AI News, Data Science Bootcamps

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.

109 Commonly Asked Data Science Interview Questions

For a data science interview, an interviewer will ask questions spanning a wide range of topics, requiring strong technical knowledge and communication skills from the part of the interviewee.

From this list of data science interview questions, an interviewee should be able to prepare for the tough questions, learn what answers will positively resonate with an employer, and develop the confidence to ace the interview.

We’ve broken the data science interview questions into six different categories: statistics, programming, modeling, behavior, culture, and problem-solving.

    Table of Contents Statistical computing is the process through which data scientists take raw data and create predictions and models backed by the data.

accordingly it is likely a good interviewer will try to probe your understanding of the subject matter with statistics-oriented data science interview questions.

Be prepared to answer some fundamental statistics questions as part of your data science interview.  Here are examples of rudimentary statistics questions we’ve found: Examples of similar data science interview questions found from Glassdoor:

To test your programming skills, employers will ask two things during their data science interview questions: they’ll ask how you would solve programming problems in theory without writing out the code, and then they will also offer whiteboarding exercises for you to code on the spot.

For the latter types of questions we will cover a few examples below, but if you’re looking for in-depth practice solving coding challenges, visit Interview Cake.

2.1 General 2.2 Big Data 2.3 Python For additional Python questions that focus on looking at specific snippets of code, check out this useful resource created by Toptal.

2.4 R 2.5 SQL Often, SQL questions are case-based, meaning that an employer will task you with solving an SQL problem in order to test your skills from a practical standpoint.

If you can’t describe the theory and assumptions associated with a model you’ve used, it won’t leave a good impression.  Take a look at the questions below to practice.

There are several categories of behavioral questions you’ll be asked: Before the interview, write down examples of work experience related to these topics to refresh your memory –

Of course, if you can highlight experiences having to do with data science, these questions present a great opportunity to showcase a unique accomplishment as a data scientist that you may not have discussed previously.

and asking questions that clarify points of uncertainty are a great way to show that you know how to ask the right questions (a trait that any data scientist should have).

There is no exact formula for preparing for data science interview questions, but hopefully by reviewing these common interview questions you will be able to walk into your interviews well-practiced and confident.

Data Science for Beginners video 1: The 5 questions data science answers

Get a quick introduction to data science from Data Science for Beginners in five short videos from a top data scientist.

These videos are basic but useful, whether you're interested in doing data science or you work with data scientists.

Data Science for Beginners is a quick introduction to data science taking about 25 minutes total.

Data Science uses numbers and names (also known as categories or labels) to predict answers to questions.

It might surprise you, but there are only five questions that data science answers: Each one of these questions is answered by a separate family of machine learning methods, called algorithms.

This is called multiclass classification and it's useful when you have several — or several thousand — possible answers.

Your credit card company analyzes your purchase patterns, so that they can alert you to possible fraud.

might be a purchase at a store where you don't normally shop or buying an unusually pricey item.

Common examples of clustering questions are: By understanding how data is organized, you can better understand - and predict - behaviors and events.

Typically, reinforcement learning is a good fit for automated systems that have to make lots of small decisions without human guidance.

The Life of a Data Scientist

They take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics and programming to clean, massage and organize them.

Then they apply all their analytic powers – industry knowledge, contextual understanding, skepticism of existing assumptions – to uncover hidden solutions to business challenges.

A high-level employee in a business that offers data-based services may be asked to structure big data projects or create new products.

$179,445 Broadly speaking, you have 3 education options if you’re considering a career as a data scientist: Academic qualifications may be more important than you imagine.

The majority of these degrees are in rigorous quantitative, technical or scientific subjects, including math and statistics (32%), computer science (19%) and engineering (16%).

To avoid wasting time on poor quality certifications, ask your mentors for advice, check job listing requirements and consult articles like Tom’s IT Pro “Best Of”

Requirements: Targeted towards the elite level, the CCP:DS is aimed at data scientists who can demonstrate advanced skills in working with big data. Candidates are drilled in 3 exams – Descriptive and Inferential Statistics, Unsupervised Machine Learning and Supervised Machine Learning – and must prove their chops by designing and developing a production-ready data science solution under real-world conditions.

Related SAS certifications include: Some data scientists get their start working as low-level Data Analysts, extracting structured data from MySQL databases or CRM systems, developing basic visualizations or analyzing A/B test results.

If you’d like to push beyond your analytical role, you could think about building/engineering/architecture jobs such as: In an oft-cited 2011 big data study, McKinsey reported that by 2018 the U.S. could face a shortage of 140,000 to 190,000 “people with deep analytic skills”

Companies of every size and industry – from Google, LinkedIn and Amazon to the humble retail store – are looking for experts to help them wrestle big data into submission.

data scientists may find themselves responsible for financial planning, ROI assessment, budgets and a host of other duties related to the management of an organization.

Data science is still white hot, but nothing lasts forever

Every day tech industry execs bemoan the lack of data scientists—the people who theoretically know how to look at the data your company generates, and delve into it to derive the all-important insights we keep hearing about.

At a Big Data panel hosted by Silicon Valley Bank and Hack/reduce in Boston, nearly 100% percent of the speakers talked about the dearth of qualified data scientists and the impact that is having on business.

According to Dr. Tara Sinclair,’s chief economist, the number of job postings for data scientist grew 57% for the first quarter this year compared to the year-ago quarter.

Yes, he said, young people should learn to program But, they should avoid getting into data science despite the current high demand because that demand will be short lived.

And there are more higher-level tools on the way from a cadre of second-generation data science companies that will improve workflow and automate how data interpretations are presented.

And as the technology solves more of these problems, there will also be a lot more human job candidates from the 100 graduate programs worldwide dedicated to churning out data scientists, said Peter Kuper, partner with In-Q-Tel, a VC firm affiliated with U.S. security agencies.

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