Data science involves the analysis of large volumes of data, often unstructured, to produce insights for organisations that inform their strategy and planning.
The field encompasses a broad range of careers and opportunities. Individuals with an academic background in maths or computer sciences can easily land jobs in data science.
If you are interested in jumpstarting your career in data science, keep reading, as this article discusses the most promising data science roles and what makes them so interesting, and should help you select a suitable data science career path.
This article covers:
- Is data science a good career?
- Prerequisites for a career in data science
- Tools you should know how to use
- Top data science job requirements at a glance
- How to expand your data science credentials
Is data science a good career?
Yes, data science is a good career field. It requires the most future-oriented skillset, given the increasing use of data analytics and machine learning, and pays well.
According to workplace transparency champion Glassdoor’s 2022 analysis, data science is among the top three jobs in the US, with data scientists reporting great job satisfaction, good pay, and plenty of jobs available. While it’s ranked fifth on the UK list of top jobs, British data scientists report greater job satisfaction than those in the top four occupations in the UK.
The median salary for data scientists in the US surpasses $120,000 a year.
Among the careers available to those with data science certification are those of data analyst, database administrator, data engineer, business analyst, and machine learning engineer.
Prerequisites for a career in data science
While you don’t need to be an expert in linear algebra or statistical tools, data scientists are expected to have a basic understanding of mathematics and programming, and at least two years’ experience in the field in order to be placed.
A bachelor’s degree with marks averaging 50% or better in a discipline related to engineering, maths, physics, statistics or business strategy is usually required.
Essential technical skills include mastery of:
- R;
- SQL;
- Python;
- Visualisation tools;
- Programming skills; and
- Data wrangling.
Essential soft skills include:
- Communication;
- Presentation;
- Collaboration; and
- Organisation and time management.
Tools you should know how to use
The analytical tools most data scientists use include:
- Excel;
- Tableau;
- KNIME;
- RapidMiner;
- Google Charts; and
- Python and R libraries.
Other data science requirements
- Documented success with processing large data sets; and
- Experience with modern data science techniques, applied mathematics, and classical statistical modelling.
Top data science careers to pursue
Sketched out in the table below are the duties, learning and experience generally required for the top three data science jobs. While the roles, responsibilities, salary and other prerequisites will differ depending on the industry, level of expertise needed, and type of company looking for people to fill these roles, this should give you an idea of what to expect.
Top Data Science Job Requirements at a Glance
| Data scientist | Data engineer | Data analyst | |
| Duties | Convert raw data into meaningful analysis | Configure new data platforms | Create dashboards |
| Test data and perform data and system analysis | Solve problems in data structures | Identify and interpret trends in data sets | |
| Analyse and design statistical and graphical models | Design and build data transformation structures | Implement databases and data collection systems | |
| Build data solutions using data cleansing and analysis techniques | Develop data processing pipelines using Spart, Python, and Hadoop | Filter and clean data to identify patterns using data visualisation models | |
| Create data visualisations that show the effects of change | Create sound data architecture for the efficient processing of data | Develop and implement data analysis techniques using statistical procedures | |
| Educational requirements | University degree in computer science, maths, statistics, engineering, and physics | Bachelor’s or master’s degree in computer science from a recognised university | Bachelor’s or master’s degree in computer science from a recognised university |
| Knowledge of data analysis software such as Python and SQL | Certificates in C#, SQL, Tableau, MS Excel and Python | ||
| Experience required | Demonstrated success processing large data sets | Excellent project management skills | Object-oriented programming |
| Experience using modern data science methods, classical statistical modelling, and applied mathematics | Working knowledge of extraction, transformation, and loading data systems | Experience in applying C#, SQL, Tableau, MS Excel and Python | |
| Experience in applying Python and SQL | Experience with Java, SQL, Scala, and Python | ||
| Debugging SQL queries |
How to expand your data science credentials
Data science offers some of the hottest careers in the tech industry.
If you have the requisite qualifications but need to add working knowledge of some of these tools to your portfolio, enrol in one of Digital Regenesys’ data science courses to get the exposure you need to Pandas, Matplotlib, NumPy, seaborn, scikitlearn, statsmodel, MySQL, Python, and other tools from our industry experts. They’ll teach you about each concept from scratch.
You might also consider brushing up on your communication, presentation and planning skills with additional short courses from Regenesys Business School, under the auspices of Digital Regenesys. Explore them here.
References
- Glassdoor. (2022). 50 best jobs in America for 2022.
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