Data analyst versus data scientist: Where do the differences lie?

A data scientist is commonly perceived as an expert who can predict the future ofdata based on its past patterns, whereas a data scientist is someone whose role is primarily to curate meaningful insight from a piece of data.

From this, we can easily understand that the job role of a data scientist is related to estimating the unknown, whilst that of a data analyst is to seek newer perspectives.

So, it becomes easy for you to decide your career path once you have considered asking yourself whether you want to generate never before heard questions for your life are you want to find answers for the questions raised?

The best way to clear the dust is by undergoing a Diploma in Data Analytics course offered in Singapore.

It is only by completing this professional degree will you be able to address modern business problems, or even become an expert at recognizing them.

Read on to find out more!

What are some of the basic differences between a Data Scientist and a Data Analyst?

Data scientists are typically known to poses data visualization skills and strong business acumen that help them explore and examine data from various sources.

Data analyst, on the other hand, lays their focus on solving business issues by developing statistical models and being versed with machine learning.

Is there any difference in skills between a Data Analyst and a Data Scientist?

Data analysts are typically masters in using SQL for slicing and dicing the data to fulfill their scientific curiosity.

A data scientist is expected to hold a strong foundation in analytics, mathematics, statistics, modeling, and computer science.

What are some of the differences in responsibilities between that of a data analyst and a data scientist?

Data Analyst job roles

  • Finding answers to complex business problems by drafting convention SQL queries;
  • Mining and analysing business data to discover patterns and identify correlations from various data points;
  • Identifying partialities in data acquisition and data quality issues;
  • Understanding innovative business operations by implementing new metrics;
  • Solving business problems by mapping and tracing data from system to system;
  • Gathering incremental data sets by coordinating with the engineering team;
  • Providing help to business executives in making better decisions by designing and creating data reports that can be leveraged when using various reporting tools;
  • Applying statistical analysis by utilising data visualisation software such as powerBI, Microsoft Excel, Tableaue.t.c.

Data Scientist job roles

  • Unlocking the value of data to find new features or products that can add value to the business;
  • Cleaning and organising data for analysis, as well as correlating the separate data sets;
  • Developing innovative machine learning models and analytical methods;
  • Applying an epidemiological approach to conduct casualty experiments, when identifying root causes of an unforeseen event;
  • Identifying new business questions and drafting explanations based on data storytelling.

Regardless of the differences and similarities between a data analyst and a data scientist, it is no wonder that the former is incomplete without the latter and vice versa.

If you are passionate about solving real-world problems then apply for the Diploma course now, to get hands-on training on tailor-made machine learning and data science projects!

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