data scientists (R, Python, SAS)

Did you know that data scientists are offered one of the highest salaries and promotion rates? Perhaps therefore, some leading professional networking sites chose data science as the most promising career in 2019. Without a doubt, picking a career in data science is synonymous to picking a future oriented career that will reward you professionally and financially. However, analysing data for actionable insights is not everybody’s cup of tea. It requires a multitude of skills and qualifications.

early stage

At the entry-level, professionals must bring a combination of hard and soft skills to the table. Data scientist jobs require the following hard and soft skills:

  • R Programming
  • Python Coding
  • SQL Coding
  • Machine Learning
  • Artificial Intelligence
  • Data Visualisation
  • Interpersonal Skills
  • Creativity
  • Curiosity
  • Ability to work in a team

To secure data science jobs, professionals must enlist in data science courses; it also pays to get a certification training course in Tableau and Big Data Hadoop for data science engineer jobs. Entry-level data science analysts typically perform duties such as analysing complex datasets, identifying meaningful patterns to make actionable recommendations, validating predictive models, and communicating with stakeholders to understand business needs and offer analytical solutions.

Some top industries that recruit data analyst and data scientists:

  • Healthcare and Pharmaceutical Industry – Medical image analysis, genetics and genomes, predictive medicine, etc.
  • Telecommunications Sector – Helping telecom companies in offering customised products and services to their customers
  • Energy Sector – Discovering unconventional sources of energy, reducing costs on exploration and drilling, preventing power outages, etc.
  • Automotive Sector – Quality control, root cause analysis, predictive maintenance

career path

A data science career can take shape along different paths:

  • A Business Intelligence (BI) Developer is responsible for designing and developing strategies that allow business user to find the information they need at the right time, which ultimately supports improved decision-making
  •  An Applications Architect tracks the behaviour of applications used in an organisation and how applications interact with each other and with users
  • A Data Science Analyst analyses and transforms huge volumes of data sets to the desired form for organisations, using a combination of programming and statistical tools
  • A Data Science Manager takes it a step further and communicates the business impact of data to stakeholders

what the future holds

There’s no doubt data science is rapidly growing and promises abundant career development opportunities and data scientist openings. The future of data science looks towards specialisations. More advanced concepts of Deep Learning and Neural Networking are gradually taking shape. Professionals looking for entry-level data science jobs and ultimately to build a data science career must brush up on the required programming languages and Machine Learning and Deep Learning skills in particular.