How to crack Big Data and Data Science roles is the flagship event of UpX Academy. This slide was used for the event on 10th Sept that was attended by hundreds of participants globally.

1. www.upxacademy.com

2. How to Crack Big Data & Data
Science Roles
Peeyush Taori
London Business School, AQR, AQR Asset Management
Institute, Indian School of Business
Manvender Singh
Founder, UpX Academy
MBA, Indian School of Business, Hyderabad

3. Agenda of Today’s Infosession
• Why is there buzz about Big Data, Machine Learning & Data Science
• What is the future of Big Data & Data Science as a career?
• Which companies are hiring for Big Data, Machine learning & Data
Science experts?
• How to position yourself to crack these roles?
• Interviews questions for Big Data & Data Science professionals
• Info about upcoming batches
• Q&A

4. A quick look at some people you will meet
Peeyush Taori Manvender Singh Madhu Reddy Arun Reddy
Chief Instructor Founder Student Services Student Services

5. What this session is
• Insights that you’ll not get on internet
• Focused on end goal(career opportunities) not starting
point(learning big data & data science)
• Understand big data & data science career opportunities across
geographies & industries
• Understand how to make career transition into Big data & Data
Science
• Address your questions related to career opportunities in Big data &
Data Science

6. What this session is not
• Not an introductory session on Big Data & Data Science
• Attend Big Data and Data Science trial classes
Big Data Trial class 12-1 pm Sunday 11th Sept
Data Science Trial class 1-2 pm Sunday 11th Sept

7. The buzz
“The Sexiest job of the 21st century “
“#1 most wanted hires in USA in 2016”
“Shortage of 140k to 190k data scientists in US alone by 2018”
“We’re moving from a mobile first world to AI first world”

8. How does Big Data analytics affect our daily lives?
More use cases on : http://upxacademy.com/2016/05/31/big-data-use-cases-industries/

9. The buzz
“The Sexiest job of the 21st century “
“#1 most wanted hires in USA in 2016”
“Shortage of 140k to 190k data scientists in US alone by 2018”
“We’re moving from a mobile first world to AI first world”

10. Machine learning applications
Self driving cars: Google, Baidu, Tesla
have implemented this technology.
Speech recognition: Google now,
Siri, Cortana
Genetics: Clustering algorithms are
used in genetics to help find genes
associated with a particular disease.
Face recognition: Facebook
automatically tags people in photos
where they appear.

11. Major acquisitions of ML and Big Data start-ups
2016
Intel acquired AI startup Nervana
Systems for $350 million
Twitter acquired machine learning
startup Magic Pony Technology for $150
million
Apple acquired Machine-Learning
Startup Turi for $200 Million
A non-profit AI research company,
OpenAI is funded by the famous business
magnate Elon Musk
2015
Microsoft acquired Metanautix, a Big
Data Analytics company

12. Big Data & Data Science - Together
• Fundamentally, part of same team
– Big Data programming and data science go hand in hand
• Firms need to deal with huge amounts of data
– Storage, Computation, Coherent Data View – Big Data
– Analytics, Statistics, Prediction – Data Science
• Let’s consider them in isolation for now

13. Big Data…What and Why?
 Characterized by 3V
• Volume
• Velocity
1. 3 Exabytes data(3 billion GB) is generated every day
2. 13 million new videos are added/month on Youtube.
3. 300 million photos uploaded/day on Facebook
• Variety
1. Structured, Semi-Structured, Unstructured
 Data is the most valuable asset
• Create insights and value

14. General Batch
Processing
Pregel
Dremel
Impala
GraphLab
Giraph
Drill
Tez
S4
Storm
Specialized
Systems
(iterative, interactive, ML, streaming, graph,
SQL, etc)
General Unified Engine
(2004 – 2013) (2007 – 2015?) (2014 – ?)
Mahout
Technology Landscape

15. Career Paths
Big Data Developer
• Excel at Big Data programming
• Hadoop, Pig, Hive, HBase, Spark
• Big Data Engineer, Consultant, Big Data Architect
Big Data Analytics
• Wear data analytics and big data programming hats
• Hadoop, Spark, Statistics, Analytics, Data Science, R, Python
• Big Data Analyst, Consultant, Big Data scientist

16. Big Data Jobs trends

17. Now, let us consider Data Science

18. Data Science…What and Why?

20. Skills a recruiters seeks

21. Typical Workday of a Data Scientist
 Gather data
• Programming, web scraping, DB
 Transform data
• DB Skills, Data Manipulation, Mathematics & Stats
 Data Modeling
• Machine Learning, Stats, Algorithms
 Data Reporting
• Inference, Business Acumen, Visualization

22. Data Science Job Trends

23. Demand across geographies
• Hottest market in US and Europe currently
• Demand outstrips supply
• Average salary of $1,00,000 for Big Data Engineers and $1,20,000 for Data
Scientists
• Similarly, £60,000 in UK
• Fastest growing job sector in India
• Average starting salary- INR 10 Lakhs
• Salaries shoot up with skill set and experience

24. Who is recruiting?
 Basically, everyone!!!
 Thought Leaders
• Google, Facebook, Amazon
 Data driven firms
• Uber, Twitter, NBC, Flipkart
 IT giants
• Catching up to the buzz
• Infosys, Cognizant, IBM, Accenture…..
 Data analytics focused startups/companies
• Arcadia, DataHero, Walmart Labs, Mu sigma, Fractal Analytics, Flutura
 Traditional Businesses
• DNV, Wal-Mart, Sears, DHL

25. Building a Resume
 Typical CV attention time span ~ 20-30 sec
 Prior Big Data/Data Science experience
• Most recent (Chronological)
• Project
• Clear, concise articulation of responsibilities and tools used
 Keep other experience to a minimum
 Demonstration of Big Data/Data Science Skills
• Certification
• Personal projects/POC/Competitions
 Finally, KISS
• Keep It Simple and Short

26. No prior experience?
 Demonstration of certified skills takes top priority
 Experience of working on Big Data/Data Science projects
 Experience of distributed computing
 Knowledge of fringe skills
 Intra-organization
• Low barriers to movement
• Certification and POC puts you in spotlight

27. What not to put in resume
• Recruiters receive lot of CVs
• Formatting and presentation matters
• Many firms use keyword extractor tool
• Buzzwords without knowledge is a strict no-no
• Keep length to max 2 pages

28. Big Data Top interview questions -
Generic
• Explain Big Data technologies
• Walk us through your previous Big Data project
• What is Hadoop and how is it related to MapReduce
• Hadoop deamons & their roles in Hadoop cluster
• Explain MapReduce
• Difference between Spark and Hadoop
• How do I deal with Streaming data
• Hive, Pig, and MapReduce

29. Big Data Top Interview Questions -
Specific
• Difference between Hadoop 1.0 and 2.0
• Architecture of Spark
• Indexing process in HDFS
• HDFS Block and Input Split

30. Data Science Top interview questions -
Generic
• Explain various Machine Learning techniques
• Walk us through your recent data science project
• Difference between supervised and unsupervised
• Assumption for a linear regression
• How do random forests work
• Trade-off between classification and regression

31. Data Science Top Interview Questions -
Specific
• How do you handle missing data
• Differentiate: Lift, KPI, model fitting
• Collaborative filtering, n-grams, KNN
• Assumptions of LDA and QDA

32. Class FAQs
• Where do the classes take place & what’s the class timings?
• Can I attend trial classes before attending?
• Do I have to purchase any software?
• What’s the difference between certificate of completion vs certification?
• What if I miss a class?
• How do I ask my doubts after the class?

33. Payment FAQs
• 20% off on course fee after trial classes. Valid till tomorrow midnight. Use UPX20
coupon code
• One time payment on website
• Credit card EMI option- currently available for ICICI, HDFC, Kotak & Amex
• 3 month interest free EMI option for select corporates.

34. Coordinates
Manav manav@upxacademy.com
Peeyush peeyush@upxacademy.com
Student Service Team: info@upxacademy.com
1800-123-1260
Fasahath/Madhu : 733-736-0431/37

35. Q&A