Virtual retinal displays (VRD)

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 Augmented Reality
Applications of Augmented Reality
1. Data Visualization
2. Virtual Trading
3. Virtual Reality Payments
4. Security
5. Financial Education
6. Customer Service
7. Virtual Branches
8. Recruitment and Training
9. Customer Acquisition

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 Machine Learning
Machine learning is an application of artificial intelligence (AI) that provides systems the
ability to automatically learn and improve from experience without being explicitly
programmed. Machine learning focuses on the development of computer programs that
can access data and use it learn for themselves. many more places than one would
expect.

Machine Learning Methods

❑ Supervised machine learning

algorithms
❑ Unsupervised machine
learning algorithms
❑ Semi-supervised machine
learning algorithms
❑ Reinforcement machine
learning algorithms

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 Machine Learning
Machine learning is an application of artificial intelligence (AI) that provides
systems the ability to automatically learn and improve from experience without
being explicitly programmed. Machine learning focuses on the development of
computer programs that can access data and use it learn for themselves. many
more places than one would expect.

Applications of Machine
learning
I. Image Recognition
II. Speech Recognition
III. Traffic prediction
IV. Product recommendations
V. Self-driving cars
VI. Email Spam and Malware
Filtering
VII. Virtual Personal Assistant
VIII. Online Fraud Detection

Internal NavKiran
 Machine Learning
ANALYZING CUSTOMER CHURN BY USING AZURE

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 Blockchain
Blockchain technology is not a company, nor is it an app, but rather an entirely
new way of documenting data on the internet. The technology can be used to
develop blockchain applications, such as social networks, messengers, games,
exchanges, storage platforms, voting systems, prediction markets, online shops
and much more. In this sense, it is similar to the internet, which is why some
have dubbed it “The Internet 3.0”.

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 Blockchain
Key Attributes of Blockchain :

1.) Peer-To-Peer: No central authority to control or manipulate it. All participant

talks to each other directly. This allows for data exchange to be made directly with
third-parties involvement.
2.) Distributed: The ledger is spread across the whole network which makes
tampering not so easy.
3.) Cryptographically Secured: Cryptography is used for the security services to
make the ledger tamper-proof.
4.) Add-Only: Data can only be added in the blockchain with time-sequential
order. This property implies that once data is added to the blockchain, it is almost
impossible to change that data and can be considered practically immutable. We
can say it has: “The right to be forgotten or right to erasure” defined here.
5.) Consensus: This is the most critical attribute of all. This gives blockchain the
ability to update the ledger via consensus. This is what gives it the power of
decentralization. No central authority is in control of updating the ledger. Instead,
any update made to the Blockchain is validated against strict criteria defined by the
Blockchain protocol and added to the Blockchain only after a consensus has been
reached among all participating peers/nodes on the network.

Internal NavKiran
 Blockchain
Applications of Blockchain :

1. Secure sharing of medical data

2. Music royalties tracking
3. Cross-border payments
4. Real-time IoT operating systems
5. Personal identity security
6. Anti-money laundering tracking system
7. Supply chain and logistics monitoring
8. Voting mechanism
9. Advertising insights
10. Original content creation
11. Cryptocurrency exchange
12. Real estate processing platform

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 Cyber Security
Cyber security is the body of technologies, processes and practices designed to
protect networks, computers, programs and data from attack, damage or
unauthorized access. In computing context, the term security implies cyber
security. And the most problematic element of cyber security is the quickly and
constantly evolving nature of security risks. Ensuring cyber security requires
coordinated efforts throughout an information system.

Elements of cyber security include:

• Application security
• Information security
• Network security
• Disaster recovery / business continuity planning
• End-user Practice correction and educating them.

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 Analytics
Analytics is an encompassing and multidimensional field that uses mathematics,
statistics, predictive modelling and machine learning techniques to find
meaningful patterns and knowledge in recorded data.
❑ Popular Analytical Methods:
There are three predominant types of analytics in use today.
• Descriptive statistics - These are the models that will help you understand
what happened and why. There are still plenty of descriptive analytics in use
today – everything from how many clicks a page receives to how many units
are produced vs. how many are sold.
• Predictive analytics - These models use past data and predictive algorithms
to help you determine the probability of what will happen next.
• https://www.predictiveanalyticstoday.com/what-is-predictive-
analytics/#applicationsofpredictiveanalytics

• Prescriptive analytics - Prescriptive analytics answers the question of what

to do by providing information on optimal decisions based on the predicted
future scenarios. The key to prescriptive analytics is being able to use big
data, contextual data and lots of computing power to produce answers in real
time.

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 Analytics
Data Analytics Tools
1.) R Programming
2.) Tableau
3.) Python
4.) SAS
5.) Excel

Social Media Analytics

Social media analytics is the practice of gathering data from social media
websites and analysing that data using social media analytics tools to make
business decisions. The most common use of social media analytics is to mine
customer sentiment to support marketing and customer service activities.

This is considered the basic foundation for enabling an enterprises to:

➢ Execute focused engagements like one-to-one and one-to-many
➢ Enhance social collaboration over a variety of business functions, such as
customer service, marketing, support, etc.
➢ Maximize the customer experience

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 Big Data
Big Data is extremely large data sets that may be analysed computationally
to reveal patterns, trends, and associations, especially relating to human
behaviour and interactions. The term "big data" refers to digital stores of
information that have a high volume, velocity and variety. Big data analytics
is the process of using software to uncover trends, patterns, correlations or
other useful insights in those large stores of data.

The importance of big data

analytics can be explained using
the following four perspectives:

1) Data Science Perspective

2) Business Perspective
3) Real-time Usability
Perspective
4) Job Market Perspective

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 Big Data Analytics
Big Data analytics involves the use of analytics techniques like machine
learning, data mining, natural language processing, and statistics. The data
is extracted, prepared and blended to provide analysis for the businesses.
Large enterprises and multinational organizations use these techniques
widely these days in different ways.

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 Big Data Analytics
Real-time Benefits of Big Data Analytics
There has been an enormous growth in the field of Big Data analytics with
the benefits of the technology. This has led to the use of big data in multiple
industries ranging from

• Banking
• Healthcare
• Energy
• Technology
• Consumer
• Manufacturing

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 What Does Data Scientist do?
Data Scientists perform an exploratory analysis to discover insights from the
data. They also use various advanced machine learning algorithms to identify the
occurrence of a particular event in the future. This involves identifying hidden
patterns, unknown correlations, market trends and other useful business
information.

Roles of Data Scientist

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 What do Big Data Professionals do?
The responsibility of a big data professional lies around dealing with huge
amount of heterogeneous data, which is gathered from various sources
coming in at a high velocity.
Big data professionals describe the structure and behaviour of a big data
solution and how it can be delivered using big data technologies such as
Hadoop, Spark, Kafka etc. based on requirements.

Roles of Big Data Professional

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 What does a Data Analyst do?
Data analysts translate numbers into plain English. Every business collects data,
like sales figures, market research, logistics, or transportation costs. A data
analyst’s job is to take that data and use it to help companies to make better
business decisions.

Roles of Big Data Analyst

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 Benefits of Big Data Analytics
Organizations decide to deploy big data analytics for a wide variety of reasons,
including the following:

• Business Transformation: In general, executives believe that big data analytics

offers tremendous potential to revolution their organizations.
• Competitive Advantage: Use of analytics is helping organizations to achieve
competitive advantage, up from 51 percent.
• Innovation: Big data analytics can help companies develop products and
services that appeal to their customers, as well as helping them identify new
opportunities for revenue generation.
• Lower Costs: In the NewVantage Partners Big Data Executive Survey 2017,
49.2 percent of companies surveyed said that they had successfully decreased
expenses as a result of a big data project.
• Improved Customer Service: Organizations often use big data analytics to
examine social media, customer service, sales and marketing data. This can help
them better gauge customer sentiment and respond to customers in real time.
• Increased Security: Another key area for big data analytics is IT security.
Security software creates an enormous amount of log data. By applying big data
analytics techniques to this data, organizations can sometimes identify and thwart
cyber attacks that would otherwise have gone unnoticed.
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 Benefits of Big Data Analytics

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 Big Data Analytics Trends
1) Open Source
As big data analytics increases its momentum, the focus is on open-source tools
that help break down and analyse data. Hadoop, Spark and NoSQL databases are
the winners here. Even proprietary tools now incorporate leading open source
technologies and/or support those technologies.
2) Market Segmentation
Plenty of general-purpose big data analytics platforms have hit the market, but
expect even more to emerge that focus on specific niches, such as security,
marketing, CRM, application performance monitoring and hiring.
3) Artificial Intelligence and Machine Learning
As interest in AI has skyrocketed, vendors have rushed to incorporate machine
learning and cognitive capabilities into their big data analytics tools.
4) Prescriptive Analytics
Seen by many as the "ultimate" type of big data analytics, these tools will not only
be able to predict the future, they will be able to suggest courses of action that
might lead to desirable results for organizations. But before these types of
solutions can become mainstream, vendors will need to make advancements in
both hardware and software.

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 Example of Data Analytics
Coca-Cola
Product development
Coca Cola is known to have ploughed extensive research and development resources into
artificial intelligence (AI) to ensure it is squeezing every drop of insight it can from the data it
collects.

Healthy options
The company combines weather data, satellite images, information on crop yields, pricing
factors and acidity and sweetness ratings, to ensure that orange crops are grown in an optimum
way, and maintain a consistent taste. The algorithm then finds the best combination of variables
in order to match products to local consumer tastes in the 200-plus countries around the world
where its products are sold.

Social data mining

Coca Cola closely tracks how its products are represented across social media, and in 2015
was able to calculate that its products were mentioned somewhere in the world an average of
just over once every two seconds.
Knowing this gives insight into who is consuming their drinks, where their customers are, and
what situations prompt them to talk about their brand. The company has used AI-driven image
recognition technology to spot when photographs of its products, or those of competitors, are
uploaded to the internet, and uses algorithms to determine the best way to serve them
advertisements.

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 Example of Data Analytics
Netflix Recommendation System
Whenever you access the Netflix service, their recommendations system helps you find a show
or movie to enjoy with minimal effort.
The likelihood that you will watch a particular title in the catalogue is estimated based on a
number of factors including:
• your interactions with the service (such as your viewing history and how you rated other titles),
• other members with similar tastes and preferences on the service
• information about the titles, such as their genre, categories, actors, release year, etc.

To best personalize the recommendations following factors are considered:

• the time of day you watch,
• the devices you are watching Netflix on, and
• how long you watchddx

All of these pieces of data are used as inputs that are processed in the algorithms. The
recommendations system does not include demographic information (such as age or gender) as
part of the decision making process.
To improve the recommendation system, feedback from every visit to the Netflix service is
collected used to and continually re-train the algorithms with those signals to improve the
accuracy of their prediction of what you’re most likely to watch.

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 Software Development Lifecycle (SDLC)
The systems development life cycle (SDLC) is a conceptual model used in
project management that describes the stages involved in an information
system development project, from an initial feasibility study through
maintenance of the completed application.
Methodologies of Systems Development Life Cycle
Various SDLC methodologies have been developed to guide the processes
involved.
▪ The waterfall model (which was the original SDLC method)
▪ Rapid application development (RAD)
▪ Joint application development (JAD)
▪ The fountain model
▪ The spiral model
▪ Build and fix
Synchronize-and-stabilize Frequently, several models are combined into
some sort of hybrid methodology. Agile Software Development is a group of
software development methods based on iterative and incremental
development, where requirements and solutions evolve through collaboration
between self-organizing, cross-functional teams. It is a time-bound approach
which encourages rapid and flexible response to change. Scrum is an
example of agile methodology.
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 Software Development Lifecycle (SDLC)

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 Software Development Lifecycle (SDLC)
The System Development is the interactive process which consists of
the following stages :-

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 Information Systems
Information System is the collection of hardware, software, data, people and
procedures for collecting, storing, and processing data and for delivering
information, knowledge and digital products.
❖ Models of Information System
Three Level Pyramid Model: Information Systems based on the type of
decisions taken at different levels in the organization.
The first level represents transaction processing systems focused on operational
decisions. The second level represents management information systems
focused on tactical decisions. The third level represents executive information
systems focused on strategic decisions.

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 Information Systems
A four level pyramid model: Information Systems based on the different levels
of hierarchy in an organization.
The first level represents transaction processing systems for workers. The
second level represents management information systems for middle managers.
The third level represents decision support systems for senior managers. The
fourth level represents executive information systems for executives

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 Information Systems
A five level pyramid model: Information Systems based on the information processing
requirement of different levels in the organization.
The first level represents transaction processing systems to process basic data. The second
level represents office support systems to process information in office. The third level
represents management information systems to process information by managers. The fourth
level represents decision support systems to process explicit knowledge. The fifth level
represents executive information systems to process tacit knowledge.

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 Types of Information System
Office Information Systems: An information system that uses hardware, software
and networks to enhance work flow and facilitate communications among
employees. The software an office information system uses to support these
activities include word processing, spreadsheets, databases, presentation
graphics, e-mail, Web browsers.
Transaction Processing Systems: An information system that captures and
processes data generated during an organization’s day-to-day transactions. A
transaction is a business activity such as a deposit, payment, order or reservation.
Management Information Systems: An information system that generates
accurate, timely and organized information so managers and other users can make
decisions, solve problems, supervise activities, and track progress. Decision
Support Systems: An information system designed to help users reach a decision
when a decision-making situation arises. A decision support system uses data from
internal and/or external sources
Expert Systems: An information system that captures and stores the knowledge of
human experts and then imitates human reasoning and decision-making processes
for those who have less expertise. Expert systems are composed of two main
components: a knowledge base and inference rules. A knowledge base is the
combined subject knowledge and experiences of the human experts.

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Internal NavKiran