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Today’s Enterprises are leaning more towards data collection and interpretation. The economy shows a huge demand for people who are efficient in mining and interpreting data. 

They are, in short, termed as, Data Scientists. It is predicted that this career will have one of the highest demands in the years to come.

If you are the one planning to make a career in Data Science, this article will layout certain essential things you must observe before opting and moving forward in your career.

1. Role of a Data Scientist

The major role of a Data Scientist is to convert and interpret huge volumes of data and carry out analysis on the same. It further evaluates the trends and obtains deeper insights. The insights regulated by the Data Scientists are leveraged into actions by the enterprises.

2. The Market Strategy  

Data Science market, in general, is divided into two distinct segments: Production & Development.

Production – This is the most mature segment of Data Science. The primary activities like predictive Analytics are used in integrating data-driven enterprises.

Huge businesses have authority within this group, mainly, financial services, manufacturing, retail, e-commerce, etc. These firms are usually distributed across the world.

Development – This is the new world of Data Science. Data Science and code are the core products of it. Many MNCs, as well as startups, are designing new analytic platforms with emphasis on embedded analytics.

The other latest innovations in Data Science include Deep Learning, Speech Recognition, IoT, and AI.

3. Four Career Responsibilities of Data Science

Data Scientists are differentiated into four types.             

  • Data Business People – They are primarily focused on the enterprise data projects. It includes junior duties of blending, cleansing data, and designing predictive models.
  • Data Creatives – It tackles the complete analytics process, from, extracting data, combining data, performing analysis, building models, and creating visualizations. It innovates recent predictive analytics use cases, products, and services. Data Creatives exist heavily in the development process.
  • Data Developer – It manages, gets, stores, and learns data. Data Structure infrastructure side is particularly good for current analysts and IT staff.
  • Data Researcher – These are the members who invent Data Science starting from its fundamental level.

4. Data Science Edges  

A minimum of self-analysis is essential before getting into Data Science. Data Science has many edges, but the worth noting are: ‘Big Data Analytics’ teams explore predictive analytics patterns in data.

5. Skills Required

There are certain hard skills you need to excel in attempting the Data Science Market. Data Scientists can access themselves in these critical skills.

Data Science SkillSet

Statistical Theory – Probability and statistical theory, analysis include sampling, hypothesis, and statistical distribution.

Programming Skills – Knowledge of scripting is a must, for a good data scientist. But choosing the right one is a matter of discussion.

SQL – This data science language reflects the extraction of data from relational databases. At the same time, SQL is available as a Hadoop query language.

Database – Database interprets how to extract the data and transform it into the required format.

Python – R versus Python is the biggest discussion of time. Python is the production language with a liberal data science library. It works more efficiently than R does.

SAS – SAS comes first in its adoption. It is the ultimate data science scripting language. SAS is ubiquitous in the production process. This skill inevitably brings a unique advantage.

          Supervised – Decision trees, Logistic Regression, SVM, and Neural Networks.

          Unsupervised – Clustering, Component Analysis, and factor analysis.

  • Big Data – Apart from fundamental algorithms, Data Scientists must also learn their adoption for large datasets. Therefore, essential tools like Hadoop, Spark, and analytics platforms constitute the dedicated module.
  • Business Analytics – The combination of advanced math and heavy technology.
  • The main skill set that any data scientist must possess is the art of questioning and analyzing the information.
  • Models that have been already built are used in assisting the IT enterprise decisions.
  • Critical Thinking, Persuasive Communications, are a few of the other skills one must continue to excel in the role.

6. Specialization              

Specialization adds value. The production world has more opportunities open only when you specialize. Two areas of specialization that find a way in the production world are:

  • Supply Chain Forecasting – Demand-driven supply chain forecasting enables a unique entry into the world of logistics.
  • IoT enabling Manufacturing – This uses predictive modeling of streaming data into SCADA. It predicts the quality output within a production run. Within the production world, however, predictive modeling obeys a complete toolset. Very soon specialty fields will emerge.
  • Deep Learning, Image Processing, AI are the additional specializations.

7. Some Major Lessons to Remember

  • Efficiency in Domain: Any aspiring Data Scientist must ensure to be well-versed in the domain. Analytics engagement will be a flop-work without consistent domain expertise. A good predictive model can be built with the right set of variables.
  • Right Expectations: Data Scientists must generate insights by reaching out to the expectations of the customer. This clarifies the doubts of the users, thereby enhancing the business.
  • Analyzing Data: Data Analysis must be carried out from various perspectives. With the structured data analysis methods, the pattern of the data follows accordingly.
  • Demonstrating the Solution: Any Data Scientist is going to be remembered for the way he showcases his talent in presenting the solution. The context of the presentation is also important to remember.

8. As the Career Progresses

Data Science is a field of tools, and other businesses, that play a vital role. A sophisticated toolbox extract, transforms, engineers, and models value from raw data.

Simplification and Automation of toolbox require innovation. A consultant must have domain knowledge, process knowledge, and methodology. These three principles are measuring data science skills.

All these skill-sets make you industry ready and well prepared in advance.

Final Words…

Data Science is an industry that is emerging with plenty of lucrative opportunities. Data Scientist has become the hottest job of the decade.

Many big companies are constantly coming out with job postings for the Data Scientists. So, with the right qualifications, you are going to enjoy a bright career in Data Science. 

Written By
Sirisha Paladhi possess love and passion towards writing, which brought her into this field. Presently, she is working as a Content Writer at TekSlate . During her career, she has written many articles on technology innovations. In her pass time, she relishes in making handy-crafts. You can also connect with her on Linkedin

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