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How to Become a Data Scientist? Get Some Amazing Facts You Might Not Know Yet Before

Data scientists are a new progeny of data analytics experts who have the technical skills to sort intricate issues. Today organizations are struggling to figure out how to understand the inordinate quantity of disparate data. 

By interpreting and sharing their insights, a data scientist assists organizations to resolve vexing problems. By consolidating computer science, analytics, statistics, modelling, mathematics, and a sound sense of business – data scientists reveal the answers to various questions that assist organizations in making objective decisions. 

What is Data Science & who is a Data Scientist?

Data Science keeps on advancing as one of the most promising and highly demanding career paths for skilled professionals. To uncover useful intelligence for their organizations, data scientists should dominate the full range of the data science life cycle and have a level of flexibility along with comprehension to boost returns at each phase of the process. 

The term “Data Scientist” was first coined in 2008 when companies and organizations recognized the demand for data professionals who have expertise in collecting, organizing, and analyzing tremendous amounts of data. 

What is the Worth of Data Science?

Data science is a profoundly interdisciplinary work on entailing a huge scope of info and one that typically considers the big picture more than other analytical fields. In business, the objective of data science is to give knowledge and intelligence related to consumers and campaigns. Moreover, creating strong plans in order to engage their audience and sell their products is also a top-notch priority of a data scientist. That’s why he is assumed as a top-ranking professional of any organization. 

How to become a Data Scientist?

Working being a data scientist can be intellectually challenging, analytically satisfying, and put you at the front line of new advances in technology. Data scientists have now become more popular and highly demanded as big data consistently enhances the importance of the way organizations make decisions. Here, you will get some enticing facts regarding what data scientists do and how they become professionals.

Basic Traits and Skillsets – Must have been Owned by a Data Scientist. 

Generally, to become a data scientist, soft skills are required to have. The soft skills entail intellectual curiosity, with a unique combo of scepticism and intuition, along with creativity. 

  1. Interpersonal skills are a crucial part of this role because it involves working across many teams on a regular basis. 
  2. Various employers or business owners anticipate that their data scientists will be strong storytellers who have enough about how to present data insights to people at all levels of an organization.
  3. Moreover, they required leadership skills to pilot data-driven decision-making processes in an organization. Besides all of them, leadership, business savvy, and the ability to foresee risks are also significant traits for handling the huge amount of data required for predictive analytics.

Apart from all of the above, there are also various skills needed to be learned related to the said role. The most encouraging and highly specialized skills are as below.

  • Research 
  • Cloud Tools
  • Programming
  • Big Data Platforms
  • Effective Communication
  • Software Engineering Skills
  • Data warehousing structures
  • Statistical Analysis and Math
  • Machine Learning techniques
  • Data Visualization and Reporting
  • Data Mining, Cleaning, and Munging

Hard Skills – Required for Data Scientist Role

Although, you have figured out various soft skills confined with a data scientist. But here, some hard skills are also essential to earn a remarkable name in the said role. Let’s put a glance over those specific skills.

  • Experience in all aspects of data science, from initial discovery via cleaning, model selection, validation, and deployment or arrangement.
  • Sound knowledge and understanding of common data warehouse structures.
  • Expertise in using statistical strategies in order to sort out analytical problems.
  • Proficiency in common machine learning frameworks.
  • Expertise with public cloud platforms and services.
  • Knowledge of a wide variety of data sources, including databases, public or private APIs, and standard data formats, i.e., JSON, YAML and, XML.
  • Capability to recognize new opportunities to apply machine learning to business processes in order to improve their proficiency and effectiveness.
  • Ability to fabricate and execute reporting dashboards that can track key business metrics and provide actionable insights.
  • Expertise of both approaches – qualitative and quantitative analysis.
  • Ability to share qualitative and quantitative analysis in an understanding mode for the audience.
  • Familiarity with AI procedures, like K-nearest neighbors, Naive Bayes, random forests, and support vector machines.

These hard skills required for the job include data mining, deep learning, machine learning, and the ability to consolidate structured and unstructured information. Expertise with statistical research techniques, such as clustering, modelling, data visualization and segmentation, and predictive analysis, all are assumed a big segment of the roles as well.

Major Roles and Responsibilities of Data Scientists

Data scientists work intimately with business stakeholders in order to comprehend their objectives and figure out how data can be utilized to accomplish those objectives. 

The design data modelling processes create algorithms and predictive models to extricate the data the business needs and assist with analyzing the data and sharing insights with peers. Because each project is different, the process for gathering and analyzing data generally move on to the underneath way:

  1. Pose the right inquiries to start the finding process
  2. Get information and whole data
  3. Process and clean the info
  4. Consolidate and store information
  5. Initial data investigation and exploratory data analysis
  6. Plump for one or more potential models and algorithms
  7. Apply data science techniques, like; statistical modelling, machine learning, and artificial intelligence (AI)
  8. Measure and enhance results
  9. Present eventual outcome before stakeholders
  10. Make adjustments according to feedback
  11. Repeat the process to solve or tackle a new issue

Some data science professionals might engage in any said job or become full-time data scientists, such as computer scientists, database and software programmers, disciplinary experts, curators, and expert annotators and librarians. In general, job postings particular for data scientists may also advertise the opening as “machine learning architect” or “data strategy architect.”

Bottom Line

In the fast-paced digital world, being a data scientist is no less than a blessing in disguise. If you follow the whole parameters and get all required skillsets discussed in the article, you will be able to solve various critical problems along with earning a million dollars. So, welcome to a bright day as a data science professional and serve the IT industry. 

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Christopher Stern

Christopher Stern is a Washington-based reporter. Chris spent many years covering tech policy as a business reporter for renowned publications. He has extensive experience covering Congress, the Federal Communications Commission, and the Federal Trade Commissions. He is a graduate of Middlebury College. Email:[email protected]

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