Data Scientist vs. Data Analyst

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No one can deny that data is vital for tech companies, and it is being generated at an unprecedented rate. Data generated is often in the raw and unorganized form containing errors.  One needs exceptional capability and ample tools for transforming the data into worthwhile or purposeful outcomes. In the absence of the precise tools and pertinent skill set, the mammoth task of data collection is irrelevant and a costly affair for tech giants. Tech companies persistently invest in developing, procuring suitable tools, and hiring experienced professionals. Hence the demand for data experts, namely the data science and analytics sector, is shooting up dramatically.

As per one of the articles in India Today, India will have close to 11 million data science job vacancies by the end of 2026. In fact, there were more than 93k data science job vacancies at the end of Aug 2020 in India. Demand is ever growing with the emergence of new fields such as IoT, machine learning, blockchain, digital media, big data analytics, etc. No matter which company one seeks, data expert roles are critical for every department. Apart from high demands, data expert roles have high salary potential and growth. Average annual salary increases with skills and higher experience level.  

With high demand and salary, one may be tempted to get into the data space. However, one may need to possess exceptional talent and command over the latest technologies to be a successful data expert.  Mere degrees in mathematics, statistics, or computer science will not be sufficient to make a good impression in a highly competitive job field. One may require additional efforts beyond theoretical knowledge like enrolling in a data scientist course

In the current article,  We will examine both job roles in detail and highlight the significant differences or similarities.

Data Scientist

As the name suggests, data scientist encompasses extensive knowledge of data and science. A data scientist primarily focuses on using advanced mathematical and statistical techniques to solve complex business problems. Data scientists may need to develop algorithms, use predictive modeling and domain knowledge to solve issues. It is a research-based role that requires extensive investigation to gather, clean, store data to develop mathematical models. 

Data Analyst

The data analyst primarily works in a team of data experts seeking a common goal of collecting, cleaning, and analyzing data to identify trends or patterns. Data analysts work to solve specific business problems and use simple tools along with basic programming.  Data analysts may need to work on SQL, python, and visualization tools such as Tableau for better data insights.

As we have seen broadly, what data scientists and data analysts are. Let us deep dive into other aspects of both job roles.

Qualification

The best thing about both roles is one does not need to have a specific degree to pursue the position. It is recommended for graduates in computer science, information technology, mathematics, statistics; however other streams may also seek the role if they are interested. A master’s degree may enhance one’s potential along with specific credentials. Strong domain knowledge is vital for a long-lasting career. 

Day to Day Work

Data scientists collect massive volumes of data, clean and organize it in two groups, namely for training and testing the models developed at a later stage. Once the machine learning algorithm is developed based on statistical and mathematical concepts, the model is trained and tested to determine and refine the model parameters. The final step is the deployment of a model on the actual data for future prediction or data-based decisions. 

Data analysts collect data, filter any unwanted data, write complex SQL queries, perform exploration to important group parameters, perform analysis to understand any relationship between parameters, visualize using graphs, charts to resolve issues or improve products. Unlike data scientists, analysts don’t develop mathematical models or work on complex algorithms. 

Skill and Tools

  • Data analysts require a basic understanding of statistics and mathematics, while data scientists must have in-depth knowledge of algebra, statistics, differentiation, and calculus.
  • Data analysts and data scientists work on Python, SQL, R; however, while analysts may need basic knowledge, scientists should be well versed with multiple languages and other tools such as Hadoop, Hive, Spark, NoSQL database, SAS, etc. 
  • Analysts and scientists work with PowerBi and Tableau for Dashboards and Appealing visualization. Data analysts may work on Microsoft Excel as data may be small.
  • Data scientists need to have a strong understanding of machine learning, predictive analytics, mathematical modeling, while data analysts need to have a good knowledge of the domain, exploratory analysis.
  • Both scientists and analysts should possess skills such as good problem-solving, analytical, and communication skills. Both should be of the creative and agile mindset, keeping customer requirements at the forefront.

Pay Scale

Both data scientists and data analysts are in high demand and with good earning potential. 

The average annual salary of

 Data Scientist – ₹854,985

The average annual salary of a Data analyst – ₹447,224

Salary depends on multiple factors such as experience level, qualification, additional skill, credential obtained, job location, nature of responsibility, and organization. 

Conclusion

Data scientist or Data analyst both roles are suitable to opt, depending on individual capability and interest level. One can even transition from one position to another quickly with a little bit of training and regular effort. Whichever course one is interested in, it is highly recommended to earn relevant credentials to excel in the domain. There is plenty of good online training along with certifications available on prominent platforms. All courses are designed and drafted by domain experts and help one develop a good understanding of the latest tools and technologies. One can learn either data science or analysis at their own pace at the comfort of home. 

Apart from data-related skills, one should also concentrate on developing soft skills as, most of the time, a data expert needs to collaborate in teams. Good communication skills, oral and written, being a team player, demonstrating agility, and passion for learning new technologies are vital in the contemporary era. For experienced individuals willing to opt for a data expert role, strong leadership and project management skills. Data expert roles require strong logical, analytical, and data-crunching skills. A master’s degree is highly desirable, which instills the passion to research or explore new technologies or urge to solve problems in innovative ways.

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