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Data Science is one of the leading career options in the 21st century. In the present data-savvy world, huge chunks of data are stored by organizations from all walks of the industry to process and churn out solutions in the form of information for answering a wide variety of questions.
Ranging from businesses and government institutions to non-profit organizations, everyone has big data that needs to be analyzed and processed for solving several intimidating queries. This is where data science comes in.
How to Become a Data Scientist?
Data scientists are professionals responsible for dealing with big data and help their employers know right answers to their questions, may it be for creating a marketing plan or targeting the right demographics for a product.
Though data scientists come from a varying educational background, most of them have some sort of technical schooling. Data science is a diverse field that demands programming knowledge along with an understanding of mathematics (statistics in particular).
As the total information available to mankind grows exponentially, so do the opportunities for data scientists. Before diving into the how-to of becoming a data scientist, let’s first take a brief look into data science and professionals pertaining to it, followed by essential skills required by the job profile.
Data Science and Data Scientists
Data science is a diverse field that involves a plethora of requisite skills. Typically, a data scientist is someone who gathers and processes data with the intent of reaching some concrete conclusions that can benefit their employer.
There are several different techniques employed by data scientists. In order to present the data in a visual context, there is something known as visualizing the data.
Visualizing the data is a way that allows a user to spot distinct patterns that otherwise won’t be so obvious had the information was to be presented in the form of mere numbers.
Data scientists create advanced algorithms that are meant to determine patterns in large chunks of data. It’s safe to say that data science is the exercise of looking for meaning in huge amounts of data.
Essential Skills for Becoming a Data Scientist
- Adequacy in Programming – Analyzing and processing information needs to be done by means of the code. Hence, programming ability is important in at least one programming languages. The more programming languages a data scientist is comfortable in, the better it is.
- Clear Vision – Data scientists are required to design algorithms that are effective and fast. Hence, creativity is very important for achieving so. Data science is not only about why it should be done, albeit how it should be done.
- Curious Approach to Work – Curiosity is perhaps one of the most important skill demanded by a career in data science. It is the inherent curiosity of data scientists that leads them to seek for fascinating patterns in the large sets of data.
- Mathematical Ability – As data science requires churning out raw data and numbers, mathematical ability is a must-have.
- Resoluteness – Working with a continuous influx of data can be frustrating at times. Hence, having a firm determination will help anyone make through the ordeals offered by the data scientist career and reap hearty benefits out of it.
- Sharp Focus, Attention to Detail, and Analytical Ability are some other important skills that can be beneficial for data scientists.
Without further ado, here is the step-by-step guide about how to become a data scientist:
Step 1 – Ensure It’s Meant for You
First things first! Before you set out on the journey to becoming a data scientist, it’s important to double-check that it is exactly what you want. Data science is a very extensive branch of general studies. Hence, you need to be sure before taking the heavy load on your shoulders.
The Internet is full of several preliminary data science courses that’ll ensure that whether what you’re seeking is good for you or not as well as what you will get by pursuing the career path if you finally decide to go for it.
While some of these courses are paid, most of them are free to get. You can also consult YouTube for the thing too. Once you’re sure about pursuing data science, it’s time to move to the next step.
Step 2 – Get a Relevant Bachelor’s/Higher Degree
Though not impossible, it is very difficult to attain all the skills required for a particular job without earning some relevant degree. It can be a master’s, bachelors or even a Ph.D. degree. Some degrees that are beneficial to data scientists are:
- Applied Mathematics
- Computer Science
- Data Management
- Information Technology
Boot camps are an excellent way of speeding up the things alongside your main degree. Another beneficial activity that you can go for side-by-side your main academic course is enrolling for MOOCs.
Massive Open Online Courses or MOOCs are online courses that allow unlimited participation and open access to learning material by means of the web. MOOCs are offered by Harvard, MIT, Microsoft, and several other deemed universities and organizations from around the world.
Step 3 – Pick an Area of Interest
There are several different paths that converge to a fruitful data scientist career. Typically, data scientists start from the undergraduate level in Computer Science, Mathematics, Statistics, etc.
They are apt for bagging jobs like that of a data visualization specialist, management analyst, and market research analyst. However, some get specialized concentrations via master degree programs, such as data engineering and machine learning.
Some of them still pursue a further doctorate degree in concentrations to the likes of business solutions and enterprise science analytics. Therefore, it’s important to pick an area of interest and get a relevant degree for it.
Step 4 – Get Certification
Certifications are an important part of the resume of any present-day professional, especially someone belonging to the IT sector.
In addition to making the pursuer a marketable candidate for specific data scientist job requirements, certifications can help in developing new as well as improving extant skills.
There are a galore of certifications available for those interested in data science. Moreover, there are several great places to have them from. Some of the leading data science certification options are:
- Big Data Certification by UC San Diego Extension School
- Certified Analytics Professional (CAP) by several institutions
- Cloudera Certified Professional: CCP Data Engineer by Cloudera
- Data Science Certificate by Harvard Extension School
- Data Science for Executives by Columbia University
- Microsoft Certified Solutions Expert by Microsoft
- Springboard Introduction to Data Science by Springboard
Step 5 – Gain a Role
Once you’re done with accumulating all the academic and educational requisites, it’s time to put your skills learned thus far to test and gain a role in the lucrative field of data science.
Now, data science is a very varying field. Thus, there is a multitude of specialized roles to opt for. Furthermore, it is possible to become a data analyst without any prior experience and then advance from there.
Online places like iCrunchData and Kaggle are excellent for searching the right kind of data science job. With constant development in the field of IT and data science, new and better options are springing up every now and then.
Pros and Cons of Becoming a Data Scientist
Obviously, there are a lot of benefits of becoming a data scientist. However, like any other career path, it has its own share of disadvantages.
- Unique and challenging
- Offers a wide variety of daily tasks ensuring the retention of the interest of the professionals involved
- Working opportunity for a diverse range of organizations from all spheres of the industry
- Opportunity to come up with powerful solutions for customer retention, general business queries, the launch of new products, marketing, and much more
- Extreme variety of subjects has the downside of not allowing the professional to go deeper into a specific topic
- Technologies used in the context of data science are constantly evolving. Therefore, tools that are effective today might be obsolete by tomorrow. A data science needs to be on their toes to deal with any kind of change
CAUTION: Data Science is Not Statistics!
It’s very easy to mistake data science for statistics. Although the two shares several aspects, each one of them is a distinct field.
Statistics typically relies on established theories and focuses more on hypothesis testing. Furthermore, it is an old discipline compared to data science that has changed very little in the past several decades.
Data Science, on the other hand, is relatively new. Unlike statistics, data science relies heavily on computers and technology. Moreover, it is a continuously evolving field where information is accessed via large databases and then the code is used to manipulate and process the same.
So, that was all about how to become a data scientist. The field of data science is growing continuously and there are no signs of it subsiding anytime sooner.
At least, until the world finds something better than data and information for doing each and everything that relies on them, which is, of course, a very impractical possibility. Hence, it is a great time to make a career in data science.
Wish you all the very best!