4 Differences between Data Science and Data Analytics

As we all know, the tech industry offers some very lucrative jobs. In particular, data science and data analytics have been talked about a lot in recent years. However, do you know the differences between them? Fear not if you don’t because this article will tell you all you need to know to differentiate between the two! Let’s start with the first difference.

1.  Data Science operates on a macro level while Data Analytics examine the details.

Typically, a data scientist is tasked with working out new ways to model data. They’re primarily concerned about how data is stored and manipulated. They consider what the organisation needs, considering its resources, constraints, and aims, and then use this information to decide how to store and analyse information for the organisation.

On the other hand, a data analyst has to analyse the data given to them and solve specific problems that an organisation has. Thus, they have to be detail-oriented and able to get right to the crux of a problem. Simply put, they have to be good at understanding data in the context of a specific scenario.

If you’re considering either job as your future career, these are the following questions you can ask yourself:

  • Am I more business-oriented or data-oriented?
  • Do I see the big picture, or do I care more about the details?
  • Do I enjoy finding the best way to do things or solving problems?

2.  Data Science involves more programming and is more challenging than Data Analytics.

Data scientists are commonly tasked with programming jobs such as doing data cleaning using programming languages such as Python or R.

They are also usually asked to perform statistical analysis using machine learning algorithms and to implement programming techniques to automate day-to-today tasks.

On the other hand, the job duties of data analysts do not involve programming as much. They usually have to do data clean-up using two main tools known as SQL and Excel. Other than that, data analysts are expected to perform data analytics and forecasts via Excel and create dashboards using business intelligence software.

Being a data analyst is usually a stepping stone to being a data scientist. It is less challenging to be a data analyst as you need less technical knowledge, and the software used is also more straightforward. However, different companies may require you to take on additional job responsibilities even if the job title is the same.

3.  Data Science has higher educational requirements than Data Analytics.

Many data scientists typically have a master’s or a doctoral degree in data science, information technology, mathematics, etc. This is because more technical knowledge and programming know-how is required to work in this field.

On the other hand, most data analytics roles require a bachelor’s degree in mathematics, statistics, computer science, etc. For more advanced data analytics positions, the employee will be required to hold a master’s or a doctoral degree.

However, there are also online courses you can take to kickstart your journey in this industry. The most reputable ones are IBM and Google. They exist to help anyone who wants a career change to pick up the relevant technical skills to work in the data analytics and data science fields.

4.  Data Science commands a higher salary than Data Analytics.

According to Glassdoor Singapore, the average salary of a data scientist is $6500. It ranges from $4000 to $10,000. The average salary for a data analyst is $4493. It ranges from $3000 to $7000.

It makes sense that the average data scientist would earn more than the average data analyst as more technical know-how and a higher level of education are required to qualify for a data science job.

While salary may be a huge factor in your decision on which job to choose, do make sure to also consider other things such as work-life balance, employee benefits, as well as cultural fit.

FAQ

  1. How long does it take to learn Python?

Python is an important programming language for both data scientists and data analytics as it can be used to manipulate and work with data efficiently. Python is considered a beginner-friendly language that can be learnt fairly quickly. However, you’ll definitely have to practise and build software to truly be a master at it.

2. What are some jobs under data analytics?

There are jobs such as machine learning engineers, business intelligence analysts, applications architects, and so on. They revolve around analysing the data of a system for a specific function. For example, a machine learning engineer would work with the data extracted from machine learning to build tests and experiments.

Conclusion

Data science and data analytics may be confusing because they both deal with data. However, once you dig into them, you will realise they consist of different job responsibilities, work with different tools, and have different educational requirements and salaries.

 

 

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