Data Science and Analytics Making it a Career
Understanding Data Science and Analytics as well as the Various Available Fields of Education?
Theoretically, data science covers almost all subjects of data analytics, as it is the broader classification that includes every field that has anything to do with the collection, retention, analysis, interpretation, research and presentation of data in general. The broadness of the subject, however, makes it necessary for the subject to be subdivided into multiple separate – but often overlapping – fields for specialization.
As academic degree courses are often created on the basis of such separations, it is imperative for students to at least be aware of the options they have before choosing a career path. To that end, we are now going to discuss some of the most useful, prospective and prolific fields of data science below.
Post the collection of data in any field of work, engineering it is essential to convert all that raw data into useable and readable formats. Once the data engineering is complete, the data sets can then be utilized for analysis. The work may include the following steps or more, depending on the project in question:
- Source management
- Data structuring
- Data quality inspection
- Storage of the qualifying sets of data
- Adding readability/usability for the analysts
Depending on one’s qualifications, training and experience, data analysts and database developers are often capable of engineering raw data as well, alongside dedicated data engineers.
Data mining is often considered to be one of, if not the most, complex areas of expertise in data science. To explain how it works, let’s divide it into the following easy to understand, simple set of steps and goals:
- Goals: Engineered data sets are used to find, categorize and pinpoint useful patterns, insights, trends, answers, conclusions and future possibilities
- Methods: The two primary methods used by analysts to achieve the desired goals are predictive models and statistical data
- The predictive models will often need to be developed for that particular project
While there is a difference between data science and business analytics, it is one of those fields of work that is shared by both data scientists and business analysts alike. Statisticians with advanced knowledge about digital tools are also capable of taking up the position, due to their strong knowledge and educational base of statistics in general.
It should be noted that in spite of the difference between data science and business analytics in terms of their educational programs, if they are working as data miners, they are most likely looking at similar pay scales.
Database Architecture and Management
More complex in some ways than data mining, database architects and managers are among the highest paid professionals in this field of work. Even the average salary is above the $110,000 per annum mark, with a majority of database architects and mangers getting paid much more than that. If you wish to pursue this highly rewarding but increasingly complex field of data science, you will either have to complete a dedicated program in database architecture, management, and administration, or pursue a specialist’s degree in data science.
The job itself consists of, but isn’t limited to:
- Database development for storing and providing retrieval access to the client’s company information
- Assessment and analysis of necessary structures, as would be required for developing software applications, on an as-needed basis
- Installation and configuration of the necessary information and interaction systems, so that a functional user interface can be created for the client to use
- Data migration
- Logical data model development and designing of conceptual data models
- Troubleshooting and boosting performance of the database software and overlying applications
Business intelligence is a field that deals with the following duties and roles, but its scope can be increased if the hired individual is adequately qualified to do so:
- Management and improvement of back-end data
- Managing and improving the accuracy of backend data sources
- Verifying and validation of data integrity through proven or custom-designed testing models
- Simplifying data collected from the back-end sources
- Development of custom analytics and solution models
- Dashboard management
- Identification of opportunities and creation of reports which key executives can base their future business strategies on
- Suggestion of the best possible course of action in their reports, in respect to identified opportunities and problems
Data strategists and business intelligence analysts/developers are best suited for this job, due to its close connection with the business management aspects.
Market Research and Data Analytics
A more conventional, and also one of the most useful roles in business analytics, is played by those that work in market research and data analytics. While it isn’t the highest paying job on this list, business analysts who work in market research have one of the highest rates of employment in data science. Moreover, higher qualifications and experience in the field increase their value to the employers, as well as their salaries quite significantly down the line.
Market analysis, web analysis, sales analysis, product analysis, etc. are all smaller roles which can either be handled by a single business analyst in small companies, or they may assume a management position to lead and guide each individual department/analyst in the aforementioned fields of analytics. It’s a broad field with multiple career options and future enhancement possibilities.
The options mentioned here are not the only ones, and many of the separate subjects are sometimes taught in a single course simultaneously. Your choice of education should not be based only on which field has the highest pay, but rather your own understanding, experience, prior qualifications, and natural knack for a particular branch of data science. Being intricately connected with business management, rest assured that as long as you are good at your job, you can potentially shine through any field of data science. However, you will likely need to pursue higher education paths later on to specialize in particular aspects of your work, depending on what they are at that point in time in the future.
Is data science a field that interests you? Let us know what path you are thinking of taking.