Data scientist vs data analyst: what’s the difference?

Data scientist

Data scientists and data analysts represent two of the highest paying and in-demand jobs in 2022, followed by machine learning, artificial intelligence and big data specialists.

According to the 2021 report published on the World Economic Forum Future of jobs reports the number of data analytics profiles are set to increase in number across all industries in the upcoming few years.

Although both the data scientist and data analyst rules work with data, it may not be always clear what the exact difference between the two profiles is, and the tasks that they carry out in different ways.

In this blog post, we will talk about some of these differences and mention the reasons why you should opt for a data analytics course in Singapore to kick start your professional journey!

What do the data scientists and data analysts do?

Data scientists deal with both unstructured and structured data by leveraging design predictive modelling processes and machine learning algorithms.

Besides making predictions of a brand by using advanced data techniques, data scientists may also engage in the following tasks on a day to day basis:

  • Developing programmes to automate data processing and collection
  • Building data visualisation reports, dashboards and tools
  • Developing processes and tools to analyse and monitor data accuracy
  • Designing machine learning algorithms and predictive models to mine big data sets
  • Collecting, sorting and processing raw data

Data analysts, on the other hand, work specifically with structured data to solve business issues by implementing Python or are programming languages, statistical analysis, and SQL and data visualisation software.

Following are some of the common responsibilities that a data analyst must fulfil:

  • Helping business owners make informed data-driven decisions by presenting findings that are easy to understand
  • Generating actionable insights by analysing data sets spot patterns and trends
  • Reorganising and sorting data for analysis
  • Acquiring data from internal and external sources
  • Identifying informational needs by collaborating with organisational leaders

What is the different set of skills and tools that data scientists and data analysts use?

Data scientists are commonly known to leverage advanced statistics, data modelling, machine learning and predictive analytics, along with upgraded object-oriented programming and software like Hadoop, MySQL, Tensorflow and Spark.

Data analysts utilise statistics and foundational mathematics, alongside analytical thinking, data visualisation, R, Python, SQL, business intelligence software, Microsoft Excel and SAS.

What are the educational requirements needed to become a data scientist or data analyst?

A diploma in technology programme offered in Singapore is tailored to provide you with the advanced knowledge and industry-relevant skill sets that are essential for landing a job as a data scientist or analyst.

The course curriculum of the diploma in data analytics includes code topics such as statistics, computer science, Information technology, data science, mathematics and finance.

Learn more about the data analytics diploma in technology programme offered in Singapore and take the first step on your data science career path, today!

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