This document discusses the field of data science and the career opportunities it provides. It defines data science as a multidisciplinary field that uses scientific methods to gain knowledge from data. The key skills required for a data scientist include programming, quantitative analysis, communication skills, and intuition. R programming is presented as a popular tool for data scientists due to its versatility and large community. Finally, data science is described as a growing field with many career opportunities and high salaries, driven by the shortage of data science talent and organizations' need to make sense of large data sets.

1. A new beginning in your career
The Future of Data Science:
What does Data Science have in store?
Saif Shaikh
Innovations in Business Solutions Inc. (IIBS)
403-151 City Centre Dr.| Mississauga, ON | L5B 2T4|
Tel: (905)-268-0958|
E-mail: info@iibs.ca, Website: http://www.iibs.ca

2. Agenda
Defining data science
Skills required
Choosing R programming
Career opportunities
Q&A

3. Presenter introduction
Saif Shaikh
• Instructor at IIBS for the Data Scientist with R Programming course
• Consultant involved in the data analytics and modeling fields
• Formerly employed in the medical devices field
• Education: B.S.E.E. (Massachusetts), M.Eng. (McMaster)

4. • Relatively new multidisciplinary field where scientific procedures are used
to gain knowledge from data that can be in various forms
• With the arrival of big data (enormous data sets) thanks to inexpensive
data collection and storage, data science can be applied on it due to
inexpensive computational power
• A data scientist follows the data science process
• Types of data to be analyzed
 Structured: Stored as a model and organized such as a relational database or
spreadsheet
 Unstructured: No model or organization such a raw data including text, images,
sound files, video
 Semi-structured: Combination of the two such as a smartphone picture where the
image data is unstructured but the appended camera information is structured
Defining data science

5. Defining data science
Data science is multidisciplinary
http://blogs.gartner.com/christi-eubanks/three-lessons-crossfit-taught-data-science/

6. Defining data science
Various disciplines contribute to data science
https://en.wikibooks.org/wiki/Data_Science:_An_Introduction/A_Mash-up_of_Disciplines

7. Defining data science
The data science process
https://en.wikipedia.org/wiki/Data_science http://blog.operasolutions.com/bid/384900/what-is-data-science

8. • Data science subfields:
 Machine learning: Subfield of artificial intelligence that gives computers access to
data so they can learn themselves. It focuses on designing algorithms that can learn
and make predictions using the supplied data.
 Natural language processing: Subfield of artificial intelligence that uses computers to
understand and derive meaning from human languages without explicit clues.
 Deep learning: Subfield of machine learning concerned with algorithms inspired by
the structure and function of the brain called artificial neural networks.
 Data mining: Discovering patterns in data using different methods such as machine
learning, statistics and database systems to explain a phenomenon.
 Data visualization: Presentation of data in graphical format so patterns, trends and
correlations can be noticed easily.
 Statistical modeling: Subfield of mathematics used to find relationships between
variables in data using mathematical equations.
Defining data science

9. • Data science applications:
 Finance: Fraud detection, risk modeling, trading
 Communication, media and entertainment: Consumer insights, recommend content,
sentiment analysis, customer acquisition
 Healthcare and pharmaceutical: Clinical trials, genetics analysis, epidemic
forecasting
 Education: Behavioral classification, teacher effectiveness
 Manufacturing: Internet of Things, failure detection
 Retail: Shelf-space optimization, pricing, promotions, up-sell
 Energy and utilities: Smart meter analysis, service quality optimization, outage
management and restoration, accident prevention, exploration
 Agriculture: Climatology insights, field characteristics, weather information
 Transportation: Fleet vehicle maintenance, self-driving vehicles, logistic optimization
 Insurance: Fraud detection, call center optimization, risk assessment
Defining data science

10. • Challenging field with high potential having 4 prerequisites:
• Programming
 Analytic software such as R
 Ability to readily adapt and learn new software tools and libraries as required
• Quantitative analysis
 Ability to analyze data, develop algorithms and build models
 Basic statistics, probability theory, algebra, read mathematical notations
• Communication
 Ability to express your findings to a non-technical audience such as marketing or
sales
 Can easily tell a story with graphs and presentations
 Will be working with many departments so teamwork skills are needed
Skills required

11. • Intuition
 Ability to understand the business product will allow you to ask the correct questions
and find the correct answers
 Should have a curious mindset to solve problems
Skills required

12. Choosing R Programming
• Software environment for statistical computing and graphics
• Free and open source language
• Excellent R software tools to get started right away such as RStudio
• Own set of tools to write publication-quality plots and documentation
• Continuous backing by statisticians, scientists, scholars and research
institutes
• Publicly available for over 20 years with a regular release cycle
• Comes with a robust packaging system to allow developers and domain
experts to easily distribute their code, often written by researchers and
accompany scientific papers

13. Choosing R Programming
• All in one environment for data manipulation, visualization, machine
learning, reporting and more
• R popularity in academia is important because it creates a pool of talent
that feeds industry that in turns creates more demand for R talent
• Top tier companies using R such as: Facebook, Google, Twitter, Microsoft,
Uber, Airbnb, IBM, HP, Ford, Accenture, American Express, Citibank and
many more

14. TIOBE SEP17 Index: Number of search engine searches
https://www.tiobe.com/tiobe-index/r/
Choosing R Programming

15. RedMonk JUN17 Programming Language Rankings: Usage and discussion
http://redmonk.com/sogrady/2017/06/08/language-rankings-6-17/
Choosing R Programming

16. Scholarly articles with data science software
http://r4stats.com/articles/popularity/
Choosing R Programming

17. • One of the hottest jobs right now because of 3 reasons: Shortage of talent,
organizations continue to face enormous challenges in organizing data and
the need for data scientists is no longer restricted to tech giants
• Data science can apply to a number of occupations
• Glassdoor released a report in January 2017 with data scientist as the best
job in America (median salary $110,000)
• Careercast revealed in 2017 that data scientists have the best growth
potential over the next 7 years as they are the toughest job to fill (median
salary $111,267)
Career opportunities

18. Career opportunities
Indeed: Data scientist job trends
https://www.indeed.com/jobtrends/q-%22Data-Scientist%22.html

19. Career opportunities
Emsi: Q416 data science related occupations and their earnings
https://www.forbes.com/sites/emsi/2016/11/16/want-to-become-a-data-scientist-where-the-jobs-are-and-what-employers-are-looking-for/2/

20. Career opportunities
Robert Half: Top 10 tech jobs in 2017
https://www.roberthalf.com/sites/default/files/Media_Root/images/rht-pdfs/rht_0916_ig_sg2017-jobstowatch_nam_eng.pdf

21. • If you require further training, we have a course available at IIBS:
Data Scientist with R Programming (55 hours) – Saif Shaikh
Weekend classes
Mississauga, ON
905-268-0958
info@iibs.ca
Q&A