Data science is fast becoming one of the most in-demand jobs. LinkedIn pegs the profession’s growth rate at 650% since 2012. This exponential growth is fueled by big data and artificial intelligence. So, if statistics, data, machines, and code sound appealing to you, data science might be your thing. Let’s walk you through the meaning, roles, and requirements of data science.
A data scientist is responsible for gathering, interpreting, and publishing data. The data they find can be utilized for some reasons, however, in the business world, it regularly applies to accounts or efficiency. For instance, a data scientist may see marketing projections from sales figures against business choices made over a specific time or period to decide how fruitful and efficient those choices were. These experts give the gauging information a business has to realize whether changes will be successful before settling on a choice.
The great about data science is that people skilled in it can work in many places., These include IT, medical services, financial institutions, retail, and marketing.
From the above description, you can easily see that data science is broad. It cuts across data gathering, coding, machine learning, computer science, and so on. There are four main branches of data science expertise and they will be expounded upon below.
1 Data analysts
2 Data engineers
3 Machine learning engineers
4 Data science generalists
1. Data Analysts
Although data analysis and data science are always conflated; they are not necessarily the same. That is, data analysis is a subdivision of data science. While a data analyst is expected to analyze data – the main function of data science, unless otherwise stated, that is the only branch of data science you are expected to have expertise in.
Your position may comprise duties such as hauling data out of SQL databases, expertise in Excel or Tableau Master, and delivering data visualizations and sets of information as well as producing dashboards. You may also lead the pack on your organization's Google Analytics account and be in charge of Search Engine Optimization.
2. Data Engineers
Data engineering is another field in data science. Typically, data engineers work in companies with huge internet presence and traffic to help set up data systems. If data engineering is your chosen field you will be required to set up a lot of the data infrastructure that the company will need. In addition to this, you may also be required to do some level of data analysis.
When searching for data engineering job openings, you will see job postings under both "data scientist" and "data engineer" for this sort of position.
For the position of a data engineer, software engineering is favored above machine learning and statistics.
3. Machine Learning Engineers
Machine learning engineers are often grouped with Artificial Intelligence engineering. This is because it is a branch of AI and machine learning engineers are well versed in artificial intelligence and machines.
Various organizations work exclusively or mostly with data. If you have the technical knowledge of statistics, mathematics, physics, or computer science conventional arithmetic, this field is well suited for you.
Your role is data-driven and since the companies that recruit machine learning engineers to deal directly with people who buy products or services, you would be charged with processing raw data.
4. Data Science Generalists
As stated earlier, not every data scientist grows expertise in a particular branch. Some have a workable knowledge of every area of data science and are skilled enough to fit into a team of data scientists or a multipurpose IT team.
This especially rings true for companies that are not explicitly data-driven. To be a data science generalist you should be capable of analysis, touch production code, data visualization, machine learning, and so on.
At this point, it’s been established that data scientists work with data to guide business decisions for organizations. However, this statement is rather generic. The duties and responsibilities you would be expected to perform if you fill up a data science are as follows:
While transferable skills are highly sought after, for technical roles such as data science, having the right hard skills is vital.
In addition to the above stated technical and soft skills, if you desire a career in data science, a bachelor's degree in data science or computer-related field will improve your chances of getting hired as an entry-level data scientist. However, you can switch careers to data science if you learn the basics and acquire the required technical as well as soft skills for it.
Additionally, most mid and senior-level data science openings will require a master's degree and in some cases, a Ph.D.
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The average salary of an entry-level data scientist is $95,000 per annum.
In Nigeria, the average remuneration of a data scientist is N265,000 per month.
1. How much do Data Scientists earn in South Africa?
The average salary for a data scientist is R 591 622 per year in South Africa.
2. What are roles and responsibilities of a data scientist?
The roles and responsibilities of a data scientist vary and some of therm are as follows:
3. Are data scientists in demand in South Africa?
Data science is one of the most in-demand careers worldwide. South Africa is not left out in this; it is not only highly sought after but the data scientists are also one of the well paid professionals.
4. How do I become a data scientist in South Africa?
To become a data scientist in South Africa, a degree in Data Science or quantitative field (Physics, Maths, Statistics, Finance, Economics, Computer Science, etc.)
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