Data analyst vs. data scientist

The amount of data copied, collected, and curated is increasing exponentially. This will result in a growing job market for data scientists and analysts. According to the McKinsey survey“Fueling Growth through Data Monetization.” McKinsey & Company, December 1, 2017. https://www.mckinsey.com/capabilities/quantumblack/our-insights/fueling-growth-through-data-monetization. on data monetization, 70% of executives reported at least a moderate change in their competitive landscape due to data and analytics. 

Before considering a data professional career, you should go through this detailed data analyst vs. data scientist comparison. Both these jobs require you to deal with big data but in different ways. A data scientist is involved in data storage, manipulation, and analysis design. In contrast, a data analyst focuses on deriving insights from existing data. The data scientist’s role is to develop innovative approaches to capture and analyze new data. Data analysts can then utilize these. 

What does a data analyst do? #

The role of a data analyst in an organization is to solve business problems. This can help businesses make more impactful decisions. Data analysts do this using tools such as SQL, Python, R, SAS, D3, Power BI, and Tableau. Some of their everyday tasks include: 

• Identifying the organization’s information needs with the stakeholders

• Collecting data from primary and secondary sources

• Cleaning and organizing data

• Analyzing datasets to present actionable trends 

• Creating reports and visualizations to inform data-driven decisions

How to become a data analyst#

You can become a data analyst by getting the right skills. There are college degrees, online courses, and boot camps that can equip you with these skills. Having a degree in data analysis is not necessarily a job requirement. However, you should fulfill the relevant background education requirements. This means a ​​bachelor’s degree in mathematics, statistics, computer science, or finance. The next step is to put your skills into practice through projects. Get an internship or an entry-level job to practice your skills. This will make you look more employable for recruiters.  

What does a data scientist do? #

A data scientist determines which questions need to be answered. The next step is identifying where to source the data to answer those questions. How is all this information processed? The structured and unstructured data go through machine learning algorithms or design predictive modeling processes. With their knowledge of business strategy and analytical skills, data scientists carry out these tasks: 

• Gathering and cleaning data

• Automating data collection and processing

• Creating mechanisms to oversee and evaluate the precision of data

• Designing predictive models and machine learning algorithms for data mining

• Building data visualization tools, dashboards, and reports

How to become a data scientist#

Data scientist roles only sometimes require degrees. Some jobs do require statistics and computer science degrees. The most important thing is developing the right data skills: 

• Mathematics and linear algebra

• Statistics

• Machine learning

• Cloud computing

• Tools like Tableau, Python, Hive, Impala, PySpark, Excel, Hadoop, etc.

• SQL and NoSQL 

• Apache Hadoop

• Calculus

Secondly, learning data science fundamentals is important. This includes data collection, analysis, data modeling, and visualization. You must also familiarize yourself with key programming languages and tools, like R and Hive. Learning how to create data visualizations is important to show your findings. How can you develop all these skills? Our Become a Data Scientist Skillpath is exactly what you need to kick-start your journey. Remember to practice through projects and build your portfolio. This is the best way to impress hiring managers.     

Data analyst vs. data scientist#

Roles and responsibilities#

Skills #

Salary #

Career growth#

If you’re just starting a career in analytics, an entry-level data analyst job would be better for you. This will give you sufficient experience in the business world to derive impactful insights. You can query databases, generate reports with BI tools, and analyze critical data. Using this experience, you can upgrade to a senior data analyst or data consultant. 

There is a need for qualified data scientists in almost every industry worldwide. This includes healthcare, e-commerce, manufacturing, logistics, etc. You can build algorithms and predictive models for these businesses to grow. 

Make the right career choice#

After reading this article, you must have gathered enough knowledge to see which career you would like to explore. This choice depends on your inclination. You should choose a data analyst career if you are interested in analytics. If you are more interested in easing human tasks through machine learning and deep learning, go for a data science career. 

If you’re a beginner, the Predictive Data Analysis with Python course will acquaint you with data analytics. Make this course a stepping stone in your data analytics journey. However, to study data science in depth, start the Grokking Data Science course now. This will help you learn statistics and machine learning fundamentals in no time.