emerging expotenial technology Initially, Industrial revolution was regarded as alterations in the way. labourer works. * then, centralised on steam and mechanisation.

1. Emerging Exponential Technologies
SUBMITTED TO :
MISS.DIGANTHA D R
ASSISTANT PROFFESSOR
SAIT
SUBMITTED BY:
SAHANA G J
PALLAVI H
PRABHALYA
NETHRAVATHI

2. DATA SCIENCE

3. DEFINATION
Data
Data is a collection of raw, unorganized facts and details like text, observations, figures, symbols
and description of things etc. In other words, data does not carry any specific purpose and has no
significance by itself. Data is measured in terms of bits and bytes.
Science
Science is systematic knowledge of the physical or material world gained through observation and
experimentation.

4. Data Science
Data Science is a multidisciplinary field that utilizes scientific inference and mathematical
algorithms to extricate important insights from a lot of structured and unstructured data.
OR
Data Science is the area of study that combines domain expertise in programming skills and
knowledge of statistics and mathematics to obtain meaningful insights from the data. This in turn
gives analyst and business users insights to develop values for business.

5. Applications of Data Science
1. E-Commerce websites suggesting items to buy
2. Filtering of Emails in spam and non-spam categories.
3. Internet Search: Google search use Data science technology to search a specific result within a fraction of a second.
4. Recommendation Systems: To create a recommendation system. Example, "suggested friends" on Facebook or "suggested videos"
on YouTube, everything is done with the help of Data Science.
5. Online Price Comparison: Price Runner, Junglee, Shopzilla work on the Data science mechanism.
6. Here, data is fetched from the relevant websites and compared.
7. Image & Speech Recognition: Speech recognizes system like Siri, Google assistant, Alexa runs on the technique of Data science.
Moreover, Facebook recognizes your friend when you upload a photo with them, with the help of Data Science.

6. There are six stages in which development of data science
Stage 1: Contemplating about the power of data
Stage 2: More research on the importance of data
Stage 3: Data science gained attention
Stage 4: Data science started being practiced throughout the 2000s
Stage 5: A new era off data science
Stage 6: Data science in demand

7. Process of data science

8. Step 1: Frame the problem
The first thing you have to do before you solve a problem is to define exactly what it is. You need to
be able to
translate data questions into something actionable.
Step 2: Collect the raw data needed for your problem
Once you've defined the problem, you'll need data to give you the insights needed to turn the
problem around with a solution. This part of the process involves thinking through what data
you'll need and finding ways to get that data, whether it's querying internal databases, or
purchasing external datasets.

9. Step 3: Process the data for analysis
you have all of the raw data, you'll need to process it before you can do any analysis. Oftentimes, data
can be quite messy, especially if it hasn 't been well-maintained.You'll see errors that will corrupt your analysis:
values set to null though they really are zero, duplicate values, and missing values. It's up to you to go through and
check your data to make sure you'll get accurate insights.
Step 4: Explore the data
The difficulty here isn't coming up with ideas to test, it's coming up with ideas that are likely to turn into insights.
You'll have a fixed deadline for your data science project (your VP Sales is probably waiting on your analysis
eagerly!), so you'll have to prioritize your questions. '
You 'Il have to look at some of the most interesting patterns that can help explain why sales are reduced for this
group. You might notice that they don't tend to be very active on social media, with few of them having Twitter or
Facebook accounts. You might also notice that most ofthem are older than your general audience.
From that you can begin to trace patterns you can analyze more deeply.

10. Step 5: Perform in-depth analysis
This step of the process is where you're going to have to apply your statistical, mathematical and
technological knowledge and leverage all of the data science tools at your disposal to crunch the data
and find every insight you can.
Step 6: Communicate results of the analysis
It's important that the VP Sales understand why the insights you've uncovered are important. Ultimately,
you've been called upon to create a solution throughout the data science process. Proper communication will
mean the difference between action and inaction on your proposals.

11. DataAnalytics Life Cycle

12. Big Data

13. Meaning
•Big data refers to the large, diverse sets of information that grow at ever-increasing rates. It
enc…
Big data is a great quantity of diverse information that arrives in increasing volumes and with
ever-higher velocity.

14. 5v’s of Big Data

15. Big value chain

16. CHAPTER-5
This Photo by Unknown Author is licensed under CC BY-SA-NC

17. Virtual reality

18. Meaning of virtual reality
Virtual reality is a technology that creates a virtual environment environment. People interact in
those environment using, for example, VR goggles or other mobile devices. It is a computer-
generated simulation of an environment or 3-dimensional image where people can interact in a
seemingly real or physical way. To interact you need special electronic equipment, such as a helmet
with a screen inside or goggles. To get the full effects of virtual reality, the user wears gloves or a suit
with special sensors.

19. Types of virtual reality

20. Advantage and Disadvantages of virtual reality

21. Applications of Virtual Reality
Virtual Reality in Retail –Online shopping is convenient , but often means we must buy then try . but with VR ,
we can preview furniture in our own home.
VR in Education/Training-The pandemic forced students to learn online. Retailers , tech companies ,
and even the military are using tools to help train their workers.
Digital marketing-example :Retailers can show potential customers how a product will look in their
home. Or nonprofits can create more empathetic messaging for political issues.
Entertainment-used in online console gaming ,introduced to cinemas and theme parks to simulate
movie-like adventures and let people experience their favorite cinematographic masterpieces.

22. Augmented Reality

23. Meaning
Augmented reality (AR) is an enhanced version of the real physical world that is achieved
through the use of digital visual elements, sound, or other sensory stimuli and delivered via
technology. It is a growing trend among companies involved in mobile computing and
business applications in particular.

24. Advantages and Disadvantages of
AR

25. Applications of AR
AR in Education-3D models,Object modelling apps for medical students.
AR in Healthcare
AR in Entertainment-Music , Theater , movies , games .
AR in Remote Assistance-Technical support , Field services, Billing profits and contracting issues.