The document introduces data science and analytics, covering the following key points: - It defines data science and related fields such as analytics, machine learning, and artificial intelligence. - It discusses the history and importance of data science, as well as its applications across different industries such as ecommerce, healthcare, and marketing. - It outlines the various jobs and roles in the data science ecosystem, including data analysts, data scientists, data engineers, and analytics consultants, as well as the marketable skills required for each. - It provides advice on how to start a career in data science, including recommended technologies and skills to learn, as well as resources for building a portfolio.

1. Introduction to
DataScience & Analytics
Ann Venkataraman
Analytics Manager, TD Bank

2. Agenda:
 Speaker Introduction
 What is Data Science?
 Brief History, importance & truth behind the hype.
 Applications in different industries.
 Jobs Ecosystem
 Skills for different roles.
 How can you make a career in Data Science?
 Common Qs.
 Links and Resources
 Q&A
Ann Venkataraman - https://www.journeyofanalytics.com/
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3. Speaker Bio
 8+ years exp – strategy analytics
 Award-winning software engineer.
 Nasdaq, BlackRock, Fintech
 Mentor students and Women in STEM
 KubeCon diversity fellow 2019
 Author – “Data Science Jobs”
Ann Venkataraman - https://www.journeyofanalytics.com/
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4. What is Data Science?
 Millions of data points - Advanced insights
 Analytics / BI
 Machine learning
 Artificial intelligence – automation
 Statistics
 What is important for one domain is not for
the other:
 Petrochemical vs Automated ads.
Math &
ML Algo
SoftwareDomain
Ann Venkataraman - https://www.journeyofanalytics.com/
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5. History & Hype
 Tech to store and process high-quality data has become cheap and easily
available
 Need for hyper-targeting and mass customization – efficiency, profits & limited
loyalty
 Game changers – internet, ecommerce and globalization
 Next industrial revolution!
 NO, robots are not going to make you obsolete, but adapting is important.
 New methods of business - Baseline just to stay competitive, leverage to beat
the competition.
Ann Venkataraman - https://www.journeyofanalytics.com/
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6. Stages
Exploration
• Descriptive
- what is
happening
• Customer
demograp
hics
Diagnostic
• Why did
this
happen?
• Fraud
patterns
Predictive
• What will
happen
next?
• Offers for
churn
customers
Prescriptive
• How can
we make it
happen?
• Promote
new
products;
self-driving
cars
Ann Venkataraman - https://www.journeyofanalytics.com/
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7. Real-world Applications
❖ ecommerce - amazon, Walmart recommender
systems
❖ petrochemical - drill holes, sensors preventive
maintenance
❖ insurance - claims adjusting
❖ finance - algorithmic trading, apps for budgeting
and cut unnecessary bills!
❖ healthcare - cancer early diagnosis, medical
treatments with data from world over.
❖ Marketing - hyper targeting and product customer
churn, chatbots for customer service.
Ann Venkataraman - https://www.journeyofanalytics.com/
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8. Jobs Ecosystem
Data/BI
Analyst
Data
Scientist
Data
Engineer
Data Viz
analyst
Analytics
Consultants
Managers
New roles :
 Product managers
 Data Governance
 Quant analysts
 Biostatistician
Education:
MS? STEM specializations?
Ann Venkataraman - https://www.journeyofanalytics.com/
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9. Marketable Skills
 Data Analyst
 SQL + (R/Python) + Tableau
 Data Scientist:
 Above + ML Algos.
 Advanced insights [10:1 rule]
 Mission critical
 Data Engineer:
 ETL / Databases / APIs
 Software infrastructure, vendor
applications
 Streaming data from multiple sources
 Specialized ProdOps
 Analytics Consultants:
 Leverage data => Profits
 Multiple software tools & programming
languages
Ann Venkataraman - https://www.journeyofanalytics.com/
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10. How to Start a Career in DS?CORE
•R/Python + SQL
•Math & Stats
•ML Algo
•Data Viz
ADVANCED
•Communication
& Presentation
•Cloud Services
•Software
Engineering
•Automated ML
PORTFOLIO
•Domain
•Generic
Ann Venkataraman - https://www.journeyofanalytics.com/
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11. Links & Resources
 Learning Resources :
❖ Udemy – Courses under $20 . Kirill Eremenko
-
https://www.udemy.com/course/machinel
earning/
❖ Coursera – Pathway specializations with
capstone projects
❖ Udacity – nanodegree . Job search and
resume help. Mentoring available.
 Tableau for DataViz – free trial version, training.
 Projects and coding:
❖ Kaggle.com – datasets, starter scripts,
kernels & discussions.
❖ Journey of Analytics – free tutorials and
career advice
❖ Towards DataScience – Medium.com
publication.
❖ ODSC – articles, code and conferences.
❖ DataScience Central, KDNuggets.
Ann Venkataraman - https://www.journeyofanalytics.com/
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12. Common Qs
 Are data science bootcamps worth it to get a data science job?
 Data Science Portfolio Ideas
 Beginner – Shiny, compare diff models, automated reporting
 Intermediate – Sentiment analysis, Image classificaition
 Expert – building apps, automate model deployement, deep learning.
 No prior experience, unable to find a job. Cold start problems?
 Interview Prep
 Behavioral – what do you offer?
 Tech – show, don’t tell. Prepare for whiteboarding, coding and multiple formats.
Ann Venkataraman - https://www.journeyofanalytics.com/
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13. Connect for more Qs!
 @Anu_analytics [ I tweet job postings using the tags #DataScience
#Career ]
 Personal - https://www.linkedin.com/in/anupamaprv/
 Delaware Tech Meetup -
https://www.linkedin.com/groups/4181233/
 https://www.journeyofanalytics.com/
Ann Venkataraman - https://www.journeyofanalytics.com/
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14. THANKS!
Ann Venkataraman - https://www.journeyofanalytics.com/
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