The 4 Pillars of Data Science Expertise

Data science has proved its existence will last for a long time and possibly make work easier by automating repetitive tasks and analyzing large data sets within seconds in time.

Individuals who find themselves in a data science career path need soft skills to be successful in their career.

Source: Towards Data Science

Success in technical track career such as data science comes with great knowledge of these four fundamental areas:

  1. Written and verbal communication
  2. Statistics and probability
  3. Business domain
  4. Computer science and software programming

Written and Verbal Communication

An Analyst, Data Scientist and Data Engineer should be able to tell stories about data to their client. The ability to explain data transition stages to your client shows the story telling field of analytics.

Written and verbal communication is very important as an analyst or scientist you must ask questions about data received to help you analyze and prepare the data for whoever that need such information from your data.

Statistics and Probability

Statistics has played a major role in mathematics of analyzing data. We work with data that contain numbers, words, signs and need to be organized for readability. Such data could be summed up to get proper visualization to explain such data.

On the other hand, Probability is used to predict uncertainty in data.  Probability is a foundation for use of statistics in analyzing data.

Business Domain

Domain knowledge of a business is very vital for a Data Scientist, Data Analyst, Financial analyst, Data Engineer and so on. Understanding how data move from a department to another within a business environment helps the analyst in analyzing possible units of contact while creating reports or building visualization dash boards for your company.

Business knowledge of a domain is a skill mastered overtime as you interact and analyze data sources within your organization.

Source: Estes Park

Computer Science and Software Programming

A data scientist needs a technical background in computer science, statistics and programming. Having these foundations prepare you for the big game ahead. Your ability to think, test, analyze and build systems evolves the analytical mindset.

To be able to program data, you need to understand the data and possibly have a good knowledge of data structures and algorithms. Machine learning is a vital skill for a quintessential data scientist, likewise knowledge of software programming languages like Python and R makes you a good Data Engineer, Data Scientist, Machine learning developer and so on.

In summary, Analysts in general interact with managers, top executives and utilize their communication skill to explain data reports and get feedback from user. Data scientists also generate business questions driven by business domain knowledge, statistics and probability differences in data. Computer and software programming knowledge has helped in automation and improving data analytics software’s.

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