Future of data science as a professionJose Quesada
How can you thrive in a future where machine learning has been popular for a few years already?
In this talk, I will give you actionable advice from my experience training serious data scientists at our retreat center in Berlin. You are going to face these pointy, hard questions:
- What is the promise of machine learning? Has it happened yet?
- Is it easy to take advance of machine learning, now that most algorithms are nicely packaged in APIs and libraries?
- How much time should I spend getting good at machine learning? Am I good enough now?
- Are data scientists going to be replaced by algorithms? Are we all?
- Is it easy to hire talent in machine learning after the explosion of MOOCs?
Big data & data science challenges and opportunitiesJose Quesada
Even when most companies see the advantages of using more data in their decisions, few actually do. Why is that? A few ideas on challenges and opportunities for (middle-size) companies. Talk audience was an engineering association, where most people represented engineering-centric companies in Germany (often in manufacturing).
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.
Business leaders everywhere are looking to data to inform their decision making. Accompanying this demand are misunderstandings of what it takes to transform data into something that can inform a decision. What is the data infrastructure required? In this talk, I'll dispel some of these misunderstandings and discuss what it takes to build good data infrastructure. I'll discuss the components of a good data infrastructure. The best practices and available tools for gathering data, processing it, storing it, analyzing it and communicating the results. The goal is for these components to create a data infrastructure which can evolve from simple reporting to sophisticated insights for decision making.
Presented at OpenWest 2018
Presentation at FlowFactor 2019. Another look at the data science hierarchy of needs specifically looking at chasing Artificial Intelligence and Machine Learning.
Future of data science as a professionJose Quesada
How can you thrive in a future where machine learning has been popular for a few years already?
In this talk, I will give you actionable advice from my experience training serious data scientists at our retreat center in Berlin. You are going to face these pointy, hard questions:
- What is the promise of machine learning? Has it happened yet?
- Is it easy to take advance of machine learning, now that most algorithms are nicely packaged in APIs and libraries?
- How much time should I spend getting good at machine learning? Am I good enough now?
- Are data scientists going to be replaced by algorithms? Are we all?
- Is it easy to hire talent in machine learning after the explosion of MOOCs?
Big data & data science challenges and opportunitiesJose Quesada
Even when most companies see the advantages of using more data in their decisions, few actually do. Why is that? A few ideas on challenges and opportunities for (middle-size) companies. Talk audience was an engineering association, where most people represented engineering-centric companies in Germany (often in manufacturing).
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.
Business leaders everywhere are looking to data to inform their decision making. Accompanying this demand are misunderstandings of what it takes to transform data into something that can inform a decision. What is the data infrastructure required? In this talk, I'll dispel some of these misunderstandings and discuss what it takes to build good data infrastructure. I'll discuss the components of a good data infrastructure. The best practices and available tools for gathering data, processing it, storing it, analyzing it and communicating the results. The goal is for these components to create a data infrastructure which can evolve from simple reporting to sophisticated insights for decision making.
Presented at OpenWest 2018
Presentation at FlowFactor 2019. Another look at the data science hierarchy of needs specifically looking at chasing Artificial Intelligence and Machine Learning.
Advances in technology for capturing information have led to the promise of “Big Data” to dramatically alter the business environment. However, technology is only an enabler of aggregation and analysis. Many firms struggle to convert information to business knowledge and insights. Learn how organizations are using data to improve skill development at all levels and developing models for organizational structures to link these skills to executive decision-making.
Speakers: Dan McGurrin, Ph.D., NC State and Pamela Webber, Cisco
Back to Square One: Building a Data Science Team from ScratchKlaas Bosteels
Generally speaking, big data and data science originated in the west and are coming to Europe with a bit of a delay. There is at least one exception though: The London-based music discovery website Last.fm is a data company at heart and has been doing large-scale data processing and analysis for years. It started using Hadoop in early 2006, for instance, making it one of the earliest adopters worldwide. When I left Last.fm to join Massive Media, the social media company behind Netlog.com and Twoo.com, I basically moved from a data science forerunner to a newcomer. Massive Media had at least as much data to play with and tremendous potential, but they were not doing much with it yet. The data science team had to be build from the ground up and every step had to be argued for and justified along the way. Having done this exercise of evaluating everything I learned at Last.fm and starting over completely with a clean slate at Massive Media, I developed a pretty clear perspective on how to find good data scientists, what they should be doing, what tools they should be using, and how to organize them to work together efficiently as team, which is precisely what I would like to share in this talk.
Netflix was a trailblazing innovator in machine learning as applied to personalization and recommendation systems but there are many other applications of machine learning at Netflix, especially as we further evolve into a global entertainment company. This talk will give an overview of how machine learning is leveraged before content launches on Netflix and how machine learning can support the creative process and serve as a tool for decision makers in our content and marketing organization. The process of creating content is a high-touch, creative endeavor so we need to be similarly creative in the machine learning innovations we develop. From neural nets that predict audience size for content that doesn't exist yet, to NLP and deep learning techniques that mine scripts to highlight properties we need legal clearance for ... we are building unprecedented innovations. The talk will also broadly cover the challenges we face in this space, including data scarcity and making ML interpretable for non-technical stakeholders.
Operationalizing Machine Learning in the Enterprisemark madsen
TDWI Munich 2019
What does it take to operationalize machine learning and AI in an enterprise setting?
Machine learning in an enterprise setting is difficult, but it seems easy. All you need is some smart people, some tools, and some data. It’s a long way from the environment needed to build ML applications to the environment to run them in an enterprise.
Most of what we know about production ML and AI come from the world of web and digital startups and consumer services, where ML is a core part of the services they provide. These companies have fewer constraints than most enterprises do.
This session describes the nature of ML and AI applications and the overall environment they operate in, explains some important concepts about production operations, and offers some observations and advice for anyone trying to build and deploy such systems.
Join our #DataTalk on Thursdays at 5 p.m. ET. This week, we tweeted with Dr. Michael Wu, the Chief Scientist at Lithium, where he applies data-driven methodologies to investigate the complex dynamics of the social web.
Michael works with big data and has developed many predictive and prescriptive social analytics with actionable insights. His R&D won him the recognition as a 2010 Influential Leader by CRM Magazine.
You can see all tweets and resources here:
http://www.experian.com/blogs/news/about/data-scientists/
These are the slides from the Gramener webinar conducted on 16-Jan-2020.
- What skills & roles will help you deliver your analytics and data visualization projects?
- What skills do most teams miss to hire for?
In a Gartner survey, CIOs reported 'team skills' as their biggest barrier ⚠️ to data science. They have trouble deciding the skill mix ⚗️needed or in finding the right people for the job.
This webinar will show the skills and roles you must plan for. You will learn how to tailor this based on your organization's data maturity. It will help you decide whether to upskill teams or hire externally. The session will show you how and where to find talent.
Throughout the webinar you will learn:
- Critical skills & roles needed in your data science team?
- Tips for data science hiring. What aspirants should know about the jobs?
- Insights presented using real-world examples
Lean Analytics is a set of rules to make data science more streamlined and productive. It touches on many aspects of what a data scientist should be and how a data science project should be defined to be successful. During this presentation Richard will present where data science projects go wrong, how you should think of data science projects, what constitutes success in data science and how you can measure progress. This session will be loaded with terms, stories and descriptions of project successes and failures. If you're wondering whether you're getting value out of data science, how to get more value out of it and even whether you need it then this talk is for you!
What you will take away from this session
Learn how to make your data science projects successful
Evaluate how to track progress and report on the efficacy of data science solutions
Understand the role of engineering and data scientists
Understand your options for processes and software
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.
Best Practices for Scaling Data Science Across the OrganizationChasity Gibson
Effective data science in the enterprise is about aligning the right model, data, and infrastructure with the right outcomes. Most organizations today struggle to unlock the potential of data science to enhance decision-making and drive business value.
Join Forrester and Anaconda for a webinar to learn best practices for scaling data science across your entire organization. Guest speaker Kjell Carlsson, a Forrester Senior Analyst, and Peter Wang, Anaconda CTO, will share their unique perspectives on how to tackle five key challenges facing organizations today:
- Identifying, defining, and prioritizing valuable problems
- Building the right teams
- Leveraging the proper tools and platforms
- Iterating and deploying effectively
- Reaching end-users to generate value
Intro to Data Science for Non-Data ScientistsSri Ambati
Erin LeDell and Chen Huang's presentations from the Intro to Data Science for Non-Data Scientists Meetup at H2O HQ on 08.20.15
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
How to Build a Successful Data Team - Florian Douetteau (@Dataiku) Dataiku
As you walk into your office on Monday morning, before you've even had a chance to grab a cup of coffee, your CEO asks to see you. He's worried: both customer churn and fraudulent transactions have increased over the past 6 months. As Data Manager, you have 6 months to solve this problem.
As Data Manager, you know the challenges ahead:
- Multitudes of technology choices to make
- Building a team and solving the skill-set disconnect
- Data can be deceiving...
- Figuring out what the successful data product must be
Florian works in the “data” field since 01’, back when it was not yet big. He worked in successful startups in search engine, advertising, and gaming industries, holding various data or CTO roles. He started Dataiku in 2013, his first venture as a CEO, with the goal of alleviating the daily pains encountered by data teams all around.
A Infrared hyperspectral imaging technique for non-invasive cancer detection.IJERD Editor
Hyperspectral imaging(HI) is an emerging technology in the field of biomedical engineering which may be used as a non-invasive modality for cancer characterization. In this project, we propose to investigate hyperspectral imaging for the characterization of gastric cancer. The hyperspectral imaging has been used for the detection of various kinds of human cancer; breast, gastric, prostate and tongue. A research group has also investigated the use of reflectance imaging to detect canine cancer using fluorescent dyes. The use of hyperspectral imaging, however, has been limited for the characterization of cancer. In this project, we have already acquired many hyperspectral images of tumors. The malignant tissue has relatively low reflectance intensity compared to the benign tissue. The decreased reflectance intensity observed for malignant tumors is due to the increased microvasculature and therefore higher blood content of cancerous tissue relative to benign tissue. In the future, we will normalize and preprocess the spectral dataset. We propose to apply various algorithms such as Support Vector Machine, Linear Discriminant Analysis and Principal Component Analysis on the spectral data to discern the malignant and benign tumors. The advantage of cancer detection using hyperspectral imaging is that it is non-invasive, highly efficient and less time consuming than traditional methods like biopsy.
Advances in technology for capturing information have led to the promise of “Big Data” to dramatically alter the business environment. However, technology is only an enabler of aggregation and analysis. Many firms struggle to convert information to business knowledge and insights. Learn how organizations are using data to improve skill development at all levels and developing models for organizational structures to link these skills to executive decision-making.
Speakers: Dan McGurrin, Ph.D., NC State and Pamela Webber, Cisco
Back to Square One: Building a Data Science Team from ScratchKlaas Bosteels
Generally speaking, big data and data science originated in the west and are coming to Europe with a bit of a delay. There is at least one exception though: The London-based music discovery website Last.fm is a data company at heart and has been doing large-scale data processing and analysis for years. It started using Hadoop in early 2006, for instance, making it one of the earliest adopters worldwide. When I left Last.fm to join Massive Media, the social media company behind Netlog.com and Twoo.com, I basically moved from a data science forerunner to a newcomer. Massive Media had at least as much data to play with and tremendous potential, but they were not doing much with it yet. The data science team had to be build from the ground up and every step had to be argued for and justified along the way. Having done this exercise of evaluating everything I learned at Last.fm and starting over completely with a clean slate at Massive Media, I developed a pretty clear perspective on how to find good data scientists, what they should be doing, what tools they should be using, and how to organize them to work together efficiently as team, which is precisely what I would like to share in this talk.
Netflix was a trailblazing innovator in machine learning as applied to personalization and recommendation systems but there are many other applications of machine learning at Netflix, especially as we further evolve into a global entertainment company. This talk will give an overview of how machine learning is leveraged before content launches on Netflix and how machine learning can support the creative process and serve as a tool for decision makers in our content and marketing organization. The process of creating content is a high-touch, creative endeavor so we need to be similarly creative in the machine learning innovations we develop. From neural nets that predict audience size for content that doesn't exist yet, to NLP and deep learning techniques that mine scripts to highlight properties we need legal clearance for ... we are building unprecedented innovations. The talk will also broadly cover the challenges we face in this space, including data scarcity and making ML interpretable for non-technical stakeholders.
Operationalizing Machine Learning in the Enterprisemark madsen
TDWI Munich 2019
What does it take to operationalize machine learning and AI in an enterprise setting?
Machine learning in an enterprise setting is difficult, but it seems easy. All you need is some smart people, some tools, and some data. It’s a long way from the environment needed to build ML applications to the environment to run them in an enterprise.
Most of what we know about production ML and AI come from the world of web and digital startups and consumer services, where ML is a core part of the services they provide. These companies have fewer constraints than most enterprises do.
This session describes the nature of ML and AI applications and the overall environment they operate in, explains some important concepts about production operations, and offers some observations and advice for anyone trying to build and deploy such systems.
Join our #DataTalk on Thursdays at 5 p.m. ET. This week, we tweeted with Dr. Michael Wu, the Chief Scientist at Lithium, where he applies data-driven methodologies to investigate the complex dynamics of the social web.
Michael works with big data and has developed many predictive and prescriptive social analytics with actionable insights. His R&D won him the recognition as a 2010 Influential Leader by CRM Magazine.
You can see all tweets and resources here:
http://www.experian.com/blogs/news/about/data-scientists/
These are the slides from the Gramener webinar conducted on 16-Jan-2020.
- What skills & roles will help you deliver your analytics and data visualization projects?
- What skills do most teams miss to hire for?
In a Gartner survey, CIOs reported 'team skills' as their biggest barrier ⚠️ to data science. They have trouble deciding the skill mix ⚗️needed or in finding the right people for the job.
This webinar will show the skills and roles you must plan for. You will learn how to tailor this based on your organization's data maturity. It will help you decide whether to upskill teams or hire externally. The session will show you how and where to find talent.
Throughout the webinar you will learn:
- Critical skills & roles needed in your data science team?
- Tips for data science hiring. What aspirants should know about the jobs?
- Insights presented using real-world examples
Lean Analytics is a set of rules to make data science more streamlined and productive. It touches on many aspects of what a data scientist should be and how a data science project should be defined to be successful. During this presentation Richard will present where data science projects go wrong, how you should think of data science projects, what constitutes success in data science and how you can measure progress. This session will be loaded with terms, stories and descriptions of project successes and failures. If you're wondering whether you're getting value out of data science, how to get more value out of it and even whether you need it then this talk is for you!
What you will take away from this session
Learn how to make your data science projects successful
Evaluate how to track progress and report on the efficacy of data science solutions
Understand the role of engineering and data scientists
Understand your options for processes and software
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.
Best Practices for Scaling Data Science Across the OrganizationChasity Gibson
Effective data science in the enterprise is about aligning the right model, data, and infrastructure with the right outcomes. Most organizations today struggle to unlock the potential of data science to enhance decision-making and drive business value.
Join Forrester and Anaconda for a webinar to learn best practices for scaling data science across your entire organization. Guest speaker Kjell Carlsson, a Forrester Senior Analyst, and Peter Wang, Anaconda CTO, will share their unique perspectives on how to tackle five key challenges facing organizations today:
- Identifying, defining, and prioritizing valuable problems
- Building the right teams
- Leveraging the proper tools and platforms
- Iterating and deploying effectively
- Reaching end-users to generate value
Intro to Data Science for Non-Data ScientistsSri Ambati
Erin LeDell and Chen Huang's presentations from the Intro to Data Science for Non-Data Scientists Meetup at H2O HQ on 08.20.15
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
How to Build a Successful Data Team - Florian Douetteau (@Dataiku) Dataiku
As you walk into your office on Monday morning, before you've even had a chance to grab a cup of coffee, your CEO asks to see you. He's worried: both customer churn and fraudulent transactions have increased over the past 6 months. As Data Manager, you have 6 months to solve this problem.
As Data Manager, you know the challenges ahead:
- Multitudes of technology choices to make
- Building a team and solving the skill-set disconnect
- Data can be deceiving...
- Figuring out what the successful data product must be
Florian works in the “data” field since 01’, back when it was not yet big. He worked in successful startups in search engine, advertising, and gaming industries, holding various data or CTO roles. He started Dataiku in 2013, his first venture as a CEO, with the goal of alleviating the daily pains encountered by data teams all around.
A Infrared hyperspectral imaging technique for non-invasive cancer detection.IJERD Editor
Hyperspectral imaging(HI) is an emerging technology in the field of biomedical engineering which may be used as a non-invasive modality for cancer characterization. In this project, we propose to investigate hyperspectral imaging for the characterization of gastric cancer. The hyperspectral imaging has been used for the detection of various kinds of human cancer; breast, gastric, prostate and tongue. A research group has also investigated the use of reflectance imaging to detect canine cancer using fluorescent dyes. The use of hyperspectral imaging, however, has been limited for the characterization of cancer. In this project, we have already acquired many hyperspectral images of tumors. The malignant tissue has relatively low reflectance intensity compared to the benign tissue. The decreased reflectance intensity observed for malignant tumors is due to the increased microvasculature and therefore higher blood content of cancerous tissue relative to benign tissue. In the future, we will normalize and preprocess the spectral dataset. We propose to apply various algorithms such as Support Vector Machine, Linear Discriminant Analysis and Principal Component Analysis on the spectral data to discern the malignant and benign tumors. The advantage of cancer detection using hyperspectral imaging is that it is non-invasive, highly efficient and less time consuming than traditional methods like biopsy.
data science @NYT ; inaugural Data Science Initiative Lecturechris wiggins
inaugural Data Science Initiative Lecture @ Brown University
2015-12-04
https://www.eventbrite.com/e/data-science-at-the-new-york-times-tickets-19490272931
Booz Allen Hamilton created the Field Guide to Data Science to help organizations and missions understand how to make use of data as a resource. The Second Edition of the Field Guide, updated with new features and content, delivers our latest insights in a fast-changing field. http://bit.ly/1O78U42
Big Data [sorry] & Data Science: What Does a Data Scientist Do?Data Science London
What 'kind of things' does a data scientist do? What are the foundations and principles of data science? What is a Data Product? What does the data science process looks like? Learning from data: Data Modeling or Algorithmic Modeling? - talk by Carlos Somohano @ds_ldn at The Cloud and Big Data: HDInsight on Azure London 25/01/13
Which institute is best for data science?DIGITALSAI1
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Similar to Data science-retreat-how it works plus advice for upcoming data scientists (20)
Data science-retreat-how it works plus advice for upcoming data scientists
1. Data Science Retreat
Berlin, Mar 2014
http://datascienceretreat.com/
Introduction to the first Data Science school
in Europe
Plus advice for upcoming data scientists
2. Who Am I
Twitter: @quesada
Before: Consulting on predictive
models of ecommerce (CLV),
data scientist at GetYourGuide
3. What this talk is about: Two problems
• Making the jump from junior to senior data
science is hard (solution: data science retreat)
• Acquiring the skillset, even with killer online
courses, is hard (solution: meerkat method)
4. contents
• Data Science retreat
• The meerkat method
• Data Science retreat for companies
• Hiring
• Getting tailored courses at your location
• Advice to anyone on their path to be a data scientist
• Advice to companies growing a data team
6. It’s too hard for companies to find data scientists
“It takes 150 phone interviews
to find someone who is good
enough to bring in to continue
on-site”
Alex Kagoshima, Pivotal,
Berlin
7. People applying to Data scientist jobs have no experience
• Vincent Granville:
“There is no shortage of data scientists. For every linkedin
Job, there are several hundreds applications on average”
8. Data scientists need to program (5 year experience)
Stefan Schmidt (Amazon Berlin):
“It takes us months to fill our positions; we hire world-wide
for Berlin openings. Most profiles cannot program at the level
we need. We have engineers, but the data scientist needs to be
able to understand large projects and commit code”
9. Truth is, data scientist is a senior role
• Often, advising to the CEO directly
• This is why so many people with strong profiles and
lots of coursera courses cannot find jobs
10. The gap from junior to senior
• Junior:
• Has a technical degree
• Has done some courses online
• Has never worked with data that generates value to
companies
• Can apply ‘recipes’, but not think creatively about data
sources and algorithms
11. The gap from junior to senior
• Junior:
• Has a technical degree
• Has done some courses online
• Has never worked with data that generates value to
companies
• Can apply ‘recipes’, but not think creatively about data
sources and algorithms
This profile has no practical value for most companies
15. Formulating the analytical problem
• Finding the question
• Translating something vague into a
dependent measure and an actual set of
predictors
• What generates business value?
• The Business Model Canvas to design a data
product
• Key performance indicators; examples,
measurement, improvement
• Most business problems are not very well
defined. How do we make them actionable?
• Analyzing big success stories in data science
• Getting Buy-in
16. Getting data (APIs, feature engineering)
• Using APIs
• Using databases
• Parsing html; web scrapping
• Transforming data (reshape)
• Finding APIs
• Feature engineering
• Avoiding autocorrelation
• Removing features with low variance
• Detecting outliers
• Exploratory analyses
• Measuring predictor importance
17. Finding insights, making predictions
• Regression
• Linear regression, penalized
models
• non-linear regression
• SVM
• K-nearest neighbors
• regression trees + rule-based
models (random forests)
19. R
• R language fundamentals
• data structures (including
data.table)
• subsetting
• input/output
• functions/control flow
• vectorization
• split-apply-combine
advanced R
functional programming in R
Profiling
object systems
packaging
Rcpp
20. R
• advanced R
• functional programming in
R
• Profiling
• object systems
• packaging
• Rcpp
21. data at scale
• MapReduce
• MapReduce, Google 2004.
• Applications, extensions. Beyond
MapReduce.
• Big Data analysis
• Preparation and configuration
• Hadoop cluster overview.
• Practice: Uploading / downloading
/ moving files around, executing
jobs, checking for completion /
failure, etc.
22. data at scale
• Hive / Pig
• Defining a Hive table, querying a Hive table.
• Integrating R with Hive.
• An introduction to Pig.
• Mahout
• Executing clustering tasks. Visualizing the
results with R.
• Executing an item-to-item recommender.
• Cascading / Pattern
• Data flow modeling using PyCascading.
• Executing Machine Learning "Pattern"
algorithms.
24. Methodology: portfolio project
• Ten students per batch
• Pair programming and code reviews with mentors (guild model)
• Datasets come from companies (non-NDA only)
• Portfolio project, where the fellow demonstrates what he can do
end-to-end to deliver value
• Weekly presentation training to improve communication to
non-technical stakeholders (video feedback)
25. Who we are looking for
• Passion for generating insights from data
• Familiarity with trends in data growth, open-source platforms, and public
data sets.
• From familiarity to strong knowledge of statistical methods
• Some experience with statistical languages and packages, including Mahout, R
or python with pandas
• Some familiarity with visualization software and techniques (including
Tableau)
• Preferably, experience working hands-on with large-scale data sets
• Excellent written and verbal communications skills
37. advantages
• No need to find the right tutorial/book/whatever
• Spend more time at the border of your capability
• You Save time doing exercises that would be too easy
38. Advantages (cont)
• Higher project completion rates: all projects must have a
concrete output, so you will see your own progress in
tangible ways
• You will have an Easier time to demonstrate progress to
yourself and to others (the Mentor vouches for the
Learner).
• You will get more hands-on training than in other methods
49. Then Laura, Stefan’s friend,
pointed him to
Data Science Retreat
… an intensive course helping selected fellows
ramp-up fast for a career in data science. “Tell
me more…”. Stefan was very interested.
50. Stefan could
interview ten
data scientists that
were
as good as Ben.
He hired three, an
they jumped into
their roles with little
training.
Stefan was Ecstatic!
51. How It works
• As a sponsor you pay 7000€ in advance + 3000€ after the
data scientist worked on-site for 3 months and you know you
want to keep him.
• students who take the sponsorship agree to work for a reduced
salary (50%) the first 3 months. The salary savings during the
internship should cover the cost of the sponsorship. When the
students finish the program, no one has any obligations.
52. • You prepay 7000€ and become a sponsor.
• At this point, you don't know the students.
• But as a committed sponsor you participate actively
during the retreat, see the student's presentations, go
out for lunch with them, etc
• Thanks to these activities, you have now developed
strong relationships and know more about the students
than what would come out in interviews.
How It works: an example
53. How it works, an example (contd)
• You have set your target on a killer candidate: Klaas. You
make an offer, he accepts, and he starts working at your
location
• Klaas gets paid 50% of his negotiated salary. If Klaas’
60000€/year, that is a 5000€/mo cost, and produces
2500€ * 3 months = 7500€ savings, which covers your
initial investment of 7000€.
54. Data Science Retreat
Contact:
Jose Quesada, PhD,
Director, Data Science Retreat Berlin
jose@datascienceretreat.com
DO you want to be a sponsor?
55. Advice to anyone on their path to be a data scientist
• Try to find a mentor
• Spend as much time at the border of your ability
• Practice communication
• Having a culture that can integrate such individuals is as
hard as finding them. Interview your companies
• How do you move from being a junior person to being the
'CEO wisperer'? Spend time with people who are
56. Getting tailored courses at your location
• We listen to the people you need to train before we
design the course
• We will start with a dataset that is important for your
company. Lacking that, we’ll bring a public that is
relevant
• Enterprisey courses are supposed to be non-effective
57. Hire somebody who’s better at engineering and teach him data science or
hire somebody who’s better with data and teach him engineering?
• Is your culture ready? Because if you manage to attract
someone senior enough, they will sense if it's not
• The problems you have must be a good match for the data
scientists. People are extremely specialized, more so after
PhDs. If you have say graph theory/recommendation
problems, and hire someone with a time series background,
things will take a while no matter who good he is in his
field