basics of data science, engineering, fundamentals of data science, application areas of data science

1. Professional Elective
Data Science (ME 404D)
Unit 1 - Introduction to Python for Data Science
BY
V. P. Bhaurkar
Department of Mechanical Engineering
Sanjivani College of Engineering, Kopargaon

2. What is Data?
➢ Data is meaningless until its conversion into valuable information.
Definition :
“Data is different types of information usually formatted in a particular manner.”
What is Data Science?
“Data Science involves mining large datasets containing structured and unstructured data
and identifying hidden patterns to extract actionable insights.”
Introduction

3. 1. The importance of Data Science lies in its innumerable uses that range from daily activities
like asking Alexa for recommendations, to more complex applications like operating a
self-driving car.
2. By 2025, global data will grow to 175 zettabytes. [Ref - www.niti.gov.in]
3. Data Science enables companies to efficiently understand gigantic data from multiple
sources and derive valuable insights to make smarter data-driven decisions.
4. Data Science is widely used in various industry domains, including marketing, healthcare,
finance, banking, policy work, and more. That explains why Data Science is important.
Why Data Science is important?

4. A] Industries and Enterprises
1. Data Science enables enterprises to measure, track, and record performance metrics for
facilitating enterprise-wide enhanced decision making.
2. Companies can analyze trends to make critical decisions to engage customers better, enhance
company performance, and increase profitability.
3. Data Science models use existing data and can simulate several actions. Thus, companies can
devise the path to reap the best business outcomes.
4. Data Science helps organizations identify and refine target audiences by combining existing
data with other data points for developing useful insights.
5. Data Science also helps recruiters by combining data points to identify candidates that best fit
their company needs.
Why Data Science is important?

5. B] Healthcare
1. In the healthcare industry, physicians use Data Science to analyze data from wearable trackers
to ensure their patients’ well-being and make vital decisions.
2. Data Science also enables hospital managers to reduce waiting time and enhance care.
3. Retailers use Data Science to enhance customer experience and retention.
C] Mechanical Engineers
1. Data science enhances the decision-making skills of mechanical engineers and helps them
effectively manage larger datasets.
2. By learning data science, mechanical engineers gain value over a short period. This means that
they can demand a higher salary or switch to a higher-paying job.
3. Data is the backbone of various decision-making processes in every organization. Engineers are
exposed to data in their scope of work, meaning that their decision-making skills are already
highly based on predicted data outcomes.
4. By studying data science, mechanical engineers can understand different programming
languages, making it easy to create scalable and efficient solutions.
Why Data Science is important?

6. 1. Healthcare: Data science can identify and predict disease, and personalize healthcare
recommendations.
2. Transportation: Data science can optimize shipping routes in real-time.
3. Sports: Data science can accurately evaluate athletes’ performance.
4. Government: Data science can prevent tax evasion and predict detention rates.
5. E-commerce: Data science envelops e-commerce and all allied activities.
6. Gaming: Data science can improve online gaming experiences.
7. Social media: Data science can create algorithms to pinpoint compatible partners.
8. Fintech: Data science can help create credit reports and financial profiles, run accelerated
underwriting and create predictive models based on historical payroll data.
Applications of Data Science

7. A] Medical
Identifying Cancer Tumors
1. Google is now applying data science to healthcare. In fact, the company developed a tool, LYNA, for identifying
breast cancer tumors that metastasize to nearby lymph nodes. That can be difficult for the human eye to see, especially
when the new cancer growth is small. [https://ai.googleblog.com/2018/10/applying-deep-learning-to-metastatic.html]
2. In one trial, LYNA — short for Lymph Node Assistant —accurately identified metastatic cancer 99 percent of the time
using its machine-learning algorithm. More testing is required, however, before doctors can use it in hospitals.
Tracking Menstrual Cycles
1. The popular Clue app employs data science to forecast user’s menstrual cycles and reproductive health by tracking
cycle start dates, moods, stool type, hair condition and many other metrics.
2. Behind the scenes, data scientists mine this wealth of anonymized data with tools like Python and Jupyter’s Notebook.
Users are then algorithmically notified when they’re fertile, on the cusp of a period or at an elevated risk for conditions
like an ectopic pregnancy.
Applications of Data Science

8. A] Medical
PERSONALIZING TREATMENT PLANS
1. Oncora’s software uses machine learning to create personalized recommendations for current cancer patients based on data from
past ones.
2. Their radiology team collaborated with Oncora data scientists to mine 15 years’ worth of data on diagnoses, treatment plans,
outcomes and side effects from more than 50,000 cancer records. Based on this data, Oncora’s algorithm learned to suggest
personalized chemotherapy and radiation regimens.
CLEANING CLINICAL TRIAL DATA
1. Veeva is a cloud software company that provides data and software solutions for the healthcare industry.
2. The company’s reach extends through clinical, regulatory and commercial medical fields.
3. Veeva’s Vault EDC uses data science to clean clinical trial findings and help medical professionals make adjustments mid-
study.
Applications of Data Science

9. B] Transport
MODELING TRAFFIC PATTERNS
1. StreetLight uses data science to model traffic patterns for cars, bikes and pedestrians on North American streets. Based on a monthly influx
of trillions of data points from smartphones, in-vehicle navigation devices and more, Streetlight’s traffic maps stay up-to-date.
2. There are more granular than mainstream maps apps too: they can identify groups of commuters that use multiple transit modes to get to
work, like a train followed by a scooter. The company’s maps inform various city planning enterprises, including commuter transit design.
OPTIMIZING FOOD DELIVERY
1. The data scientists at Uber Eats have a fairly simple goal: getting hot food delivered quickly.
2. Making that happen across the country though, takes machine learning, advanced statistical modeling and staff meteorologists.
3. In order to optimize the full delivery process, the team has to predict how every possible variable — from storms to holiday rushes — will
impact traffic and cooking time.
Applications of Data Science

10. B] Transport
IMPROVING PACKAGE DELIVERY
1. UPS software uses data science to optimize package transport from drop-off to delivery.
2. The company’s integrated navigation system ORION helps drivers choose over 66,000 fuel-efficient routes.
3. ORION has saved UPS approximately 100 million miles and 10 million gallons of fuel per year with the use
of advanced algorithms, AI and machine learning.
4. The company plans to continue to update its ORION system, with the last version having been rolled out in
2021.
5. The latest update allowed drivers to reduce their routes by two to four miles.
Applications of Data Science

11. B] Sports Data Science Applications
MAKING PREDICTIVE INSIGHTS IN BASKETBALL
1. RSPCT’s shooting analysis system, adopted by NBA and college teams, relies on a sensor on a basketball
hoop’s rim (a metal ring holding the net), whose tiny camera tracks exactly when and where the ball strikes on
each basket attempt.
2. It funnels that data to a device that displays shot details in real time and generates predictive insights.
3. “Based on our data, we can tell [a shooter], ‘If you are about to take the last shot to win the game, don’t take
it from the top of the key, because your best location is actually the right corner,’
…………………………… RSPCT CEO Leo Moravtchik told to News team..
Applications of Data Science

12. B] Sports Data Science Applications
TRACKING PHYSICAL DATA FOR ATHLETES
1. WHOOP makes wearable devices that track athletes’ physical data like resting heart rate,
sleep cycle and respiratory rate.
2. The goal is to help athletes understand when to push their training and when to rest — and
to make sure they’re taking the necessary steps to get the most out of their body.
3. Professional athletes like Olympic sprinter Gabby Thomas, Olympic golfer Nelly Korda
and PGA golfer Nick Watney are among the WHOOPS’ users, according to the company’s
website.
Applications of Data Science

13. B] Sports Data Science Applications
GATHERING PERFORMANCE METRICS FOR SOCCER PLAYERS
1. Players wear a tracking device, called a Tracer, while its specially designed camera records
the game.
2. The AI bot then takes that footage and stitches together all of the most important moments
in a game — from shots on goal to defensive lapses and more. This technology allows
coaches and players to have more detailed insights from game film.
3. Beyond stitching together clips, the software also provides performance metrics and a field
heat map.
Applications of Data Science

14. C] Government Data Science Applications
1. To track the status of prisoners, their performance, how much times they have re-filed
their case in court and many more.
2. To keep the record of driver’s license photo databases, which will be helpful in
tracking criminal record, or any other disputes.
3. Data science finds application in finding the tax frauds in the country, tax payers data,
payment patterns and many more.
Applications of Data Science

15. D] E-Commerce Data Science Applications
1. Database with Google, Amazon about advertising, their frequency, monetisation and
other allied services.
2. SEO (Search engine optimisation-Google, Ask, Bing, AOL, Yahoo) working
algorithms, searching keywords, frequently used word, seasonal used words, search
news, website recommendations, speech recognition (Alexa)
3. The frequently visited websites and products, frequently purchased materials,
discounts and offers, trendy sale, likes and dislikes
4. The posts on social media like Instagram, viewing frequency and trends, sponsored
ads, users age and education (users profile), users comments
5. Huge amount of videos on websites like Youtube, their search, comments and many
more.
Applications of Data Science

16. A] Finance
1. Improved Sales and Revenue
I. Customer interactions, personal connections, improved facilities, customer satisfactions
II. Customer behaviour and proper services to clients, improved sales of company
2. Getting Helpful Insights
I. Problem of fraud and cybercrimes
II. Financial transactions, riskier clients
III. Automisation in routine processes like transactions, clients data, linked accounts and other credentials
Benefits of using Data Science

17. B] Risk Analytics
1. Every company has some sort of risk while doing business. Analyzing the threats and risks has become
a crucial part of every organization. This is a strategic step that is known as risk analytics.
2. A company can increase its effectiveness and security by applying data science tools as data is the core
of risk management. Data science has in it the knowledge of problem-solving making strategies.
C] Customer Data Management
1. Data is generally obtained in two types that are structured and unstructured. It is easy to analyze and
use the structured data as it is already in a particular format but in the case of unstructured data, it
becomes challenging to analyze it and is more time taking as it is not obtained in any particular form.
D] Algo Trading
1. Algorithmic trading is used to channel huge data into streamlined information.
Benefits of using Data Science

18. Thank You !