How to Get Your First Job in Data Science

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How to Get Your First Job in Data Science

By now, most probably you know that the jobs related to data science as “the sexiest jobs of the 21st century”. Needless to say that data science is one of the best options for the enthusiastic, young community to build up their career. Now, a lot of questions arise in the minds of the concerned people such as, how to start, what to follow, what are the for requirements, what and how to learn, how to practice and to gather experiences and so on and so forth. In this article, you will come across the primary queries and their solutions as well.

What are the most other significant aspects data scientist skills and tools? And how can you get them?

Data science is not about a single skill rather, it is a field of multi-skills. The most basic skills demanded by data science are:

1. Software Tools:

Each and every experienced professional has their own bag of tools, obviously software in this case. Basically, a data science-oriented personnel requires tools for cleaning, mining and assimilating data. Regardless of the type or purpose of the organization, they expect a data scientist to know the working manual of some primary tools and these include languages necessary for statistical programming, database querying etc. mostly preferred coding languages are

  •    Python
  •    R
  •    SQL
  •    Bash/command line
  •    Java (in some cases)

2. Statistics:

A virtue of the data scientists is to understand the course of data just by having a look at them. That requires more than knowing the ABC of statistics such as mean, median, mode which are familiar to the general people too. Real problems of the market are not that easily solvable. A data scientist should have deep knowledge of statistical tests, maximum likelihood estimator, distributions and other significant aspects of statistics. If you are willing to join a data-driven company, good control over statistics is a must for you.

3. Machine Learning:

Machine learning is prevailing since the idea of data since was coined for the first time. Job-seekers from engineering and technology background are well acquainted with a few aspects of artificial intelligence since there student life. To implement machine learning to the fullest, one should have rich knowledge in programming languages, especially Python. To some certain extent, machine learning scares a good number of beginners in data science. Externally, machine learning algorithm seems like a few lines of code but these few lines are very much significant. It is nearly a must to understand the humming words of machine learning like random forests, k-nearest neighbors, and ensemble methods and so on.

4. Linear Algebra and Multivariable Calculus:  

This section is required to brush the memory of what you have learned so far in order to prepare you for the upcoming load of statistical models and machine learning. Sometimes the data scientists need to create a machine learning output of their own and the machine learning will act according to data input. Basic algebra and calculus are important as the products are expressed through data-driven approaches and predictive models.

5. Data Mugging:

Many of the data scientists of recent past have claimed their job as janitorial work. The statement is not absurd at all if anyone rethinks it carefully. Precise and effective output comes from the most useful bunch of data. Various data sources literally throw away data from different directions. Data engineers process them by organizing, pulling and managing storage. Then comes the part of a data scientist; they accomplish the job of data cleaning and mugging.

6. Data Visualization:

it is itself a career. Frontline developers are familiar with the tools like GPlot, D3.js etc which are famous as very helpful tools in reporting data plotting and prediction. Visualization and interaction are the significant virtues of a skilled communicator working in the newer organization making data-driven decisions. Data visualization is a better replacement for old-fashioned dashboard software and reporting. Now it is possible and way easier to present data on the web using mobile phones. Just to know the names of the tools required for data presentation is not enough; acknowledgment of the principles of data dependent decision-making procedure is equally important.

7. Software Engineering:

Data analysts and software engineer remain only a few steps away from being data scientists. Software engineering is considered to be a prime skill of a data scientist. A history in software engineering will help you to cut an extra portion of the pie when you are going to attend an interview in young companies. You may also have the possibility of being in charge of handling data logging and data dependent product development.

8. Putting yourself in a Data Scientist’s Shoes (Mainly Multi-tasking):

The arena of thinking and working protocol of each and every character in the data science industry are different from one another with a few coincidences.  The companies which are dependent on data-driven decisions demand data scientists be problem-solving agents. During the selection procedure, the authority of the company might test your efficiency of solving problems by providing very practical issues they face daily with necessary data. A good candidate will at first compartmentalize data according to the priority and usefulness. Then he/she should move forward with the instinct of a data scientist and finally should deliver an effective verbal solution.

How to Learn

By now, you may have understood that data science does not deal with any particular section of science, engineering or technology rather; it is a stunning combination of the mentioned sectors. It is obvious that your educational background will not cover everything. Then, how to be prepared for the desired career? Where to go? No need to panic. For you, here are two elegant sources from where you can meet your queries and can make yourself up for your dream job.

1. Books:

This may seem a bit old fashioned but you should not forget that this is the most authentic way of learning. Books provide well described and easily gettable information about any corner of data science you need to explore. You may be benefitted from the following books:

  •    Lean Analytics — by Croll&Yoskovitz
  •     Business value in the ocean of data — by Fajszi, Cser&Fehér
  •    Naked Statistics — Charles Wheelan
  •    Doing Data Science — Schutt and O’Neil
  •    Data Science at the Command Line — Janssens
  •    Python for Data Analysis — McKinney
  •    I heart logs — Jay Kreps


2. Online Webinars and Video Courses:


In this age of information and technology, you need not get out of your room in order to learn something. There are a lot of online technical training centers eagerly waiting for you 24/7. You can also take part in MOOC (Massive Open Online Courses) where you will be provided all kind of supports. Charging a small amount, they will teach you starting from data coding to business intelligence. Hopefully, you will be benefitted if you take help of are of them.

How to Practice and to Earn Real Experience

There is no company that does not demand a candidate with real working experience of one or two years. But how is it possible to have experience of such period when you are applying for your first job? How can you possibly convince them to hire you without hesitation? Well, you can work on some pet projects to gather that experience. “Pet project” means the projects created on your own. You may collect data from a data-driven company, find a problem and a time-demanding solution using your creative skills. It would be more convincing if you collect and analyze data of the company you are going to apply for.


Where and How to Send Your First Job Application

In case of choosing the first company of your data science career, lucrative salary or luxurious startup environment should not be your problem-solving concern. Rather, you should try to fit in such a company that would provide you the healthy atmosphere to prepare yourself for the next jump. Therefore, keeping the fancy companies out of the list would be better for you because, in those companies, you may not get a good mentor as everybody should be very busy oiling their own business. So, you can apply in the mid-level or rising companies as a startup.

Now comes the question, how to get the interview? Obviously, there will be a good number of applicants applying for the same job you have applied for. For example, from millions of candidates, a few thousands can reach the interview board of Google and only about 5000 candidates every year get to manage their dream job.

In case of online application, what the recruiters of the big companies do is to run the eyes on the referred applicant’s resume and to reply them within a very short time calling for the interview. Other applications remain untouched in many cases.

Now you know what to do. Manage a friend currently working inside or an alumnus of the company. You will be lucky if you could reach a helpful guy. If you get selected, do not forget to pay him back with the good news!



Nothing is impossible. It only requires self-kick, passion, and hard work. Again, it is not that easier as it sounds. So, what else are you waiting for? Start your journey right now! Wish you all the best of luck.

Tao is a passionate software engineer who works in a leading big data analysis company in Silicon Valley. Previously Tao has worked in big IT companies such as IBM and Cisco. Tao has a MS degree in Computer Science from University of McGill and many years of experience as a teaching assistant for various computer science classes.

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