Data Analyst vs Data Scientist : Which Career to Opt for?

In today’s rapidly evolving world of science and tech, big data alone holds the cards, especially when it comes to the world of business. Innumerable jobs are constantly cropping up in various new verticals such as datasets and data visualization that offer critical insights into voluminous data. Consequently, as a result of this, two flourishing career paths that have shaped up in response to the scaled emphasis on artificial intelligence and business analytics is that of data analytics vs data science.

Data analysts and data scientists were two of the most high-paid professions in 2021. Also, as per the World Economic Forum Future of Jobs Report 2020, data analyst and data scientists held the number one position owing to their increasing demand across industries, and this was subsequently followed by AI and machine learning specialists.

While there’s plenty of interest in data professionals, but these two professions may sound synonymous to many. However, that isn’t the case as there are quite a few differences between data analyst and data scientist, the crucial one being that while a data analyst closely works with data visualization and statistical analysis to better understand data and identify trends, but a data scientist on the contrary, is responsible for developing frameworks and algorithms to collect data that the business could use.

If you find this interesting and would like to further know about these professions, then this detailed blog endeavors to throw light on various important dimensions of the data analyst vs data scientist job profile, their responsibilities, salary, and more. We have expounded in detail about all these crucial aspects that you must know about before you decide to opt for either of these two programs. So make sure to give this blog a read and then make the right choice for yourself.

 

Data Analyst vs Data Scientist : Job Profile

Data Analyst :

A data analyst helps collect and organize data and thereby obtain statistical information from the data accumulated. Data analysts are also responsible for creating detailed reports and visualisation and presenting the voluminous data in a lucid manner. This is typically in the form of charts, tables and graphs so as to make it easier for other people to interpret and use the data. These data analysts therefore assist an organisation to work around crucial data insights that may in turn help shape up the future business decisions. They use data manipulation techniques to better analyse and interpret the complex data sets to help businesses make better decisions. Furthermore, the job profile of a data analyst involves answering questions about business operations. They help enterprises improve their overall work operations in order to increase their productivity and profitability. For instance, a data analyst may take the results obtained from a market research survey and then see how those numbers could be extrapolated to the wider target market. The survey results can in turn help a data analyst guide an enterprise to function optimally.

 

Therefore, some day-to-day tasks of a data analyst may include :

• Acquiring data from primary and secondary sources.
• Cleaning and reorganising data for better analysis.
• Collaborating with organizational leaders to recognize the informational requirements.
• Meticulously analyzing data sets to identify trends and patterns that could be translated into actionable insights.
• Furnishing findings in an easy-to-understand manner to help an enterprise take informed data-driven decisions.

 

Data Scientist :

A data scientist is a professional who closely analyses data from a business perspective, and he accordingly makes predictions to further help businesses take accurate decisions. A data scientist is someone who can predict the future based on past patterns as their job involves estimating and analysing the previous data insights. Data scientists are therefore quite astute and skilled in various computer applications, statistics, maths and modelling. They are responsible for collecting and cleaning up the data to make it more understandable and usable. These data scientists look around for patterns to create algorithms and models so that enterprises could use the data collected and accordingly interpret it in various scenarios.

These professionals more often than not work with data engineers and business leaders to put into meaningful use the data that they collect. An easy-to-understand example of data science is that of customer segmentation. Quantifying differences in customer buying behaviour and then pairing those with various demographics to better target customers can offer enterprises with the correct marketing strategies.

Some of the most common tasks of a data scientist entail the following :

• Accumulating, cleaning and processing raw data.
• Building predictive models and machine learning algorithms in order to mine big data sets.
• Creating tools and processes to evaluate and analyse data accuracy.
• Developing data visualization dashboards, tools and reports.
• Writing programs to automate the process of data collection.

 

While both of these professions are certainly poles apart, and we have enumerated the key differences below, but however, the data analyst and data scientist roles are very much in demand, and well these two professions also repeatedly topped the list of Glassdoor’s Best Jobs in America.

Furthermore, Angela Ahrendts, Senior VP of Retail at Apple rightly said that consumer data would be the biggest differentiator in the coming years, and it is therefore imperative for businesses to unlock the actual potential of data and use it strategically. Data analysts and data scientists are certainly the linchpins who help do so.

Now here’s a succinct overview of the major differences between data analyst and data scientist.

 

Requirements for a Data Analyst :

 

Students who are keen on pursuing a career in data analysis would need to meet a few requirements such as :

(i) Education : Those who want to work in the data analysis vertical should hold a bachelor’s or master’s degree in a field that is related to data analysis such as mathematics or statistics.

(ii) Must Know Computer Programming Languages :

It is a cherry on the cake should a candidate know programming languages such as Python, CQL, SQL and R as these have immense usage in data analysis.

(iii) Essential Soft Skills & Technical Know-How :

It is imperative to have excellent verbal and written communication skills with great analytical skills. Also, experience in data mining along with know-how of some of the latest technologies pertaining to data analysis and machine learning algorithms are desirable.

(iv) Microsoft Office Skills :

A thorough understanding and command over the Microsoft Office suite is essential for data analysts to effectively communicate their findings and translate those into easy-to-comprehend reports and presentations.

 

Requirements for a Data Scientist :

 

Students who are keen on pursuing data science must have the enthusiasm to take a deep dive into the humongous sea of data wherefrom they need to decipher and bring about quantifiable and usable insights. In addition to this, students must also fulfill the below mentioned requirements :

(i) Education :

A student must hold at least a bachelor’s degree in data science, however an advanced degree such as a master’s or a Ph.D in a related field such as computer science, mathematics or even statistics would surely be a wonderful feather to the cap.

(ii) Must Know Computer Programming Languages :

It is preferable for students to know programming languages such as SQL, Java, R and Python.

(iii) Prior Experience with Data Mining :

Professionals working as data scientists are required to have extensive experience with data mining and with certain tasks and tools that involve developing the data architectures, text mining, conducting complex tests and so on.

(iv) Prior Experience with Web Services & Data Sources :

Web services such as Spark, Hadoop, S3 and DigitalOcean and third-party data sources such as Google Analytics, Crimson Hexagon, Site Catalyst and Coremetrics play a significant role in the day-to-day job of a data scientist, and a candidate should be therefore well versed with these.

(v) Prior Experience with Statistical Tools & Technology :

It is preferable for a data scientist to have hands-on experience with various statistical tools and technologies such as machine learning, artificial intelligence and deep learning.

 

While data science generally involves greater use of computer science, however, the program can be undertaken by students from any stream, provided the student had mathematics and obtained a minimum of 50% aggregate in senior secondary.

 

Having that said, the scope of data science and data analytics is immense and there is constant professional growth for those who pursue either of this highly-sought after careers. Herein we would like to mention that the Robert Half Salary Guide, 2020 observed that a data analyst in the US earns anywhere between $83,750 to $142,500, depending upon his skills and expertise. Data scientists, on the contrary, earn even more that is around $105,750 to $180,250. It is therefore quite important to take up such job-oriented professional courses in today’s day and age as the same would make a significant impact in shaping up one’s career.

 

KR Managalam offers a one-of-its-kind three-year undergraduate program that is B.Sc. (Hons.) in Data Science. Our course is a fine blend of world-class pedagogy with immense practical exposure to each of our students as we envision to shape the leaders of the future. We have a multidisciplinary curriculum with innovative teaching methods that ensure robust skill development in all our students who thereafter attain professional excellence and our venerable legacy with illustrious alumni are a testament to that.

 

1. Which of the two is better: data analyst vs data scientist?

While both the professions have great potential with innumerable work opportunities, however if you’re a math lover and would like to dive into the technical world of coding and modelling, then a degree in data science is apt for you. On the contrary, if you are good with numbers and possess good communication skills, then you may consider a profession in data analytics. While data science is surely a lot more well paid, but irrespective of that, both data analyst and data scientist have immense career growth opportunities.

2. Is data analyst a good career? 

Well data analyst is undoubtedly a highly sought-after career choice that is really well compensated. The course empowers professionals to use their analytical thinking skills to resolve various business concerns. Also, with the immense increase in digitalization and technological advancements, the demand for data analysts has grown manifolds in the recent few years. So if you enjoy playing with numbers, then rest assured, data analytics is a great career choice to opt for.

3. Can a data analyst subsequently become a data scientist?

Yes, a data analyst can become a data scientist by honing their programming expertise, developing strong mathematical and analytical skills, and also by having a profound understanding of various machine learning algorithms.

4. . What are the common skills used by both data analysts and data scientists?

Well both data analyst and data scientists make use of programming to clean, transform and analyse the voluminous data. They also use various BI tools such as Tableau and Excel to create business reports. Furthermore, both data analyst and data scientist are the ultimate masters in the field of data wrangling and data visualisation.

5. What is the course fee for pursuing B.Sc. (Hons.) In Data Science at KR Mangalam University?

Our course fee for B.Sc. (Hons.) in Data Science is about Rs.1,03,000 per annum. We however offer various scholarships to meritorious students, and in addition to this, we have also partnered with banks that offer low-rate education loans to our students. Students are required to repay the loan only a year after completion of their program or six months after their job placement, whichever is earlier.