Data Scientist vs Data Analyst

Data Scientist vs Data Analyst – The Career Comparison

Are you a Data Science freak? Did you ever confuse yourself between “Data Scientist” and “Data Analyst”? Well, this has been the greatest confusion for young professionals to choose a perfect career in Data Science.

| The key to an extraordinary business is Data-driven Decisions.

So what is Data Science? It is the recording, storing, and analyzing data to effectively extract information from raw data. And here is where Data Scientist and Data Analyst comes into picture. Today in 2020, the number of job listings in data analytics and data science has grown to 2.7 million. While both Data Scientist and Data Analyst work with data, the way data is dealt is quite different. Data Scientist vs Data Analyst has been the hot conversation in the web.

Difference Between Data Scientist and Data Analyst

Data Scientist is responsible for interpreting complex data and analyzing the unknown by building statistical models. They pose multiple skills like coding, mathematical, statistical, analytical, and ML skills. Data Scientist use machine learning extensively for prediction and solve business problems.

While on the other hand Data Analyst analyse the raw data and gathers, investigate data to extract useful information. They are responsible for cleaning the data. They need to be expert in statistics to analyse results using statistical technique.

Choosing the Best Career

Now that you are crystal clear with the differences, let me take you through some skill-based career path you should select for yourself.

• Data Analysts are more customer facing and are directed towards customer needs and satisfaction for business development. If you consider yourself good at communication skills along with strong analysing skills with a passion towards Data science, you may opt as a Data Analyst.
• A Data Scientist should possess strong business acumen and advance data visualisation skills. They can organize undefined sets of data using multiple tools at the same time, and build their own automation systems and frameworks. Data scientists, are focused on designing and constructing new processes for data modelling and production.

“Anyone who keeps learning is young and the greatest thing in your life is to keep your mind Young”

~ Henry Ford

 

Develop your skills for the industry requirements through our course on Data Science

Salary Insights

• Data analysts love numbers, statistics, and programming and work almost exclusively in databases to uncover data points from complex sources.
• As a Data scientists, you should have a blend of math, statistics, and computer science and an inclination towards the business world.

A Data Scientist earns significantly more money than Data Analysts due to their work difference. The median salary for data scientists is $113,436 while data science managers leading the data science team at an organization earn an average of $176K.

The average salary of a data analyst depends on the type of analyst you are – financial analysts, market research analyst, operations analyst, etc. According to a salary survey report the average salary of market research analysts is $60,570, operations research analyst on average earn $70,960 and the average salary of a financial analyst is $74,350. As of 2019, entry-level salary for a data analyst ranges from $50,000 to $75,000 and for experienced data analysts it is between $65,000 to $110,000.

Listing down every job here will be difficult and time consuming. Every organization which exists today requires both Data Scientist and Data Analyst. Hence is preferable to acquire relevant skills and experience in the same. You may visit your desired company profile’s career section to know about job postings and requirements.