Last year in March 2020, I had presented on campus of SRM Institute of Science and Technology, Ramapuram, Chennai for their computer science students on Career Opportunities in Artificial Intelligence. This year around, they invited me again for the same topic (!) but for their MCA (Master of Computer Applications) students. Since a year has passed where the job market, the technology industry and the entire world has changed irreversibly due to the ongoing pandemic, I decided to rework the talk in entirety.
Video of the talk is at:
https://venkatarangan.com/blog/2021/04/career-opportunities-in-artificial-intelligence-during-the-pandemic/
Today in the online talk I was expecting about 40-50 students, instead I was pleasantly surprised to see over 150 students attending the Zoom call. The topic was the same, “Career Opportunities in AI”. I covered the different job roles that are in general available in Artificial Intelligence, Machine Learning & Data technologies. Various industries which are employing AI – the list is of course expanding fast – I quoted some lesser-known verticals where phenomenal growth is happening. Added some real-world examples from Monster, Indeed and others. Finally, I shared few tips on how the students can prepare themselves on campus and off campus to land good jobs.
Career Opportunities in Artificial Intelligence during the pandemic
1. IMPORTANCE OF AI &
CAREER OPPORTUNITIES
FOR MCA STUDENTS
Venkatarangan Thirumalai
tncv.me | @venkatarangan
3.April.2021
SRM Institute of Science & Technology, Ramapuram
Campus
2. Agenda
History of A.I.
Areas where A.I. is being
used
Lesser known fields
Well Known Secrets
Major Job Roles in A.I. Major Job Roles in Data
Job Openings Examples
What should I do now?
Options in front of you after
M.C.A.
Life After M.C.A. : Myths
Jobs at risk due to
automation
Next steps after graduating
Last tip!
3. A Catalan Poet
Ramon Llull in 1308
writes “Ars Generalis
Ultima”
AI is 800
years old
4. AI Canvas
– Example
for fire
alarm
hbr.org/2018/04/a-simple-tool-to-start-making-decisions-with-the-help-of-ai
5. Areas where
AI is being
used
1. Banking & Finance
2. E-Commerce
3. Marketing: Online Advertisements, Social Media & more
4. Sales
5. Health Care
6. Communication
6. Lessor known
areas
1. Military and Defence
2. Construction
3. Agriculture
4. Education
5. Sports
6. Manufacturing
7. Transportation
8. Major job
roles in A.I.
1. A.I. Scientist – PhD in Mathematics (algebra, calculus,
algorithms, probability, and statistics), Cognitive Science
Theory, Bayesian Networking (including Neural Nets) &
who can create Algorithms (MIT/Stanford).
2. A.I. Scale Engineer – Top-End Backend/Cloud
Engineer. Knowledge of Programming, Infrastructure &
Storage: HDFS, Apache Spark, Apache Hudi. Work with
highly distributed data. Usage of Algorithms. Identify
bottlenecks in models & regenerate. Develop Model
pipelines.
9. Major job
roles in Data
3. Data Scientist – Knowledge of Algorithms, Qualified Statistian, Mathematics,
Love for Data, Data Drift & Build models and understand behaviour. Ability to
convert a business problem** to a machine-learning problem.
4. Data Engineer / Data Analyst – Programming Skills, Data Structure &
Formats, Python, SQL, JSON, Apache Avro & Apache Parquet formats, Data
Quality Index & Intro to Statistics
5. Data Labellers – Microsoft Excel, Multiple Data Processing Tools, Jupyter
Notebook, Trillium data quality & Ataccama Data Quality, Apache Spark
6. Data Pipeline Engineer – Analytic Skills, EDL routines, Programming skills,
Data Processing Skills, Data Formats & Visualisation.
14. What should I
do now?
1. Choose your Project wisely
2. Internship,
3. Online courses,
4. Github,
5. YouTube videos
15. Options in
front of you
after MCA
1. Go for higher studies
2. Start on your own
3. Get a job
16. Life After
MCA:
Myths
1. Job Market is hot, and I have a BE or M.C.A.
2. I must graduate from IIT or a top city college
3. I need to do higher studies to get placed
4. It is for companies to find me and train me
17. Jobs at
risk due to
Automation
by 2030
mckinsey.com/featured-insights/gender-equality/the-future-of-women-at-work-transitions-in-the-age-of-
automation
18. Next steps
after
graduating
1. Improve online/offline presentation
2. Attend local industry association events
3. Shortlist companies
4. Don’t wait for advertisements
5. Ask you will get it – Reach out to your network
origins of AI, (surprise) it started way back in 1308 when a Catalan Poet & Theologian Ramon Llull publishes his “Ars Generalis Ultima” a paper-based mechanical means to create new knowledge from combinations of concepts.
Refer: https://www.springboard.com/blog/5-careers-in-artificial-intelligence/
Comments for TNCV:
AutoML – Data Scientist work will reduce as AutoML.
Regenerate Models – Fresh Desk 3000 Models or so, 20 minutes to generate each model, every single day.
Identify edge conditions: Tesla should step on a black dot (garbage back) or go around. Driver Decision vs Self driving car.
High order affinity for data, unlike affinity for coding/programming languages. No discussion around OO or Function or Similar. Amount of data you handle in AI is more, Code is less. Most sophisticated ML programs are less than 2000 lines of code.
These people need to Ensemble Learning: ensemble methods use multiple learning algorithms to obtain better predictive performance.
AI & ML – In general, reducing the cost of decision. An AI or ML cannot replace a person with wider skills, it can easily replace a person who is an expert on one skill.
Refer: https://www.springboard.com/blog/5-careers-in-artificial-intelligence/
Comments for TNCV:
AutoML – Data Scientist work will reduce as AutoML.
Regenerate Models – Fresh Desk 3000 Models or so, 20 minutes to generate each model, every single day.
Identify edge conditions: Tesla should step on a black dot (garbage back) or go around. Driver Decision vs Self driving car.
High order affinity for data, unlike affinity for coding/programming languages. No discussion around OO or Function or Similar. Amount of data you handle in AI is more, Code is less. Most sophisticated ML programs are less than 2000 lines of code.
These people need to Ensemble Learning: ensemble methods use multiple learning algorithms to obtain better predictive performance.
AI & ML – In general, reducing the cost of decision. An AI or ML cannot replace a person with wider skills, it can easily replace a person who is an expert on one skill.
Refer: https://www.springboard.com/blog/5-careers-in-artificial-intelligence/
Comments for TNCV:
AutoML – Data Scientist work will reduce as AutoML.
Regenerate Models – Fresh Desk 3000 Models or so, 20 minutes to generate each model, every single day.
Identify edge conditions: Tesla should step on a black dot (garbage back) or go around. Driver Decision vs Self driving car.
High order affinity for data, unlike affinity for coding/programming languages. No discussion around OO or Function or Similar. Amount of data you handle in AI is more, Code is less. Most sophisticated ML programs are less than 2000 lines of code.
These people need to Ensemble Learning: ensemble methods use multiple learning algorithms to obtain better predictive performance.
AI & ML – In general, reducing the cost of decision. An AI or ML cannot replace a person with wider skills, it can easily replace a person who is an expert on one skill.
Refer: https://www.springboard.com/blog/5-careers-in-artificial-intelligence/
https://medium.com/@albertchristopherr/how-to-start-a-career-in-artificial-intelligence-in-2019-a-step-by-step-guide-b18ad32d1b1f
Comments for TNCV:
AutoML – Data Scientist work will reduce as AutoML.
Regenerate Models – Fresh Desk 3000 Models or so, 20 minutes to generate each model, every single day.
Identify edge conditions: Tesla should step on a black dot (garbage back) or go around. Driver Decision vs Self driving car.
High order affinity for data, unlike affinity for coding/programming languages. No discussion around OO or Function or Similar. Amount of data you handle in AI is more, Code is less. Most sophisticated ML programs are less than 2000 lines of code.
These people need to Ensemble Learning: ensemble methods use multiple learning algorithms to obtain better predictive performance.
AI & ML – In general, reducing the cost of decision. An AI or ML cannot replace a person with wider skills, it can easily replace a person who is an expert on one skill.
Refer: https://www.springboard.com/blog/5-careers-in-artificial-intelligence/
Comments for TNCV:
AutoML – Data Scientist work will reduce as AutoML.
Regenerate Models – Fresh Desk 3000 Models or so, 20 minutes to generate each model, every single day.
Identify edge conditions: Tesla should step on a black dot (garbage back) or go around. Driver Decision vs Self driving car.
High order affinity for data, unlike affinity for coding/programming languages. No discussion around OO or Function or Similar. Amount of data you handle in AI is more, Code is less. Most sophisticated ML programs are less than 2000 lines of code.
These people need to Ensemble Learning: ensemble methods use multiple learning algorithms to obtain better predictive performance.
AI & ML – In general, reducing the cost of decision. An AI or ML cannot replace a person with wider skills, it can easily replace a person who is an expert on one skill.
Refer: https://www.springboard.com/blog/5-careers-in-artificial-intelligence/
https://medium.com/@albertchristopherr/how-to-start-a-career-in-artificial-intelligence-in-2019-a-step-by-step-guide-b18ad32d1b1f
Comments for TNCV:
AutoML – Data Scientist work will reduce as AutoML.
Regenerate Models – Fresh Desk 3000 Models or so, 20 minutes to generate each model, every single day.
Identify edge conditions: Tesla should step on a black dot (garbage back) or go around. Driver Decision vs Self driving car.
High order affinity for data, unlike affinity for coding/programming languages. No discussion around OO or Function or Similar. Amount of data you handle in AI is more, Code is less. Most sophisticated ML programs are less than 2000 lines of code.
These people need to Ensemble Learning: ensemble methods use multiple learning algorithms to obtain better predictive performance.
AI & ML – In general, reducing the cost of decision. An AI or ML cannot replace a person with wider skills, it can easily replace a person who is an expert on one skill.
Refer: https://www.springboard.com/blog/5-careers-in-artificial-intelligence/
https://medium.com/@albertchristopherr/how-to-start-a-career-in-artificial-intelligence-in-2019-a-step-by-step-guide-b18ad32d1b1f
Comments for TNCV:
AutoML – Data Scientist work will reduce as AutoML.
Regenerate Models – Fresh Desk 3000 Models or so, 20 minutes to generate each model, every single day.
Identify edge conditions: Tesla should step on a black dot (garbage back) or go around. Driver Decision vs Self driving car.
High order affinity for data, unlike affinity for coding/programming languages. No discussion around OO or Function or Similar. Amount of data you handle in AI is more, Code is less. Most sophisticated ML programs are less than 2000 lines of code.
These people need to Ensemble Learning: ensemble methods use multiple learning algorithms to obtain better predictive performance.
AI & ML – In general, reducing the cost of decision. An AI or ML cannot replace a person with wider skills, it can easily replace a person who is an expert on one skill.
Refer: https://www.springboard.com/blog/5-careers-in-artificial-intelligence/
https://medium.com/@albertchristopherr/how-to-start-a-career-in-artificial-intelligence-in-2019-a-step-by-step-guide-b18ad32d1b1f
Comments for TNCV:
AutoML – Data Scientist work will reduce as AutoML.
Regenerate Models – Fresh Desk 3000 Models or so, 20 minutes to generate each model, every single day.
Identify edge conditions: Tesla should step on a black dot (garbage back) or go around. Driver Decision vs Self driving car.
High order affinity for data, unlike affinity for coding/programming languages. No discussion around OO or Function or Similar. Amount of data you handle in AI is more, Code is less. Most sophisticated ML programs are less than 2000 lines of code.
These people need to Ensemble Learning: ensemble methods use multiple learning algorithms to obtain better predictive performance.
AI & ML – In general, reducing the cost of decision. An AI or ML cannot replace a person with wider skills, it can easily replace a person who is an expert on one skill.
Refer: https://www.springboard.com/blog/5-careers-in-artificial-intelligence/
https://medium.com/@albertchristopherr/how-to-start-a-career-in-artificial-intelligence-in-2019-a-step-by-step-guide-b18ad32d1b1f
Comments for TNCV:
AutoML – Data Scientist work will reduce as AutoML.
Regenerate Models – Fresh Desk 3000 Models or so, 20 minutes to generate each model, every single day.
Identify edge conditions: Tesla should step on a black dot (garbage back) or go around. Driver Decision vs Self driving car.
High order affinity for data, unlike affinity for coding/programming languages. No discussion around OO or Function or Similar. Amount of data you handle in AI is more, Code is less. Most sophisticated ML programs are less than 2000 lines of code.
These people need to Ensemble Learning: ensemble methods use multiple learning algorithms to obtain better predictive performance.
AI & ML – In general, reducing the cost of decision. An AI or ML cannot replace a person with wider skills, it can easily replace a person who is an expert on one skill.