What Skills Do I Need to Become a Data Scientist?

Despite the importance of data science being the most sought after profession in the tech market these days, there is still a skill gap that needs to be addressed. Hiring the right candidate remains a challenge for most organizations. Although, companies have improved their hiring strategies by offering lucrative compensation to candidates having industry related data science skills, yet the industry still finds it difficult to reap the greatest benefit.
Pretty much many analytic heads and analytic organizations are grappling with the dearth of talent in the analytics market. And because data science and data analytics are taking over, it is recommended to up-skill to survive in this rapidly growing tech world. Data science is growing at a very rapid pace, and if one cannot extract the true value of a business based on the data collected then this can lead to a huge loss for the company. Regardless of your previous skills, degree or work experience, one needs to know what skills to develop and learn to bag that data science job.
Data science -- the sexiest job of the future
There is no doubt data science is the sexiest and hottest job of the future. And shifting a career towards data science will be remarkable in terms of growth and job security. Data science professionals are hard to find given the demand for such professionals in the industry. The job role has become critically important for businesses and companies looking to gain the most valuable insight to predict the future of the company.
Data Scientist jobs are in-demands. Data Scientist jobs are the fastest growing jobs in the US for 2019. A data scientist salary starts at $130k a year. The demand of data scientist is expected to grow at 56% this year. Lear more about Top 10 Jobs in the US for year 2019.
A data scientist’s skillset comes from various expertise, irrespective of the background one comes from. However, the data science foundation is built on four pillars; i.e., Statistics, Computer Science/Software Programming (technical skills), Business Acumen, and Communication Skills. Each of these skills plays a major role in a data science professional. Here’s a detailed explanation for all these skills.
Mathematics and calculations are a basic need of being a data science, Being a data scientist, it is essential to have an extensive understanding of statistics and statistical analysis. If one fails to understand the core concept in statistics it can become difficult to learn how statistical modelling works. To begin with, one can start learning basic statistics, descriptive and inferential statistics.
Programming languages
R and Python are the most used programming languages by data scientists while R programming is used mainly for statistical analysis and Python programming for general purposes. One does not really need to be exceptionally great with coding skills, however, a basic understanding of programming languages will be an added advantage.
Machine Learning is often connected with data science. However, there are data science libraries that are required for data analytics, predictions, charts, and report generations.
Machine Learning
Being proficient with machine learning algorithms is important for modern data scientist today. Data scientists should learn machine learning techniques such as adversarial learning, reinforcement learning, and neural networks etc. Apart from these, getting skilled in techniques like supervised learning, unsupervised learning, decision trees, and logistic regression are cutting edge techniques that are sought after by recruiters and hiring managers. Having distinct skills will help one solve different kinds of data science problems. Data science is all about getting involved with large datasets, hence you might want to be familiar with these machine learning techniques.
Data Visualization
Organizations and businesses are producing a large amount of data every day. To convert this complex data, data scientists must be able to translate the data into a format that is simple to understand. Pictorial formats and the graphical representation makes it much easier for people to understand. To ease this, data visualization tools such as Tableau, RapidMiner, ggplot2 etc. are used. These data visualization tools help convert complex data into formats that are easy to comprehend.
Having working knowledge in databases as a data scientist is a must-have skill. It is a powerful language that can be used for extracting data from databases -- some examples are databases such as SQL, NoSQL, and MongoDB etc.
Business Acumen
Data science adds value to the business market, from predicting the future of the company to making important decisions needed for the company’s growth.
Communication Skills
To be able to communicate the findings, communication is very important. How else can one explain it to the stakeholders and the concerned authorities? One should be very good at storytelling to explain the findings without any hesitation.
These are the important skills and technologies that one should start learning to become a data scientist.
Data Science Certifications
Recruiters these days are open to hiring professionals having data science skills. However, demonstrating the skills is one aspect that might be a challenge for aspiring data scientists. This is where certifications come in handy. If you’re looking to stay relevant and in demand in today’s tech world you need to consider taking up data science certification.
Certifications are the best source to showcase your skill-set to potential recruiters and hiring managers. Having a credible certification will set benchmarks to prove the employers you possess the right skills.
DASCA (Data Science Council of America) offers two certifications for Data Scientists today. Ideal for senior professionals already working as Big Data Analysts, these credentials are also great career-shaper qualifications for experienced and accomplished market researchers and marketing experts.
Data Scientist Certifications 
The two DASCA certifications are: the Senior Data Scientist (SDS™) Credential, which sharply targets senior Big Data Analysts and Engineers who want to move to Data Scientist roles. The Principal Data Scientist (PDS™) Credential, which is designed for experienced Data Science professionals who desire more challenging roles in their careers.
To learn more about these certifications, visit

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