Essential Skills That You Need To Become A Data Scientist

Being a “Data Scientist” is awesome but landing at it is not an easy task. To have your foot in the door, a comprehensive mastering of many essential skills is necessary. For the benefit of aspiring Data Scientists, we are now going to discuss essential skills that are needed to be harnessed to step into this lucrative career.

Non-Technical Skills to become a Data Scientist:

1. Education: Definitely education plays a vital role in a Data Scientist’s career but it is not the only deciding factor. If your educational background is ingrained with strong mathematics and statistical knowledge, then the path is pretty easy for you. It doesn’t mean that if you are not a math or statistics graduate then you will not be able to get into a Data science career. Data Science is an interesting career that demands your interest as the major factor to pursue, so never let anyone dissuade you from pursuing your passion by pointing your education. Even if you are an engineering graduate or from a hardcore science background, you can still pursue training and education that will allow you to achieve it.
2. Communication Skills: Holding strong communication skills would definitely be a great asset for Data Scientists. Irrespective of the industry in which they are working, there will be loads of data generated each day and Data scientists will be deriving insights from the raw data. Though there are many technicalities involved, Data scientists need to present these complicated hypotheses in a crisp and meaningful way so that the non-technical persons and business owners can understand them easily. It is crucial to convey the right message and a high level of communication skills will be of immense value in these situations.
3. Intellectual curiosity: No wonder you see this phrase in all job opportunities of Data scientists. Intellectual curiosity is the root cause of many innovations and in fact, great scientists are driven by this ability. It is the “why?” question that keeps you awake the whole night to find out a creative path to get things done. Intellectual curiosity can drive you to deliver results that are of great value to a commercial enterprise. In fact, this is one of the main features that an interviewer will be looking for when you attend an interview for the Data scientist position.
4. Business Knowledge: Even for a purely technical role, it is important to understand the business domain and its goals to deliver considerable results as well as to make a difference in the long run. As a Data scientist, understanding the business and its various norms, trends, terminologies will add value to your career. Though Business knowledge might not be critical criteria for a beginner but is essential to boost your career after you dive into the Data Science Ocean.

Analytics and Technical skills to become a Data Scientist:

1. Basic Statistics knowledge: Brush up your high school statistics book to lay a strong foundation on basic statistical concepts such as Hypothesis Testing, Descriptive, Probability and Inferential Statistics. As a Data scientist, you need to understand business statistics to work on it. So, your basic statistical knowledge will be a strong foundation for your business analytics path.
2. Coding Skills: R or Python? Which one would lay a better career path for a Data Scientist? Is the debating topic that is going on. It is definitely good to have a basic to intermediate proficiency in any one programming language that is being frequently used in this career. Later, attaining a higher level of proficiency can certainly be of great value to your career.  Either you choose R or Python, it is important to understand its core values and should be able to apply them to your work without any hiccups.
3. Ability to work on Unstructured and Structured Data: Data scientists need to know how to work on unstructured as well as structured data to derive useful insights for the company. Be it of any form, they should have the ability to juggle the data by using their math, statistics and programming skills to uncover hidden business solutions. They should possess the knowledge of working with all the latest tools, technologies, and platforms to effectively handle the data.
4. Big Data processing Platforms: Big Data needs to be processed effectively to derive more potential information for a company. There are many Big data management platforms available such as Hadoop, Spark, Flink, etc attaining the working knowledge of any one of these data processing frameworks is vital for your role. The Hadoop platform is the heavily preferred one and also having experience with Hive or Pig is beneficial.
5. Machine Learning/Data mining skills: Holding a certification in Machine Learning and Data Mining will lay a strong foundation for your Data Science career. It is not the theoretical knowledge that is being spoken here, we are referring to practical skills too. Understand the concepts and learning implementations based on particular environments is what preferred. Choose a certification provider who gives both theoretical and practical exposure to these concepts.

After all these discussions, you will still be left with the biggest question in your mind

“Is math skill enough for me to think of a Data Science career?”

If you are a beginner and looking for a lucrative career in Data science with only Math skills in hand then it’s time for you to change your mind. People often think that math skill is the only prerequisite for Data Science career because they need to work on numbers all the time. It is true that strong maths background is really needed for a Data science career but only possessing this skill doesn’t work out. A tremendous amount of curiosity and the ability to synthesize raw data are the primary abilities that help you to make a successful career in Data Science. There might be situations when analyzing the data alone will not happen instead you need to work on business problems and narrow them down to arrive at the right solutions. Furthermore, you should have the ability to explain the statistical evidence and analytical results in a satisfying way to non-technical, non-academic clients. Learning Data science is not just working on a bunch of numbers instead it outlines non-technical, analytical and technical skills. To Sum up, math skill is one of the prerequisites and not the only prerequisite for a Data Science career.

Each company is unique to their demand from a Data Scientist, some might expect a Data scientist to do more of a Data analysts job whereas some combine their duties with data engineer others might expect a high-level expert working on data visualizations and machine learning. Data scientists might experience varied responsibilities depending on the company that they choose.

Why is the Demand for Data Scientists

But “why is this mad rush for Data Science?” would be the next question that arises in your mind. This is just a simple law of economics- “more demand less supply”. With the availability of a huge amounts of data, there is a high demand for Data Scientists to juggle the data. But the unfortunate truth is that there are only limited amounts of potential resources available and couldn’t fill the large gap. While the supply is low for this huge demand, obviously it has become a hot career that pays you a lucrative sum. Similar to a computer science career which was once the highest paying one. As times passed by, the supply was increased and the salaries were reduced.

The data-driven decisions are gaining the limelight status among business owners because of the availability of many advanced tools such as Google Analytics, Tableau, Python, R, etc to analyze the raw data easily, which was once a nightmare. When these tools are not hard to use and organizations can afford employees to take advantage of them, it has become a driving factor for lucrative packages. Let’s look at a few top paying companies that achieve immense benefits by using Data Science.

1. Google is a data-driven company and every step that they take is based on data insights. Even their HR team evaluate the strategies through scientific methods to make employees more productive
2. Amazon delivers whatever product you want at your doorstep. When you browse through the product recommendations, remember that there is a complicated data science algorithm sitting behind.
3. Facebook is the popular social media that is generating huge ad revenue. Their creative ways to target people is not as easy as you think.

Not only the big organizations have started taking advantage of Data scientists, but even many startups have started thinking about data-driven decision making to expand their business. Now the situation has arrived that, not adapting and employing data science tools and techniques might force the business owners to step out of the business world. The present situation is positive for Data science and it is expected that demand will continue to grow to provide many employment opportunities. Many interesting predictions are being measured on “how the Data science career will be in the year 2020?” and in fact, this has been discussed earlier in our blog also is one of our most popular posts.

With the rise of Data Science, it is the correct time to enter this field. You may be a fresh graduate with no working experience or an experienced professional transitioning from another field, you can still gain the necessary skills through a global service provider. Datamites™ Institute is accredited by the International Association of Business Analytics Certification (IABAC™) providing training on various skills associated with Data Science through convenient modes.

Enrol now to exploit the great opportunities available in the Data Science field.