How to crack Big Data and Data Science roles
- 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