Difference Between Data Analyst and Data Scientist | Salary | Tools | Career Choice |

Hello and welcome back to yet another blog and today, I'm going to talk about two most popular positions or you can call it, two most in-demand job profiles. The first, being a Data Scientist and the other being a Data Analyst. Here I’m going to tell you what are the things that you should know when beginning a career in Data Science. One of the most popular questions that arise is, what is the difference between a Data Scientist and a Data Analyst? And how should we choose in between these two?

Difference Between Data Scientist and Data Analyst

The best way to find an answer to the above question is, to look at the job descriptions, and also to check some of the LinkedIn profiles. So, for this, I went through different LinkedIn profiles and their job descriptions, and this is what I have found. Now, I would also suggest you for the same, i.e. visit the LinkedIn profiles and check the job descriptions.

After a keen observation, this is what I found to be the difference, in both these job profiles.

1. A Data Analyst needs to Collect, Explore, and Analyze data, and they also need to identify the trends and patterns, from the data that they have collected. And then lastly, they need to create visualizations and also produce reports.
2. On the other hand, a Data Scientist, also needs to analyze the data, but they are more often required to Design, Experiment, and also test Hypotheses. And then, based on those outcomes, they need to apply different algorithms to build machine learning models, and then deploy those machine learning models to a production environment.

In a nutshell, both Data Scientists and Data Analysts need to gather actionable insights to make informed decisions.

Skills Required

Let's look at the skill that you need if you want to become a Data Scientists or a Data Analysts. The three main skills that you need are:

1. Mathematics and Statistics.
2. SQL Queries.
3. Communication Skills.

Data Science uses statistical approaches like T-Test, Standard Deviations, and various others to help you to explore data, detect signals, and also identify the unit patterns and trends. For mathematics, you will need to apply different algorithms to train and build models, like regressions, decision trees, clusters, and also neural networks.

You also need to master SQL (Structured Query Language) to help you to assess and manage the data in the database, so that you can use it to create, retrieve, update, and also delete records.

I consider communication as one of the most important skills because, often we have to make presentations to people who are less technical savvy, such as the end-users, consumers, and also stakeholders. Now good communications include, asking the right questions instead of just talking, and to listen, and to observe their responses, and also how do you motivate your audience to take action? So, when you present your findings or results, you make use of the data, to support and to show the impact of consequences, if they don't do so.

Tools used by Data Scientist and Data Analytics

Let us look at the tools that Data Scientists and Data Analysts use it when they are doing their daily jobs. When they are exploring data, especially when the data is not super huge. They use the tools that we are familiar with, like Microsoft Excel because they are easily accessible.

And, when they are producing visualizations or building dashboards, they normally use Business Intelligence or BI tools, such as Power BI, Tableau, to deliver professional charts and reports. If they want to perform some serious heavy lifting tasks, such as writing algorithms, building predictive models, or machine learning, they will turn to a programming language like Python or R.

Career prospects

When it comes to career prospects, we often hear that companies are hiring Data Analysts and Data Scientists. But how high are the demands for these positions? Let's find out. According to LinkedIn, the number one emerging job in Malaysia is Data Scientists. Now it was also the number one in 2019, and to climb up from number five in 2018. And it continues to be the most demanding job within the US for 3 years in a row, Data scientists are expected to be in demand even in the upcoming year.

Salary Analysis

A junior Data Analyst has a starting pay at around 45k – 60k per month, and then their salary increases, as they progress with different tasks and responsibilities. So that a Senior Analyst, who is experienced makes about 1L – 1.2L per month. And as Analytics Manager, just like all managers, they are responsible to see a team of Analysts and their primary duties are, to manage and lead them towards achieving goals by implementing different analytical solutions, and so they make about 1.7L – 2.2L per month. And at the director level, they work more closely with the management, and more involved in their day-to-day operations and using data analytics in setting directions, and they make about 2.5L - 5L per month.

An entry-level Data Scientist makes around 45k – 60k per month. And once they're able to prove their value, the salary goes up to quite high like 80k per month. And a Senior Data Scientist can make up to 1.2L – 1.5L per month. Data Scientist is a new role that's constantly evolving. The benefit of gaining experience as a Data Scientist is, many companies don’t hire a Lead Data Scientists or Data Science Manager, rather the most Senior Data Scientist is usually made the team leader of a Data Science Team. The same is for a Data Science Director, the senior Data Scientists are promoted to a Chief Data Scientist, or sometimes Chief Data Officer, or Chief Information Officer. And therefore, their responsibilities and salaries are pretty much on par, with other C levels position like CEO and COO.

What to choose between Data Scientist and Data Analyst?

For us, the key question here is, how do we choose between a Data Scientist or a Data Analyst position? But before that, let's try to understand the process of an analytics project. It usually covers three phases from Descriptive Analytics, Predictive Analytics, to Prescriptive Analytics. After having some research, I found that Data Analyst usually suits someone, who prefers to be more specialized in the industry, for example, Students/Professionals who prefer to work in the finance industry, they choose to become a Financial Analyst, or a Risk Analyst, or a Fraud Analyst. As they need to have a deeper understanding of those industries, to know where they can find the right data to help them to answer the questions.

Whereas, Data Scientists are someone more of a generalist. And they usually work in different areas, i.e. from agriculture, IoT, retail, eCommerce, to even epidemics. Data scientists work very closely with data analysts and use the outputs from their analysis, to build models and put those models into different applications.

Conclusion

At last, I would like you to understand that Data Scientists and Data Analysts are two very different roles. While Data Scientists are also good at analyzing data, but their main goal is to build models and take those models to production. Data Analysts should not be seen as a junior version of the Data Scientist, as they can also develop their expertise in respective fields. So, on this node, I would like to wrap-up this article. I hope this blog clears all the doubts and helps you in making a better decision about which career you should choose in a Data Science Industry.