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Data Science Master Guide: Top 10 Tips to Ace Your Interview Like a Pro

Data Science Master Guide: Top 10 Tips to Ace Your Interview Like a Pro

More than 80% of Data Science jobs demand three to five years of experience from you. But there’s no denying it, data science is the hot job of the future.

Albeit, you’ve got to be a pro from day zero!

Despite it being a new line of profession, data science commands huge money. Advertised jobs pay upwards of $80,000 on average, exceeding jobs requiring a university degree by over $8,000.

In short, data scientists earn top dollar for what they do.

And the Good News?

There’s still enough room for aspiring data scientists. LinkedIn listed Data Science as one of the fastest growing jobs today.

Top 20 Emerging Jobs

Source: LinkedIn’s 2017 U.S. Emerging Jobs Report


A recent report from IBM has even juicier stats. The year 2015 saw 2.35 million data science/analytics (DSA) jobs. But, Data Science job openings should grow by 15% by 2020.

Given its growth rate, Data Science jobs will hit 2.714 million in 2020. And the demand for data professionals is going to surge by 39% during this period.

It’s safe to infer that the demand for data professionals is higher than the supply. DSA jobs typically take five days longer than average to be filled.

DATA SCIENCE/ ANALYTICS LANDSCAPE

In truth, these are the best times to be a data scientist.

The Data Science Mix

Data Science Mix

Image Credit: Creative Commons

Most times, people don’t get what Data Science job roles entail.

This confusion is because data science is not an isolated field. There is so much going on it requires a mix of disciplines to get it right.

First off, Data science is an interdisciplinary field that combines mathematics and scientific methods for data analysis. An analysis in this sense means discovering useful data patterns for creating products or solving problems.

Designing and building architecture to store this data is still under the Data Science Spectrum.

You should see data scientists manning core roles in the health industry. The health industry commands 30% of the world’s data. Everyone needs data, including the automotive and energy industries.

Note, Data Science isn’t Artificial intelligence (AI). AI consumes insightful data supplied via Data Science.

Your Path to Data Science Mastery

Just before we dive deep into data science mastery, here’s your path,

  1. Data Scientist Qualifications

  2. Branches of Data Science

    • The Data Science Generalist

    • Data Analysts

    • Machine Learning Engineer

    • Data Engineer

  3. Buff-Up your skill-set (Tip #1)

    • Mathematics Skills

      • Statistics

      • Multivariable Calculus and Algebra

    • Computer Science Skills

      • Programming Skills

      • Data Wrangling

      • Machine Learning

    • Data Communication Skills

      • Data Visualization

      • Data Intuition

  4. Master Data Science Tools (Tips #2 to #4)

    • Programming tools (Tip #2)

      • Python

      • R

    • Pick-up on data collection and cleaning tools (Tip #3)

    • Master machine learning and modeling tools (Tip #4)

  5. Interview Preparations (Tips #5 to #10)

    • Insight into Question Types: Categories of Interview Questions (Tip #5)

      • Technical and Statistical Sessions

      • Communication skills and work ethics

    • Preliminary Prep for your Data Science Interview (Tip #6)

      • Read the Job Description

      • Review your Resume

      • Remember the Intricate Details of Past Projects

    • Check Out Sample Questions Online (Tip #7)

    • Practice Your Data Science Skills (Tip #8)

    • Make a List of Important Questions to Ask Your Interviewer (Tip #9)

    • Read Other People’s Experiences Before the Big Day (Tip #10)

  6. Wrapping It Up

Now let’s put some flesh on this skeleton.

1. What Qualifies You to Be a Data Scientist?

What qulaifies you to be a data scientist

Image Credit: Creative Commons

Drop all the fancy degrees aside. The most important requirement for a data scientist role is grit.

Plenty of grit.

Because you’ll need more than you can muster to keep going up against complex data-sets.

Grit to keep learning and never stop questioning. Grit to move from one new technology to another. Grit to keep churning results faster than you did the previous time.

The world of a data scientist is about your ability to adapt than it is about your qualifications. Employers and clients love results. And will choose a candidate with a vast portfolio over a Ph.D. who has never built anything.

But qualifications matter too.

To be eligible, this field requires a sound knowledge of statistics and data processing. So if you loathe Math, sorry, try something else.

Another thing to love is programming. All data science tools need their fair share of complex coding. But well, that’s why they pay you top dollar right?>

No, it isn’t all dark and scary. You can become a great data scientist. But only if you put in a decent effort.

Today, lots of helpful online courses are available to tutor you from novice to expert.

So even if you don’t have university degrees, you still get another chance to go after your dreams. Just have grit, lots of it.

2. Branches of Data Science

branches of data science

Image Credit: Flickr

Like every other discipline, data science has core skills and specializations.

Of course, there are generalists and those who know the major aspects of data science too. But for these people, their depth of knowledge is sacrificed for the breadth. The fields are,

  1. Data Science Generalist

  2. Data Analysts

  3. Machine Learning Engineer

  4. Data Engineer

I’ll talk about these four core fields under data science. You decide what field you fall under.

1. The Data Science Generalist

data science generalist

Image Credit: Creative Commons

We’ve met this guy in the previous paragraph. He knows enough about all core aspect of data science to be useful to your company.

You’d wonder why there are generalists.

Well, most companies are not data companies. They want a guy who can solve all their data analysis problem.

This means that you will delve into other aspects of data science and get your hands dirty time after time.

So expect to be doing some machine learning duties. As well as data analysis and visualization.

Don’t worry, you don’t have to know them as well as the specialists.

2. The Data Analyst


data analyst

Image Credit: Creative Commons

This guy lives and breathes data. He is the one who decodes the pattern in a complex dataset. He is also responsible for communicating the data in concise and clear visuals.

As a data analyst, you must be comfortable with spreadsheets, database programming, and data communication. You are easily the most sought-after guy for start-ups. 

But that’s if you are not poached by the big guys first.

3. The Machine Learning Engineer

This guy focuses on creating data-sets that can simulate human intelligence. Also known to be the most tasking Data Science role, but it also is the juiciest.

To qualify as a great Machine Learning Engineer, you must be very conversant with programming languages.

You need a sound knowledge of Math, Statistics and a bit of Computer Science. Companies who hire Machine Learning Engineers are those whose data is their product.

machine learning engineer

Image Credit: Creative Commons

4. The Data Engineer

This is the software engineering guy in the mix. When most companies start out, they make the mistake of storing their data all over the place. As soon as they grow and expand, they need to clean up this mess. This point is where you come in.

As a data engineer, you are responsible for creating infrastructure for storing and accessing data. Your software engineering skills must be top notch. And your innate ability to think on your feet, unparalleled.

The Top 10 tips to Ace Your Data Science Interview

Now you understand data science, let’s proceed to crack your job interview in no time. Let’s explore the 10 tips!

Read on.

Section I / Tip #1: Buff-Up Your Skill-Set

If you don’t have the skills you won’t get ahead. Simple.

I’ve already mentioned a couple of these skills in bits. Let’s zoom in on them. You can broadly divide these skills into three,

  • Mathematics Skills

  • Computer Science Skills

  • Data Communication Skills

Let’s get to the details of each skill.

Mathematics Skills

maths skills

Image Credit: Pixabay

Under mathematics, you’ll need statistical, multivariable calculus and algebra skills. Let’s explore each of them.

Statistics

To compete in this industry, you have to take your statistical skills serious. Most important areas relevant to the industry are statistical tests and estimators. But the king of them all is statistical distribution. Here are some related resources to brush up if you are lagging behind.

Most of the data you will analyze will be new data. Or you will be analyzing old data for new insights.

Many times you must know from the get-go what statistical approach to problem-solving is valid and invalid. So make sure your statistical intuition is top-notch.

Multivariable Calculus and Algebra

You can’t escape mathematics in this business. Get used to it already.

Companies are beginning to favor in-house solutions instead of ready-made methods. While you won’t be expected to build algorithms from scratch, you should be able to refactor old algorithms to suit peculiar problems.

Computer Science Skills

computer science skills

Image Credit: Creative Commons

Computer science skills comprise programming, data wrangling, and machine learning skills. Let’s deep dive into each of them.

Programming Skills

The point of data science is to help machines do the heavy lifting for you.

And the only way to tell machines what to do is to speak their language. This is where programming skills become relevant.

In fact, data science binds to computer programming in a way that it is useless without it. You can learn this skill on GreyCampus, so don’t be dismayed.

Areas to master are data structures, algorithms, and conditional statements. Object-oriented programming, as well as the fast-rising functional programming, are necessary too.

Also, knowledge of database programming is essential. You will later be developing products that talk to data infrastructure after all.

Data Wrangling

Data wrangling comprises the processes and methods for getting valuable insights from data.

The problem with data is not its availability but rather its imperfection. You must be comfortable with dealing with these imperfections often.

To explain this in detail, look at this screenshot below. Imagine I am a lousy typist using Google Search. Now I want to search for a location for a vacation in Africa and input the following jargon into the search engine.

data wrangling

Notice that the result is still very much in line with my intentions.

Well, that’s data wrangling. The system corrected the imperfection in my input so it could output something sensible. Now, go brush up on your data wrangling skills.

Machine Learning

Chances are that you will have to train some machines in your career as a data scientist. If machine learning skills don’t come naturally to you, you may need to work hard at upgrading your abilities here.

Most companies who use lots of data for their analysis consider this skill a must-have. And it matters very little what aspect of data science that your specialty lies.

You must know enough machine learning tricks to stay relevant.

Data Communication Skills

data communication skills

Image Credit: Flickr

Data communication entails data visualization and data intuition skills. Let’s look at each of them.

Data Visualization

Nobody is going to appreciate the information you glean from data if you don’t present it well.

Data presentation is as important as its analysis. As a data scientist, you must have data visualization skills to communicate your data clearly to your team members.

In data visualization, you should hone your charts and graphs presentation skills. Expect to deal with intricate plots that involve 3-dimensional relations too.

Data Intuition

Strive to develop your intuition.

Most companies test for this skill in their interview sessions. They may ask you some logic-based questions to test how your thought process function.

You’ll save time if you’re able to tell how to model a dataset at a glance. And companies are always racing against time.

No, you don’t have to get it right the first time. Albeit, you want to get it right after a minimal number of trials, at most.

A sharp intuition reflects your rich experience working with data of some degree of complexity. This skill also tells your employer that you have a solid understanding of the fundamentals.
 

Section II: Master Data Science Tools

master data science tools

Image Credit: Pixabay

In the previous section, I listed the skills that are necessary for data science. This section will focus on the tools needed to build these skills.

Tip #2: Programming Skills

Hundreds of programming languages exist on the web today. But not all of them support data science well enough. There’s no need to bother with the rest, we’ll focus on the two most popular ones in the industry.

Programming Skill #1: Python

Python

Image Credit - python.org

All you need to know about python is that it is a multi-purpose open source language. This computer program is insanely powerful and enjoys robust support from its ever-active community.

Python was not built with data science at its core. Albeit, the program’s flexibility makes it a strong contender for the number one spot as a choice language for data science.

Don’t get dismayed, Python is easy to learn. I’d wager you can design simple products in one day of learning python.

Industry specialists recommend Python for data manipulation. It comes in handy when you need to do some machine learning. And its community is always expanding the toolset for this purpose.

Programming Skill #2: R
R programming

Image Credit- inwizards.com

R Language is native to data science. With so many functions baked in its core, it’s hard not to go with this language.

It is a statistical language for all intents and purposes and should be your choice if you want to manipulate huge datasets.

I must warn, it is not very easy to pick up on R. The learning curve is pretty steep. But once you get the hang of it, you’re set to conquer data-sets for a living.

It’s community, though not as large as python, is equally very active. New tools are created, tested and released for free almost on a daily basis. You can learn R online.

To sum this up, learning the Python and R programming languages is a better approach. They both have strengths and weaknesses that balance each other out.

Tip #3: Pick-Up on Data Collection and Cleaning Tools

To qualify for a position as a data scientist, you must learn tools for data collection and cleaning. There are lots of tools to do this, but some of them cost an arm and a leg.

You can get free tools from open source communities. Usage differs among these tools. So if you know how to use one, you can pick up on another fast.

For your data cleaning, you can get your hands dirty with Jupyter

data cleaning jupyter

It supports plenty of features to get your data cleaning and transformation done in no time. Other tools are Trifacta Wrangler and OpenRefine.

Tip #4: Master Machine Learning and Modeling Tools

Used mostly for machine learning and modeling of data, data science tools are some of the most sought after. To compete in the industry, you should have a solid grasp of some of these tools.

For machine learning purposes, you’d stand an advantage if you have knowledge of the TensorFlow framework. Examples of machine learning and modeling tools include Datarobot, GraphLab Create, Jupyter, and BigML.

For visualization, Excel, MatplotLib, and GGplot are essential.

machine learning modelling tools

Image Credit: Creative Commons

 

SECTION III: Interview Preparations

This section focuses on the job interview preparations, fully.

Tip #5: Insight into Question Types: Categories of Interview Questions

So, I imagine you have checked yourself against the requirements above. Now you are ready to confront the interviewers for that role.

Well, there’s still a bit you need to know.

Like most interview sessions, this interview is often divided into sessions.

1. Technical and Statistical Sessions

This session will seek to test your technical and statistical background. It is at this point that all your data science skill should be put to good use.

The areas in which they will test you for this session include:

a. Programming Skills: Try your hands on a sample question below.

Write a program that prints the numbers from one to 100. But for multiples of three, print “Fizz” instead of the number and for the multiples of five print “Buzz.” For numbers which are multiples of both three and five print “FizzBuzz.”

b. Database Programming Skills

Write a SQL query to create a table that shows, for each class, the value of the highest grade in the class.

data base programming skills

c. Statistical skills and tools you have mastered:

Try the following problem.

A company selling a competitor to Microsoft Office is testing their marketing by sending out two different sets of emails. One set contains business-related content, and one contains consumer related content. We are interested in how each campaign performed; did one do better at getting people to click-through? Below is a selection of graphs on the two email campaigns. The bottom two graphs have the same data as the top two, only bucketed by the amount the customer has spent with the company the year before the emails were sent. Which campaign did better?

statistical tools

Image Credit: Medium.com

2. Communication Skills and Work Ethics

No data company hopes to hire a slouch or a lazy employee. And this is regardless of your skill level.

After testing for your technical skills, expect to go through another session focused on work ethics and values.

As much as you have mastered your data science, prepare to learn about your company and what they do.

Your vision, values must match theirs. Or you should be able to put up a good-enough show to prove it.

Tip #6. Preliminary Prep for Your Data Science Interview

Let’s explore how to get ready for your data science interview. Read on.

1. Read the Job Description

communication skills and work ethics

Image Credit: Creative Commons

Read and digest your job description so you know how well to appraise the role you want. A data analyst’s position differs from a data engineer’s. Most of these tiny details are in the job description. Don’t skip this ever.

2. Review your Resume

review your resume

Image Credit: Pixabay

There’s no emphasizing this enough. Make sure that you know all its content by heart. Nothing screams incompetence like a candidate who can’t remember his previous job experience. If they allow it, print a copy and take it with you into the interview.

3. Remember the Intricate Details of Past Projects

Intricate details of projects

Image Credit: Wikipedia

Employers are getting more and more interested in experienced applicants. Make sure you are that applicant. You should include your past projects in your resume no matter how small. But be ready to expatiate on them during your interview. No better way to sell yourself, you’d agree.

Tip #7: Check Out Sample Questions Online

You won’t be the first data scientist to get hired. Hence, there is a huge chance someone else has documented their experience on the web. Research and select relevant questions for practice sessions. A resource like this one is indispensable.

To stay in top shape also,

A. Conduct Mock Interviews

After curating these questions, be sure to practice with them. If you have a willing partner, then even better. If not, then no problem, make do with a mirror. Practicing will help you smooth out those tiny errors and help you relax on the D-day.

B. Ask HR for the Interview Format

Call the HR department of your intended company and find out if they can give you the interview format. It’s no big deal. If they do, you’ll be all the better for it. If they don’t, you’ve lost nothing.

Tip #8: Practice Your Data Science Skills

Make sure to have covered all the important data science skills before the interview day. It is a practical role so you must get around to showing off your expertise.

Keep it simple. Don’t bother mastering complex skills that’ll take hours to resolve, especially if you don’t know them already.

Your employer only wants to be sure you have the basics on lockdown. Most of the questions will be solvable within an hour or two at most.

Tip #9: Make a List of Important Questions to Ask Your Interviewer

Job descriptions are mostly not exhaustive. Use the opportunity of a face to face interaction to ask questions about the company. The roles and how life at the company feels like.

Also, no matter how the interview goes, have a thank you email ready. As soon as you end the interview, send them out of courtesy and keep the channel of communication open.

Tip #10: Read Other People’s Experiences Before the Big Day

Nothing prepares you better than reading other people’s detailed experiences. Read a couple of these experiences to steal your confidence and help you relax. Some of these resources are:

Also, read employer’s hiring tips as they provide invaluable insight into their expectations. Some useful resources include,

Wrapping It Up

Landing a data science job is not as difficult as people portray it to be. If you are willing to study the field and develop useful skills, you are well on your way up.

The interviews often determine the outcome of your application. So while skills are necessary, don’t neglect to prepare to ace the interview like a pro. Cheers to your success!

About The Author

Nicholas Godwin has worked on data science research and related projects for Fortune 500 companies, global tech corporations and top consulting firms, from Bloomberg Beta, Accenture, PwC, and Deloitte to HP, Shell, and AT&T. Drawing from his rich experience in the tech space, Nicholas helps businesses tell profitable stories that their audiences love. You can catch Nicholas on TechContentLabs or say hello on Twitter (@Donglitzie).

Nicholas Godwin

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