The document discusses the growing field of data science. It begins by defining data science and explaining how the rise of big data and the internet of things has led to an increasing demand for data scientists. It then discusses the skills and qualifications needed for different types of data science roles, including data analysts, engineers, and research scientists. Finally, it provides resources for continuing to learn about data science.
data scientist the sexiest job of the 21st centuryFrank Kienle
Invited talk, describing the exciting work at Blue Yonder (www.blue-yonder.com),
'congress smart services - new business models' in Aachen, Germany 2015
Introduction to Data Science (Data Summit, 2017)Caserta
At DBTA's 2017 Data Summit in New York, NY, Caserta Founder & President, Joe Caserta, and Senior Architect, Bill Walrond, gave a pre-conference workshop presenting the ins and outs of data science. Data scientist has been dubbed the "sexiest" job of the 21st century, but it requires an understanding of many different elements of data analysis. This presentation dives into the fundamentals of data exploration, mining, and preparation, applying the principles of statistical modeling and data visualization in real-world applications.
data scientist the sexiest job of the 21st centuryFrank Kienle
Invited talk, describing the exciting work at Blue Yonder (www.blue-yonder.com),
'congress smart services - new business models' in Aachen, Germany 2015
Introduction to Data Science (Data Summit, 2017)Caserta
At DBTA's 2017 Data Summit in New York, NY, Caserta Founder & President, Joe Caserta, and Senior Architect, Bill Walrond, gave a pre-conference workshop presenting the ins and outs of data science. Data scientist has been dubbed the "sexiest" job of the 21st century, but it requires an understanding of many different elements of data analysis. This presentation dives into the fundamentals of data exploration, mining, and preparation, applying the principles of statistical modeling and data visualization in real-world applications.
Defining Data Science
• What Does a Data Science Professional Do?
• Data Science in Business
• Use Cases for Data Science
• Installation of R and R studio
Data Scientist has been regarded as the sexiest job of the twenty first century. As data in every industry keeps growing the need to organize, explore, analyze, predict and summarize is insatiable. Data Science is creating new paradigms in data driven business decisions. As the field is emerging out of its infancy a wide range of skill sets are becoming an integral part of being a Data Scientist. In this talk I will discuss the different driven roles and the expertise required to be successful in them. I will highlight some of the unique challenges and rewards of working in a young and dynamic field.
The presentation is about the career path in the field of Data Science. Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
A brief overview of the Rady School of Management at UC San Diego and how are developing ethical leaders for innovation-driven organizations. Find out if the Rady School is the MBA or Master of Finance program for you.
Smart Data Slides: Data Science and Business Analysis - A Look at Best Practi...DATAVERSITY
Google “citizen data scientist” today and you will see about 1M results. That number is data. It may be interesting, but it is meaningless without context. Sometimes it appears that we are drowning in data from systems and sensors but starving for insights. We definitely produce more of the former than the latter, which has created demand for more powerful tools to simplify the process and lower the skills requirement for analysis. As vendors build systems to meet this demand, we hear about the coming ”democratization” of big data as more people at varying levels within organizations are empowered to find meaning and improve their own performance with data-driven insights. This is a good thing, but it does require caution.
To paraphrase Col Jessup in A Few Good Men: You want answers? You can’t handle the data.
In this webinar, we will survey emerging approaches to simplifying analysis, and discuss the benefits, dangers, and skills required for individuals and organizations to thrive in the brave new world of analytics everywhere, for everyone.
Data Science is in high demand, the melting pot
of complex skills requires a qualified data scientist have made them the unicorns in today's data-driven landscape.
Ayasdi (ai-yaz-dee), a Silicon Valley start-up, has created technology that may prove to redefine an entire industry. Ayasdi provides a highly differentiated platform for data analysis based on the concept of Topological Data Analysis, first documented in the 1700’s – a platform that has the potential to shift the direction of future technology development. This case study briefly explores the “Big Data” industry as it is today, and the future implications that Ayasdi may have on the industry; including the strategic challenges Ayasdi has in positioning themselves as a contender and prospective leader within the “Big Data” and Enterprise Technology market segments.
Explains: What is Data Science? What is the difference between Data Science and Data Engineering, and between Data Science and Business Intelligence? What type of work do Data Scientists do, and what types of companies employ them? What is the job outlook for Data Science? What professional education is required?
What data scientists really do, according to 50 data scientistsHugo Bowne-Anderson
My talk at PyData NYC, 2018.
This is the abstract:
Hugo Bowne-Anderson, data scientist and host of the DataFramed podcast, will give you a view into the thinking of 50 leading data scientists from around the world about the trends driving the data science revolution. During his interviews with these thought leaders, Hugo discovered themes and lessons about the past, present, and future of data science.
Data science is different from Data Analytics,Data Engineering,Big Data.
Presentation about Data Science.
What is Data Science its process future and scope.
Data Science Presentation By Amit Singh.
"Sexiest job of 21st century"
Do you want to understand the emerging new data-driven jobs? This presentation discusses the emerging roles of Data Science and Data Engineering, and how they are related to Business Intelligence and Big Data. We will talk about skills and background needed for the jobs, and what education and certification is important.
Idiots guide to setting up a data science teamAshish Bansal
Some nuggets of how I started the data science practice at Gale Partners on a budget. Presented at the Toronto Hadoop Users Group (THUG) in April, 2015.
Building a Data Platform Strata SF 2019mark madsen
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT.
[This is a new, changed version of the presentations of the same title from last year's Strata]
Keynote - An overview on Big Data & Data Science - Dr Gregory Piatetsky-ShapiroData ScienceTech Institute
Data Science Tech Institute - Big Data and Data Science Conference around Dr Gregory Piatetsky-Shapiro.
Keynote - An overview on Big Data & Data Science Dr Gregory Piatetsky-Shapiro - KDnuggets.com Founder & Editor.
Paris May 23rd & Nice May 26th 2016 @ Data ScienceTech Institute (https://www.datasciencetech.institute/)
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
Data Scientist: The Sexiest Job in the 21st CenturyLyn Fenex
Presentation from WUSS 2015:
“Data scientist” is often used as a blanket title to describe jobs that are drastically different. There are plenty of articles and discussions on the web about what data science is, what qualities define a data scientist, how to nurture them, and how you should position yourself to be a competitive applicant. There are far fewer resources out there about the steps to take in order to obtain the skills necessary to practice this elusive discipline. This presentation will explore a collection of freely accessible materials and content to jumpstart your understanding of the theory and tools of Data Science. We will also discuss some of the variable understandings that companies use to define the roles of their Data Scientists.
Evolution of Data Analytics: the past, the present and the futureVarun Nemmani
This paper delves into the topic of advanced analytics, the current industry demands to utilize and analyze huge/diverse amounts of data, how big data analytics is becoming a part of the decision making process and to anticipate trends. This paper takes the reader from Analytics era 1.0 to the current Analytics era 3.0; shows the future projections of big data analytics and also the current leaders of the Big Data Analytics market.
A look at the evolution of analytics and its revolutionary potential to transform ordinary businesses, power new business models, enable innovation, and deliver greater value. http://www2.deloitte.com/us/en/pages/deloitte-analytics/articles/analytics-trends.html
Defining Data Science
• What Does a Data Science Professional Do?
• Data Science in Business
• Use Cases for Data Science
• Installation of R and R studio
Data Scientist has been regarded as the sexiest job of the twenty first century. As data in every industry keeps growing the need to organize, explore, analyze, predict and summarize is insatiable. Data Science is creating new paradigms in data driven business decisions. As the field is emerging out of its infancy a wide range of skill sets are becoming an integral part of being a Data Scientist. In this talk I will discuss the different driven roles and the expertise required to be successful in them. I will highlight some of the unique challenges and rewards of working in a young and dynamic field.
The presentation is about the career path in the field of Data Science. Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
A brief overview of the Rady School of Management at UC San Diego and how are developing ethical leaders for innovation-driven organizations. Find out if the Rady School is the MBA or Master of Finance program for you.
Smart Data Slides: Data Science and Business Analysis - A Look at Best Practi...DATAVERSITY
Google “citizen data scientist” today and you will see about 1M results. That number is data. It may be interesting, but it is meaningless without context. Sometimes it appears that we are drowning in data from systems and sensors but starving for insights. We definitely produce more of the former than the latter, which has created demand for more powerful tools to simplify the process and lower the skills requirement for analysis. As vendors build systems to meet this demand, we hear about the coming ”democratization” of big data as more people at varying levels within organizations are empowered to find meaning and improve their own performance with data-driven insights. This is a good thing, but it does require caution.
To paraphrase Col Jessup in A Few Good Men: You want answers? You can’t handle the data.
In this webinar, we will survey emerging approaches to simplifying analysis, and discuss the benefits, dangers, and skills required for individuals and organizations to thrive in the brave new world of analytics everywhere, for everyone.
Data Science is in high demand, the melting pot
of complex skills requires a qualified data scientist have made them the unicorns in today's data-driven landscape.
Ayasdi (ai-yaz-dee), a Silicon Valley start-up, has created technology that may prove to redefine an entire industry. Ayasdi provides a highly differentiated platform for data analysis based on the concept of Topological Data Analysis, first documented in the 1700’s – a platform that has the potential to shift the direction of future technology development. This case study briefly explores the “Big Data” industry as it is today, and the future implications that Ayasdi may have on the industry; including the strategic challenges Ayasdi has in positioning themselves as a contender and prospective leader within the “Big Data” and Enterprise Technology market segments.
Explains: What is Data Science? What is the difference between Data Science and Data Engineering, and between Data Science and Business Intelligence? What type of work do Data Scientists do, and what types of companies employ them? What is the job outlook for Data Science? What professional education is required?
What data scientists really do, according to 50 data scientistsHugo Bowne-Anderson
My talk at PyData NYC, 2018.
This is the abstract:
Hugo Bowne-Anderson, data scientist and host of the DataFramed podcast, will give you a view into the thinking of 50 leading data scientists from around the world about the trends driving the data science revolution. During his interviews with these thought leaders, Hugo discovered themes and lessons about the past, present, and future of data science.
Data science is different from Data Analytics,Data Engineering,Big Data.
Presentation about Data Science.
What is Data Science its process future and scope.
Data Science Presentation By Amit Singh.
"Sexiest job of 21st century"
Do you want to understand the emerging new data-driven jobs? This presentation discusses the emerging roles of Data Science and Data Engineering, and how they are related to Business Intelligence and Big Data. We will talk about skills and background needed for the jobs, and what education and certification is important.
Idiots guide to setting up a data science teamAshish Bansal
Some nuggets of how I started the data science practice at Gale Partners on a budget. Presented at the Toronto Hadoop Users Group (THUG) in April, 2015.
Building a Data Platform Strata SF 2019mark madsen
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT.
[This is a new, changed version of the presentations of the same title from last year's Strata]
Keynote - An overview on Big Data & Data Science - Dr Gregory Piatetsky-ShapiroData ScienceTech Institute
Data Science Tech Institute - Big Data and Data Science Conference around Dr Gregory Piatetsky-Shapiro.
Keynote - An overview on Big Data & Data Science Dr Gregory Piatetsky-Shapiro - KDnuggets.com Founder & Editor.
Paris May 23rd & Nice May 26th 2016 @ Data ScienceTech Institute (https://www.datasciencetech.institute/)
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
Data Scientist: The Sexiest Job in the 21st CenturyLyn Fenex
Presentation from WUSS 2015:
“Data scientist” is often used as a blanket title to describe jobs that are drastically different. There are plenty of articles and discussions on the web about what data science is, what qualities define a data scientist, how to nurture them, and how you should position yourself to be a competitive applicant. There are far fewer resources out there about the steps to take in order to obtain the skills necessary to practice this elusive discipline. This presentation will explore a collection of freely accessible materials and content to jumpstart your understanding of the theory and tools of Data Science. We will also discuss some of the variable understandings that companies use to define the roles of their Data Scientists.
Evolution of Data Analytics: the past, the present and the futureVarun Nemmani
This paper delves into the topic of advanced analytics, the current industry demands to utilize and analyze huge/diverse amounts of data, how big data analytics is becoming a part of the decision making process and to anticipate trends. This paper takes the reader from Analytics era 1.0 to the current Analytics era 3.0; shows the future projections of big data analytics and also the current leaders of the Big Data Analytics market.
A look at the evolution of analytics and its revolutionary potential to transform ordinary businesses, power new business models, enable innovation, and deliver greater value. http://www2.deloitte.com/us/en/pages/deloitte-analytics/articles/analytics-trends.html
Data lineage is a regulatory and internal requirement with potential to deliver significant operational and business benefits, but financial institutions can find it difficult to implement and complex to maintain as systems and regulatory requirements themselves, change quickly. The importance of understanding where the true source of the data is coming from, where the data flows to and what has changed cannot be overstated. The webinar defines data lineage and discuss implementation through the eyes of those that have implemented and sustained successful lineage solutions with significant benefits.
Listen to the webinar to find out about:
- Data management for data lineage
- Winning buy-in for projects
- Best practice implementation
- Operational and business benefits
- Expert practitioner advice
As 2017 begins, we are seeing big data and data science communities engage with new tools that specifically cater to data scientists and data engineers who aren’t necessarily experts in these techniques. Given rapid technological advances, the question for companies now is how to integrate new data science capabilities into their operations and strategies—and position themselves in a world where analytics can upend entire industries. Leading companies are using their data science capabilities not only to improve their core operations but also to launch entirely new business models.
Data, Insight, & Action - University of Utah IS 6482 Data Mining Jan 2015Richard Sgro
A presentation I gave at the University of Utah about the role of data in companies of various stages. Also featured a brief discussion on careers working with data.
Tracxn Big Data Analytics Landscape Report, June 2016Tracxn
New Enterprise Associates, Andreessen Horowitz, Accel Partners, Intel Capital and Khosla Ventures are the top 5 investors in big data analytics, with over 10 investments each.
Exploring the impact and evolution of Advanced Analytics Tools.pdfStats Statswork
The impact and evolution of advanced analytics tools have transformed how businesses operate, offering unprecedented insights and decision-making capabilities. Statstwork has been at the forefront of this evolution, providing cutting-edge solutions that leverage big data, machine learning, and AI. These tools enable companies to analyze vast amounts of data in real-time, identify trends, and predict future outcomes with high accuracy. As a result, businesses can optimize their operations, enhance customer experiences, and drive innovation. The continuous advancement of these tools promises even greater efficiencies and opportunities, making them indispensable in the modern data-driven landscape.
For more information contact:
https://www.statswork.com
& https://www.statswork.com/contact-us/
Contact our Experts:
Our Email id: info@statswork.com
Contact No: +91 8754467066
Exploring the impact and evolution of Advanced Analytics Tools.pdfStats Statswork
The impact and evolution of advanced analytics tools have transformed how businesses operate, offering unprecedented insights and decision-making capabilities. Statstwork has been at the forefront of this evolution, providing cutting-edge solutions that leverage big data, machine learning, and AI. These tools enable companies to analyze vast amounts of data in real-time, identify trends, and predict future outcomes with high accuracy. As a result, businesses can optimize their operations, enhance customer experiences, and drive innovation. The continuous advancement of these tools promises even greater efficiencies and opportunities, making them indispensable in the modern data-driven landscape.
For more information contact:
https://www.statswork.com
& https://www.statswork.com/contact-us/
Contact our Experts:
Our Email id: info@statswork.com
Contact No: +91 8754467066
Why is big data all the rage? What is this "data science" that people are talking about? Why do I care — as a customer, and as someone who works at a company generating data? In this talk, I present the case for models, and how we can use data science to create and use models of our customers and the society around us.
Similar to Data Scientist: the Sexiest Job of the 21st Century (20)
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. WUSS 2015
Experis | Friday, February 26, 2016 2
Our Time Today
– What is Data Science?
– Who needs a Data Scientist?
– What makes a Data Scientist?
– What kind of Data Scientist are
you?
Specialized
Talent and
Solutions
New
Work
Models
Shifts in
Business
4. WUSS 2015
Experis | Friday, February 26, 2016 4
What is Data Science, anyway?
5. WUSS 2015
Experis | Friday, February 26, 2016 5
Hal Varian, the chief economist at Google, is known to have
said, “The sexy job in the next 10 years will be statisticians.
People think I’m joking, but who would’ve guessed that
computer engineers would’ve been the sexy job of the 1990s?”
The Hot Job of the Decade
Source: HBR
6. WUSS 2015
Experis | Friday, February 26, 2016 6
The internet population now has over 2.1 billion people, and with every
website browsed, status shared, and photo uploaded, we leave a digital trail
that continually grows the hulking mass of big data.
Every minute, on average,
• YouTube users upload 48 hours of video
• Facebook users share 684,478 pieces of content,
• Instagram users share 3,600 new photos, and
• Tumblr sees 27,778 new posts published.
A perspective on how Big is Data
Source: That Conference, 2015
7. WUSS 2015
Experis | Friday, February 26, 2016 7
“By 2015, 4.4 million IT jobs globally will be created to support big
data, generating 1.9 million IT jobs in the United States,” said Peter
Sondergaard, senior vice president at Gartner and global head of
Research. “In addition, every big data-related role in the U.S. will
create employment for three people outside of IT, so over the next
four years a total of 6 million jobs in the U.S. will be generated by
the information economy.“
Source:
Gartner Symposium/ITxpo 2012
The Information Economy
16. WUSS 2015
Experis | Friday, February 26, 2016 16
A big data scientist understands how to integrate multiple systems and
data sets.
They need to be able to link and mash up distinctive data sets to
discover new insights.
This often requires connecting different types of data sets in different
forms as well as being able to work with potentially incomplete data
sources and cleaning data sets to be able to use them.
Sound familiar?
17. WUSS 2015
Experis | Friday, February 26, 2016 17
8 Data Skills to Get You Hired
• Basic Tools
• Basic Statistics
• Machine Learning
• Multivariable Calculus and Linear Algebra
• Data Munging
• Data Visualization & Communication
• Software Engineering
Source: Udacity
18. WUSS 2015
Experis | Friday, February 26, 2016 18
Most in-demand data skills
Source: WANTED Analytics, 2014
20. WUSS 2015
Experis | Friday, February 26, 2016 20
Source: Udacity
4 Types of Data
Science Roles
21. WUSS 2015
Experis | Friday, February 26, 2016 21
All joking aside, there are in fact some companies where being a data
scientist is synonymous with being a data analyst.
Your job might consist of tasks like pulling data out of MySQL
databases, becoming a master at Excel pivot tables, and producing
basic data visualizations (e.g., line and bar charts).
You may on occasion analyze the results of an A/B test or take the lead
on your company’s Google Analytics account.
.
A Data Scientist is a Data Analyst Who Lives in San Francisco:
22. WUSS 2015
Experis | Friday, February 26, 2016 22
You’ll see job postings listed under both “Data Scientist” and “Data
Engineer” for this type of position.
Since you’d be (one of) the first data hires, there are likely many low-
hanging fruit, making it less important that you’re a statistics or machine
learning expert.
A data scientist with a software engineering background might excel at
a company like this, where it’s more important that a data scientist make
meaningful data-like contributions to the production code and provide
basic insights and analyses.
Please Wrangle Our Data!
23. WUSS 2015
Experis | Friday, February 26, 2016 23
There are a number of companies for whom their data (or
their data analysis platform) is their product. In this case, the
data analysis or machine learning going on can be pretty
intense.
This is probably the ideal situation for someone who has a
formal mathematics, statistics, or physics background and is
hoping to continue down a more academic path.
Data Scientists in this setting likely focus more on producing
great data-driven products than they do answering operational
questions for the company.
We Are Data. Data Is Us:
24. WUSS 2015
Experis | Friday, February 26, 2016 24
A lot of companies fall into this bucket. In this type of role, you’re joining
an established team of other data scientists.
The company you’re interviewing for cares about data but probably isn’t a
data company. It’s equally important that you can perform analysis, touch
production code, visualize data, etc.
Generally, these companies are either looking for generalists or they’re
looking to fill a specific niche where they feel their team is lacking, such
as data visualization or machine learning.
Reasonably Sized Non-Data Companies Who Are Data-Driven:
25. WUSS 2015
Experis | Friday, February 26, 2016 25
Business Brain
• Concentrate on developing a “business brain” in addition to those
hard data skills.
• Data insights are useless without the foundational knowledge of the
business to which the data belongs.
• Keep your eyes and ears open to absorb as much understanding of
how a business and strategy works.
• Develop presentations to advise senior management in clear language
about the implications of their work for the organization.
• Develop ability to create examples, prototypes, demonstrations to help
management better understand the work.
26. WUSS 2015
Experis | Friday, February 26, 2016 26
The Changing Role of Analysts
• Statistics done by non-Statisticians,
• The growth of Statistics into new areas such as healthcare and financial
applications,
• Greater expectations by management for statisticians to “be responsive
and vital to today's business needs and to be able to prove their
contributions quantitatively,”
• The requirements of analyses to be timely as well as appropriate,
• The need to work with immense databases, and
• Adapting to new forms of communication. (Hahn & Hoerl, 1998)
28. WUSS 2015
Experis | Friday, February 26, 2016 28
Resources for ongoing learning
29. WUSS 2015
Experis | Friday, February 26, 2016 29
As Peter Sondergaard, global head of research at Gartner, said in a
2012 statement,
The most valued data analysts of tomorrow will be able not only to
derive insights from existing data sets, but also to tell the quantitative
future:
“Dark data is the data being collected, but going unused despite its
value. Leading organizations of the future will be distinguished by the
quality of their predictive algorithms. This is the CIO challenge, and
opportunity.”
In conclusion
The investment made in data collection demands putting the data to good use to drive business results forward.
Big data and analytics are all around these days. IBM projects that every day we generate 2.5 quintillion bytes of data. This means that 90% of the data in the world has been created in the last two years.
In 2013, Gartner projected that by 2015, 85% of Fortune 500 organizations would be unable to exploit big data for competitive advantage and that 6M jobs would be created as a result of the data explosion.
Couple of key points:
Using data for predictive analysis
30+% believe that their demand will exceed the supply of available talent
Also- traditional BI professionals may not have the profile required to impact these new requirements. Like SAS says- looking through the windshield instead of the rear view mirror.
According to this graph from Forbes, the greatest preponderance of Data Scientists are working in the services information industry-
Search engines (Google, Microsoft), social networks (Twitter, Facebook, LinkedIn), financial institutions, Amazon, Apple, eBay, the health care industry, engineering companies (Boeing, Intel, Oil industry), retail analytics, mobile analytics, marketing agencies, data science vendors (for instance, Pivotal, Teradata, Tableau, SAS, Alpine Labs), environment, utilities government and defense routinely hire data scientists, though the job title is sometimes different.
Traditional companies (manufacturing) tend to call them operations research analysts.
This article cited that there is very little difference in result between 2013 and 2015, except that there appears for be more growth of hiring in smaller companies.
But these companies corroborate the previous graph; platform and software developers, as well as service/consulting companies are the largest employers of this skills set.
In this graphic, It is this outer ring of skills that are fundamental in becoming a data scientist.
The skills in the inner part of the diagram are skills that most people will have some experience in one or more of them.
The other skills can be developed and learned over time, all depending on the type of person you are.
When it comes to being a data scientist it might be fair to say you are a ‘A jack of all trades and a master of some’.
This graph outlines some of the characteristics that EMC thinks makes a Data Scientist- notice that none of these are technical skills
Many resources out there may lead you to believe that becoming a data scientist requires comprehensive mastery of a number of fields, such as software development, data munging, databases, statistics, machine learning and data visualization.
Don’t worry. You don’t need to learn a lifetime’s worth of data-related information and skills as quickly as possible. Instead, learn to read data science job descriptions closely. This will enable you to apply to jobs for which you already have necessary skills, or develop specific data skill sets to match the jobs you want.
The big data scientist needs to be able to program, preferably in different programming languages such as Python, R, Java, Ruby, Clojure, Matlab, Pig or SQL.
You need to have an understanding of Hadoop, Hive and/or MapReduce.
Many resources out there may lead you to believe that becoming a data scientist requires comprehensive mastery of a number of fields, such as software development, data munging, databases, statistics, machine learning and data visualization.
Don’t worry. You don’t need to learn a lifetime’s worth of data-related information and skills as quickly as possible. Instead, learn to read data science job descriptions closely. This will enable you to apply to jobs for which you already have necessary skills, or develop specific data skill sets to match the jobs you want.
Hopefully this gives you a sense of just how broad the title ‘data scientist’ is. Each of the four company ‘personalities’ above are seeking different skillsets, expertise, and experience levels. Despite that, all of these job postings would likely say “Data Scientist,” so look closely at the job description for a sense of what kind of team you’ll join and what skills to develop.
A company like this is a great place for an aspiring data scientist to learn the ropes. Once you have a handle on your day-to-day responsibilities, a company like this can be a great environment to try new things and expand your skillset
It seems like a number of companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they’re looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis.
There will be less guidance and you may face a greater risk of flopping or stagnating.
Companies like CoreLogic, Lexus Nexus, RAND Corporation make their business out of identifying trends
Companies that fall into this group could be consumer-facing companies with massive amounts of data or companies that are offering a data-based service.
Some of the more important skills when interviewing at these firms are familiarity with tools designed for ‘big data’ (e.g., Hive or Pig) and experience with messy, ‘real-life’ datasets.
Being able to advice senior management in clear language about the implications of their work for the organization;
Having an, at least basic, understanding of how a business and strategy works;
Being able to create examples, prototypes, demonstrations to help management better understand the work;
[Use this slide to summarize what you know about the client’s current business challenges]