Top Data Science Careers of the Future— Part 2

Data Science use cases you might not have explored before...

When I wrote the first part of the blog Top 9 Data Science Careers of the Future in July, I did have in mind other Data Science career pathways that needed to be put out. I was overwhelmed to see people reaching out on this particular blog and sharing the views on many other careers: retail, education, and more.

Well, it is time that I finally write a Part 2 on Top Data Science Careers of the Future.

In the rapidly expanding technological world of today, when humans tend to generate a lot of data, it is quintessential that data is analyzed. Data is now the frontier for business or I can say it has become fuel for industries.

There are various industries like banking, finance, manufacturing, transport, e-commerce, education, etc. that we’ve heard use data science. In this article, I have listed 9 Data Science careers that have transformed with the rise of Data Science and related fields. We will see how Data Science has revolutionized the way businesses perceive data and excite you about what is in store for the future.

1. e-Commerce / Retail

e-Commerce is all about making recommendations to the customer and luring them into buying things they might not even need and making them regret about the payment they just made. Am I right or am I right? 😛

Any e-commerce website is sure to have a recommendation system. If you are interviewing for Amazon, Wish, Instagram, or any app/website that sells products, a recommender system project can surely be a talking point in your interview. Other use cases of Data Science in e-commerce:

1. Analyze customer sentiment from social media, user ratings, comments, product reviews
2. Predict the lifetime value of customers: Customer lifetime value is the total value of the customers’ profit to the company over the entire customer-business relationship.
3. Manage inventory with data models
4. Improve Customer Service
5. Retain customers with customized offers
6. Prime Optimization rules e-commerce: when to make what offer and for how long
7. Fraud detection: Payment integration invites fraud detection and risk management
8. Market Basket Analysis: You might also like, People also bought

2. Education

1. Forecast grades of students in the class based on learning parameters. If the model generates a low GPA for a student, the model can trigger a warning to the student and instructor indicating that the student will have to work harder to reach the desired mark
2. AI-based evaluation virtual interview platform that mimics an actual face to face interview. It can be used to automatically evaluate the body language of the candidate
3. A similar technique can be used in classrooms in times of online classes to examine who in the classroom is paying proper attention, who is not, and who is pretending
4. Predictive models help in evaluating the risks of student dropouts using data analysis and in turn help in taking precautionary measures against it
5. Student recruitment
6. Innovating curriculum
7. Monitor student’s social-emotional skills

The University of Florida has been using a platform to extract student data and use it to monitor and predict student performance.

Arizona State University’s (ASU) maths department has adopted a system called ‘adaptive learning’ to improve their student performance. Their systems collect information about students like marks, strengths, weaknesses, and even measures moments of hesitation.

3. Government

Okay, which Data Scientist has never come across the famous infamous “Chicago crime dataset” in their journey?

The governments in 2020 realize the importance of not just being online but using data for the effective working of geographies.

1. Federal Housing Authority (FHA) utilizes data science to forecast claim rates, default rates, and repayment rates
2. Food and Drug Administration (FDA) uses big data technologies to break down and understand the patterns of foodborne illnesses
3. The Center for Disease Control (CDC) performs real-time sentiment analysis to forage through social media to track the spread of illnesses or track early signs of suicidal tendencies from social media behavior
4. The U. S. Department of Homeland Security (DHS) has a lot of data: one of the best open-source datasets for analysis. The agency uses big data strategies that include interoperability to integrate and compare data from various security agencies to predict or identify potential threats to the country.
5. Governments use analytical algorithms and AI-powered solutions to identify enterprises that are not enrolled in the register anymore, gave up trading, and do not subject to legal liabilities and taxation.
6. Street crime analysis: Fingerprints, DNA samples, ballistic analysis
7. Cyberattacks
8. Emergency response: Real-time analytics

4. Military Intelligence

Who said data can only be used for profits to a business?

It has been my dream to serve as an Intelligence Analyst in the military. Regardless of my dream coming true or not, I would love for everyone to know the importance of data in an intelligence agency and how Data Science helps achieve the goals.

Data collection is obviously not a problem in the military. An enormous amount of data is collected every day from sensors arrayed in intelligence, surveillance, and reconnaissance equipment through land, sea, air, and space.

1. Military Logistics Take Advantage of Big Data to predict the Improvised Explosive Device (IED) placements using aggregated data from DoD, FBI, CIA, and other agencies
2. Data analysis with signals intelligence, full-motion video, photos, unstructured text, and social media identifies anomalies or that needs attention
3. Patterns and correlation in suicide bombings and IED placements
4. Monitoring troops’ health
5. Detect and track moving vehicles and foot soldiers
6. Identifying personnel for special training
7. Radar social media for any out-of-the-book activity instigating terrorism, culture, riots
8. Post-blast signature analysis

5. Social Media

When we talk about data science in social media, it is not just some Google Analytics jargon but an avenue of factors that contribute to users being glued to social media.

An average human spends at least 2.4 hours on social media. In times when 3.8 billion people use social media which is more than half of the world’s population, it is quintessential that we understand how Data Science governs the world of social media. Some of the use cases:

1. Personalized marketing
2. Customer sentiment analysis: Customer attitudes, the context of their engagement, the volume of service issues addressed through social
3. Market optimization: Campaign revenue, conversions, content performance, channel performance, post timing impact, most active engagers
4. Revenue Generation: Leads and sales by channels, revenue by product by channel, social revenue vs. direct revenue, search rankings and traffic, customer lifetime value, transaction size, and frequency
5. Content distribution on social media
6. Collecting and analyzing customer insights
7. Analysis of media content used to track engagement

How does Instagram use Data Science?

1. Targeted Advertising: as if IG knows what we exactly want By assessing the search preferences and engagement insights from its users, Instagram can sell advertising to companies who want to reach that particular customer profile and who might be most interested in receiving a particular marketing message
2. Fight Cyberbullying and remove offensive comments (they better do it)
3. Filter spam or bot accounts (the reason why “influencers” often lose their follower numbers)
4. Enhance User Experience When Instagram changed its feed from reverse-chronological order to showing posts that they believe users would like and share, machine-learning algorithms were put on the job to help sort the information and to better learn over time what is most valued and relevant for each user to create a personalized feed [Forbes article]

6. Travel

In the fast-paced world that humans live in, finding time to travel to a ticket office and getting a trip planned is a big ask. Thanks to the technology, we can now not only book tickets online, but find the best deals curated for our trip, find the lowest prices and loyalty points other than air miles. The following are some ways how the travel industry has been using Data Science.

1. Recommendation engines: 99% of all successful products have recommendation engines. Booking.com offers alternative destinations you might like for your next trip. When searching on Expedia for flights to London, you will be offered several accommodation options for your trip.
2. Flight fare and hotel price forecasting: Google flight tracking. If the price for flights to Hawaii usually drops below average a week before Thanksgiving, you will be offered to wait and book your flight closer to the date.
3. Intelligent travel assistants: chatbots for extended customer experience
4. Airlines use Data Science for route optimization
5. Marketing: Improve loyalty by offering tailored for MVCs (most valuable customers)
6. Sentiment analysis on social media
7. In-stay experience: Radisson Blu Edwardian Hotel in London uses a chatbot named Edward, and Las Vegas Hotel Cosmopolitan has a bot named Rose that answers any questions that guests have and helps 24/7
8. Customer Experience: Accor Hotels used TIBCO Spotfire to gauge customer satisfaction, feedback, and ways to improve customer engagement

7. Transportation

From self-driven cars to data-driven route adjustments, Data Science is hitting the roads. Major companies are implementing Data Science algorithms to optimize routes to save thousands of gallons of gas. Some of the use cases for Data Science in Transportation is as below:

UPS

I was reading an article for a school project where I learned how UPS uses data science to optimize package transport from drop-off to delivery. They have a network planning tool that operates on AI/ML for efficient supply chain and package routes. With Data Science in Optimized Package Routing, UPS plans to save $100 to $200 million by end of 2020.

Uber Eats

Uber Eats has been a mover in revolutionizing food delivery with Data Science. The data scientists at Uber Eats rely on machine learning, advanced statistical modeling, and staff meteorologists to optimize the delivery process on every possible variable — storms, holiday rushes, city traffic, cooking time, and such.

Amazon

Amazon has been the pedestal in any Data Science use case. With the advent of using bots for fast delivery for it’s Prine 1-day delivery or using Data Science to increase fulfillment center productivity, Amazon has been there, done that.

That’s it from my end for this blog. Thank you for reading! I hope you enjoyed the article. Do let me know what careers are you looking forward to exploring in your Data Science journey?

Happy Data Tenting!

Disclaimer: The views expressed in this article are my own and do not represent a strict outlook.

Know your author

Rashi is a graduate student at the University of Illinois, Chicago. She loves to visualize data and create insightful stories. When not rushing to meet school deadlines, she adores writing about technology, UX, and more with a good cup of hot chocolate.