Modern information and communication technology is increasingly capable of collecting and generating large amounts of data that need to be analyzed to become useful or profitable. In fact, these amounts quickly become too large for immediate human understanding, leading to a situation in which “we are drowning in data but starved for knowledge”.
Data science represents an essential technology to transform such data into knowledge. It allows the automated discovery of interesting regularities or anomalies in large databases, thereby surpassing standard statistical summarizing. Typical tasks include the construction of predictive and descriptive models for classification, regression, clustering, associations, and probabilistic inference.
The DTAI research group of the department of Computer Science, KU Leuven, presents a course that provides a gentle introduction to data science for professionals who need to analyze data themselves, interpret results obtained using data science techniques, or give guidance to data analysts. The course introduces the principles, techniques and methodology of data science. It provides the attendants with an overview of the wide variety of data science techniques available, insight in which techniques are useful for what kind of tasks, expertise with practical data science tools, and real-life case studies.
The target audience of this course consists of professionals who experience a need for a better understanding of data science: which tasks can be solved, which techniques can be used, which are their strengths and weaknesses.
Registration deadline: 20 January 2015
Course: 5-6 February 2015
We are now at 20, up from 17. I hope I find the time to write a one-page survival guide for UNIX, Python and Perl. Here’s one for R. The links to core data science concepts are below – I need to add links to web crawling, attribution modeling and API design. Relevancy engines are discussed in some of the tutorials listed below. And that will complete my 10-page cheat sheet for data science.
This post was written by the team behind DataCamp, the online interactive learning platform for data science.
After being dubbed “sexiest job of the 21st Century” by Harvard Business Review, data scientists have stirred the interest of the general public. Many people are intrigued by this job, namely because the name has an interesting ring to it. But it is exactly the name that also raises a lot of questions. Because what is a data scientist and what do data scientists do exactly? Many of us who devote their lives to data science have frequently been confronted with questions like these.
The answers to these questions are mostly not as straightforward as you would expect: a short search on Google with the string of words “How to become a data scientist” shows that the concept has different meanings to different people. In addition, many articles indeed suggest various tools, courses and applications for people to become a data scientist, and with good reason: the options are unlimited. But let’s face it, for someone that is not familiar with the field, this advice may sometimes seem like a jungle of information. What’s more, they could work demotivating: the descriptions are sometimes fearfully long and the many details often hit the readers as an overwhelming avalanche.
DataCamp’s Guide to Becoming a Data Scientist
With all this in mind, DataCamp decided to help those who can’t see the forest for the trees: we designed a step-by-step infographic that clearly outlines how you can become a data scientist in 8 easy steps. This visual guide is meant for everyone that is interested in learning data science or for everyone that has already become a data scientist but wants some additional resources for further perfection. The infographic is called “Become a data scientist in 8 easy steps”. Have a look at it!
If you are thinking about becoming a data scientist, do not be taken aback by the eight steps that are presented in the infographic. We would like to emphasize that becoming a data scientist takes time and personal investment, but that the journey is everything but dull! And don’t forget, there are plenty of courses available to set you on the right way.
If you are already a data scientist, drop us a line firstname.lastname@example.org if you think of other steps that you have undertaken in your professional journey.
The Social Network Analysis MOOC started this week on Coursera.
The course is given by Lada Adamic, an assiciate professor at MU who took a sabbatical year to go and work at Facebook. A year later she’s back with this inspiring course.
Lada Adamic will introduce you to social network mechanics and concepts. The tool of choice in this case is Gephi, which is a free to use graph/network visualisation tool.
This 8 week course combines video lectures with homework assignments during which you will learn to use Gephi and apply the freshly acquired knowledge on real data sets.
The course offers the possibility to apply for a certificate.
As a personal note from Glenn Vanderlinden:
I already went through the first couple of units and it looks rather interesting. It makes use of Gephi, which is to an extent an alternative to Neo4j. Might be interested for people who attended the last Meetup or who are interested in graph/network analysis. I hope this is useful for the community.
Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.
The Brussels Data Science Community is setting up a 12 week Boot Camp, if you want to participate follow this link.
We want to keep this post alive and up to date,so please send us your updates regularly.
You can use this form to update the list:
Data Science Bootcamp Programs – Full TIme, Part Time and Online
I’ve gotten a lot of inquires on options to move into Data science. This is my attempt to answer that question. If I excluded any programs from this, please feel free to ping me. You’ll see that there are quite a few options and you need to find the best fit based on your profile. This list does not include any university programs.
Everyone seems to reference the quote from Google Economist Hal Varian “Being a statistician is the sexiest job of the 21st century” and the McKinsey report about the shortage in Data Science talent.
Here we go…
Zipfian Academy : This is not a 0-60 school. It’s more like 40-80. They are currently about to graduate their second cohort.
Notes : Of all the Data Science bootcamps, Zipfian has the most ambitious curriculum. Graduates from the first cohort are currently working in Data Scientist roles across the Bay Area. I’m currently part of the second cohort
Location : San Francisco, CA
Requirement : Familiar with programming, statistics and math. Quantitative background
Duration : 12 weeks
Update : Since the initial post went up a few months ago, Zipfian Academy has added two more programs
Insight Data Science: Accepts only PhDs or PostDocs. They have completed 5 cohorts in Palo Alto and are opening up a new class in New York this summer. From their website, it does look like they have almost perfect placement. It is project based self directed learning, so if you need some hand holding or you’re not already very familiar with the material this may not be the program for you
Notes : No Fees, pays Stipend
Location : Palo Alto, CA / New York, NY
Requirement : PhD / PostDoc
Duration : 6 weeks
Insight Data Engineering : They’ll enroll the first cohort this summer. Bootcamp will focus on the data engineering track. It is project based self directed learning, so if you need some hand holding or you’re not already very familiar with the material this may not be the program for you
Notes : No Fees
Location : Palo Alto, CA
Requirement : strong background in math, science and software engineering
Duration : 6 weeks
The Data Incubator: Accepts only STEM PhDs or PostDocs. The first class is starting summer 2014.
Notes : Curriculum is mostly in R, though they support other languages (python, clojure, julia ). They have tiered pricing for the class, so you can pay for which tier meets your needs
Location : Berlin
Requirement : Experience with programming, databases, R, Python
Duration : 12 weeks
Data Science For Social Good : hosted by the University of Chicago. The students work with non-profits, federal agencies and local governments on projects that have a social impact
Notes : they focus on civic projects or projects with social impact
Location : Chicago, IL
Requirement : It looks like they target academics (undergraduate and graduate students)
Duration : 12 weeks
Metis Data Science Bootcamp : This looks like its modeled after the Zipfian program from a duration / structure / curriculum stand point. It is owned by Kaplan which also recently acquired Dev Bootcamp. Looks like the big .edu players are trying to make a play for the tech bootcamp space
Notes : It enrolls the first cohort Fall 2014
Location : New York, NY
Requirement : Familiarity with Statistics and Programming
Duration : 12 weeks
Data Science Europe Bootcamp : This looks like its modeled after the Insight program. Select a small group of very smart people with advanced degrees and help them get ready for Data Science roles in 6 weeks.
Notes : It enrolls the first cohort January 2015. Also if you don’t receive an offer for a quantitative job with 6 months of completing the course, you’ll receive a full refund on tuition paid
Location : Dublin, Ireland
Requirement : Quantitative Degree, Programming knowledge and Statistics background. It looks like they prefer graduate students and Post Docs but are open to applications from undergrads.
Duration : 6 weeks
Science to Data Science: They accept only PhDs / Post Docs or those close to completing their PhD studies. We are seeing more bootcamps adopt this model.
Notes : It enrolls the first cohort August 2014. There is a small registration fee for the course otherwise the program is free for participants
Location : London, UK
Requirement : PhD / Post Doc
Duration : 5 weeks
NYC Data Science Academy : This looks like its also modeled after the Zipfian 12 week immersive. Another option for non-postdocs on the east coast looking to make the transition to Data Science
Notes : It enrolls the first cohort February 2015. Just looking at the curriculum, it appears well thought out and seems to cover a lot of breadth. They focus on R and Python and spend significant amounts of the course time covering both ecosystems.
Location : Manhattan, NY
Requirement : Looks like they prefer people with STEM advanced degrees or equivalent experience in a Quantitative discipline or programming
Persontyle : London, UK. Offering R based Data Science short classes
Data Science Dojo : Silicon Valley, CA / Seattle, WA / Austin, TX. Offering data science talks, tutorials and hands on workshops and are looking to build a data science community
AmpCamp : This is run by UC Berkeley AMPLab. Over two days, attendees learn how to solve big data problems using tools from the Berkeley Data Analytics Stack. The event is also live streamed and archived on YouTube
DataInquest : Silicon Valley, CA. They organize hands on tutorials / training on big data technologies. They offer three different courses and cover quite a variety of the latest technologies. Session run on weekends
These bootcamps are popping up and thriving because there is currently an imbalance between demand and supply of Data Science talent and the acceptance rates at some of full time bootcamps are anywhere from 1 in 20 to 1 in 40
p.s : I need to stress that with any of the programs listed above, you need to do your due diligence and ask the tough questions to find out if it’s a good fit for you. You probably want to be on the look out for programs that are not transparent about their placement.
Update 1 – 05/14 : Added the new Zipfian programs, Persontyle
Update 2 – 07/14 : Added Metis, Data Science Europe, Science to Data Science
Update 3 – 08/14 : Added Data Science Dojo
Update 4 – 10/14 : Added AMPLab
Here bellow you can find an example of a Datascience Boot camp organised by Metis in New York. The objective is Learn Data Science in 12 weeks with 100% in-person instruction with experts from Datascope Analytics.
Bootcamp: January 12, 2015 – April 3, 2015
Application deadline December 8, 2014
This bootcamp runs in-person for 12 weeks, Monday through Friday, from 9 am – 6 pm. It is preceded by online pre-work focused on command line, Python, and installing various packages.
Applicants must have some previous experience programming (writing code) and studying or using statistics.
Upon graduating, you will be comfortable designing, implementing, and communicating the results of a data science project, including knowing the fundamentals of data visualization and having introductory exposure to modern big data tools and architecture such as the Hadoop stack. You should feel confident pursuing a job as an entry-level data scientist or data analyst. Read our syllabus