AI News, Data Science Bootcamps

Data Science Bootcamps

Thank you Sebastià Agramunt for taking the time to do a review of your experience at Data Science Europe Q

My thesis topic was simulating soft ferromagnets interacting with other magnetic materials such as antiferromagnets or superconductors.

I enjoyed this research because I developed all the code by myself allowing me to understand the physics of these magnetic systems and make predictions using simulations.

I was asked why would I like to become a Data Scientist and my answer was very honest: I love programming, math and computer science, something I realized when I was in academia.

: I felt I could learn data science by myself using MOOCS but joining a bootcamp would accelerate my learning.

An important point is to face a real interview, I thought this course helped me perform better in interviews.

We learned about databases, R, models in data science and finally preparation for interviews.

Every student chooses a topic (or proposes a new one) for their capstone project and develops their idea with the help of the mentors.

I learned about real data science problems faced by real industries, that’s something you can’t learn in books.

Also, someone may argue that you learn how to interview by doing a lot of interviews and that’s half true.

: Did you find employment within three months of finishing the bootcamp program ? A

: Do you feel alums are actively involved in the program through mentoring, giving presentations or in other capacities? A

: Alumni usually go to the course for a couple of days and present about the companies they are working for or a case study they’ve developed.

They do really care about you being happy in a job, after all, you will perform better in a company that way.

come from a theoretical physics background and I expected pure math explanations for the models (I mean, math formalism), but I realized that these explanations would take a lot of time and that’s not practical for the course.

Alumni network is great and actually you never finish the course, we always share interesting information and learn from each other.

  If you have attended and graduated from a Data Science Bootcamp and you’d like to do a review of your experience, we’d love to hear from you.

Data Science Dojo Bootcamp Q A

Get your most frequently asked bootcamp questions answered by David Langer, one of our instructors at Data Science Dojo.If you have any questions not answered here email:help@datasciencedojo.comQuestions:- Why trust Data Science Dojo?

Our in-person data science training has been attended by more than 2700+ employees from over 400 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook.--Learn more about Data Science Dojo here: what our past attendees are saying here: Us: Us: with Us: find us on:Google +:

Choosing a Coding Bootcamp:Your Comprehensive Guide And Complete School List

Coding bootcamps are intensive, accelerated learning programs that teach beginners digital skills like Full-Stack Web Development, Data Science, Digital Marketing, and UX/UI Design.

Most bootcamps help graduates find an internship or match students with an employer network- in fact, in Course Report's most recent research, 75% of alumni report being employed in programming jobs within 120 days of graduation.

Plenty of successful developers are self-taught using books, online resources, etc. Here are 6 things to consider when deciding if you should attend a bootcamp or teach yourself.

Schools look for the following skills in intensive bootcamp applicants: If a full-time coding bootcamp is not an option, consider a part-time bootcamp or online bootcamp.

Some 'zero to sixty' code schools are meant to bring beginners into the fold and other 'twenty to one-twenty' bootcamps aim to help current developers make a leap or learn a new technology stack.

Students study concepts over a longer period of time and spend 6-15 hours per week in class and another 10-15 hours per week on additional concepts.

Even if you choose to study online, you'll still have options between flexible or full-time courses. Students complete curriculum and activities on their own and meet with a mentor several times each week.

While you will still find the majority of dev bootcamps in major tech hubs like San Francisco and New York, bootcamps have sprung up in smaller markets since 2012 (there are coding bootcamps in over 70 US cities)!

Demographics Report, cities with the highest average salaries remain the large tech hubs with plenty of developer jobs: San Francisco, Oakland, Seattle, New York City, Denver, and Los Angeles were among the cities with highest mean and median salaries.

While language shouldn’t be the main deciding factor when choosing a bootcamp, students may have specific career goals that guide them towards a particular language.

Learning a specific language may lead you to a new job market and offer pathways to different career tracks, average salaries and areas of business.

Most bootcamps offer financing options, payment plans, and loan partnerships through companies like Earnest, Skills Fund, Pave, Climb Credit and Affirm, in addition to scholarships and discounts for women, military veterans, and underrepresented minorities.

Accredited coding bootcamps often have to submit their curricula (and any major curricula changes) for approval, invest in liability insurance in case of closure, and publicize their course catalog.

Coding Bootcamps have caught the attention of many politicians and government bodies, including the White House Office of the CTO and President Obama, who launched the TechHire initiative in March 2015.  In October 2015, the Department of Education announced EQUIP (Educational Quality through Innovative Partnerships). EQUIP is a US Department of Education initiative that encourages partnerships between universities and alternative education providers (read: bootcamps)!

What does EQUIP look like? Flatiron School's partnership with CUNY and General Assembly's partnership with Lynn U are examples.  In March 2017, CIRR (Council on Integrity in Results Reporting) was announced as a group of 17 bootcamps and member organizations who have developed a common framework for reporting, documenting, and auditing bootcamp student outcomes.

Data Science Bootcamps

We recently caught up with Juraj Kapasny, Co – founder at .We will be learning about the origins, selection process and outcomes at Data Science bootcamp.

During my last year at the university, I got an internship at Teradata as a Data Science consultant and stayed as a full-time employee after my studies.

After 2.5 years my friend Lukas Toma and I decided to start something on our own and founded Knoyd, a Data Science consulting business and shortly after,, a Data Science bootcamp.

With my background coming from consulting jobs for huge corporate entities like Vodafone, Metro or Saudi Telecom, I have more experience with traditional data analytics like linear regression, logistic regression, market basket analysis and SQL for data preparation.

You can learn all the theory but might have trouble gaining practical experience because companies are searching for people with practical experience (see the problem?).

: I like the one that says a Data Scientist is someone who knows more coding than a statistician and knows more statistics than a developer.

Plus I think navigating the business environment is a must for any Data Scientist working in the industry (like deadlines, approaching non-tech people etc.).

Because we work on real-world problems, the community includes companies, alums working in the industry as well as any new potential fellows.

: Can you describe the typical background (academic / professional) you look for in your fellows?

: The typical Basecamp student has at least a Bachelor’s degree in some quantitative field (or equivalent experience), some programming background and at least basic exposure to college math (linear algebra and entry level statistics).

We organized a hiring day at the end of the bootcamp, where students presented their work on their projects.

: Do you have a hiring day and what percent of students are typically placed from a company they meet at hiring day? A

: Yes, we organized a hiring day at the end of the bootcamp, where students presented their work on their projects.

If they are looking to fill a junior role, they should look for technical and coding skills, problem-solving and out-of-the-box thinking.

: Can you give a short summary of a typical day in the life / week in the life for your fellows? A

: Our day is structured into 2 blocks, 3 hours in the morning and 3 hours after lunch.

Sometimes the exercise can take longer than 3 hours (participants create a lot of functions and algorithms from scratch to improve their understanding and coding skills).

At the end of each week, we have a short session where participants show their approaches and compare them with others.

During the bootcamp, approximately 30% of time is allocated to the independent project work with a mentor available at all times for support –

For example, in the 1st week of the 1st cohort we got the feedback that the course was more theoretical than it should be (because they can always look for some stuff online in case they need to) and we immediately started to focus more on parts like why do we need this and where can we use it.

Nowadays, there are a lot of tools which can be used without knowing the theory behind the machine learning algorithms but I believe that it is exactly what differentiates strong Data Scientists from the rest.

: We believe that our way is unique because our participants work on real projects with real data during the course.

During the bootcamp, approximately 30% of time is allocated to the independent project work with a mentor available at all times for support.

At the end of each cohort, we organize a hiring day, where headhunters and other people from big and small companies are invited.

To prepare the students for real world situations we have them communicate with the company that provided us with the data project –

In lectures, we start with basic background like probability theory and statistics, algebra, data wrangling, data processing and APIs then we proceed with the basics of Machine Learning like regressions, trees and basic optimization techniques.

We go through supervised and unsupervised learning, NLP, recommenders, deep learning, reinforcement learning, data at scale (Apache Spark) and so on.

We try to improve this by project work in our bootcamp and by providing expert mentoring from Senior Data Scientists who have a lot of practical experience from different positions in different industries.

Once they move up in their careers to senior positions and will be looking for Data Scientists for their own teams, we hope that they will turn to Basecamp once more to help them to find the right talent.

: Do Basecamp alumni stay involved with the program and help make introductions / referrals for new fellows? A

However, our preferable target groups are people who want to transition from their jobs into Data Science or students who look for more practical experience to supplement their education.

: My advice is that they should never stop learning, even when they finish their education and believe they are ready for their career.

To find out more about you can either reach out to Juraj Kapasny, engage with Basecamp on Twitter @basecamp_ai, take a look at their online offering or reach out to their former students or Instructors.

Q & A with a Springboard Data Science Alumnus

While working toward his Master’s Degree in Information Systems, Adarsh became passionate about using data to help companies make smarter decisions.

He started searching for an intensive, mentor-led program that would help him learn Data Science skills that were not covered in his coursework, with the intention of pursuing a Data Science position after graduation.

Springboard seemed like the perfect fit because it offered the kind of one-on-one support that other online programs lacked.

A year into my Master’s degree, I started exploring options to learn data science skills that were not covered in my coursework.

I also interned at a tech startup in Los Angeles during Summer 2016 and thought that learning Python would allow me to implement some cool stuff at work.

In the first semester of my Master’s degree, I took a class in Statistics which involved different statistical tests, interpretation of statistical findings and statistics for data analysis.

I developed an interest in finding useful information hidden in the data, and I decided to pursue Data Science as a career.

I could see the entire curriculum, and just by looking at the website, going through alumni testimonials, and going through reviews, I trusted the program.

Finally, the capstone project was a huge plus, as it enabled me to develop a portfolio, work on a real-world project with an industry professional, and market myself to potential employers.

The Slack community and the office hours add a human touch and allow you to interact with people to solve problems.

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