A Comparesion Between Data Scienctist, Data Analyst and Data Engineer

Three of the most important data related roles in the field of data science, are Data Scientist, Data Analyst and Data Engineer. In this article we’re going to pit them against each other ..

Firstly we will talk about the job descriptions, the skill sets required for each role, the salary roles and responsibilities and the companies hiring for these positions. Let’s have a look at each of these roles in detail.

First off let’s have a look at data scientist. A data scientist is able to create machine learning based tools or processes within the company.

  • Data scientists use advanced data techniques such as clustering, division trees, neural networks and so on so that they can derive business conclusions.
  • Data scientists are the senior most members in the team which involves a data engineers as well as a data analysts.
  • Data scientists need to have in-depth knowledge of statistics, data handling and machine learning. They also take inputs from data engineers as well as analysts so that they can formulate actionable insights for the business.

Data scientists also need to have the same skills as a data analyst and an engineer but need to have a lot more in-depth knowledge and expertise with these skills.

Data analyst is someone who is able to translate numeric data into a form that everyone in the organization can understand, now this is an entry-level position in the data analytics team, he or she needs to have technical skills in programming languages such as Python and have knowledge of tools like Excel and understand the basics of data handling, modeling, and reporting. in due time they can move up the ranks by taking up roles of data engineer and data scientist with some experience that they can accumulate over the years.

Data engineer is someone who’s involved with preparing data for analytical or operational purposes.

  • They are the intermediary between the data analyst and the data scientist.
  • He or she needs to have a lot of experience when it comes to developing, constructing and maintaining architectures.
  • They do generally work on big data and submit their reports to the data scientist so that they can be analyzed.

Required Skills

Let’s have a look at the skill sets required for each of these roles. First, off we have data scientists, since this role is a little more coding-oriented you need to know a great deal when it comes to programming languages such as Python, R, SQL, SAS, Java, and so on. You also need to be well-versed with frameworks in relation to big data such as big, spark, and Hadoop. Also, need to be well versed with machine learning deep learning, and other similar technologies. Next up we have data analyst, these roles are much less technical as compared to a data scientist as well as a data engineer considering how its entry-level, here knowing programming languages is a great bonus so an idea about programming languages such as Python or SQL javascript SSAS and so on is a great benefit, at the same time you do need to be well-versed with tools such as SAS miner, Microsoft Excel, SSAS, SPSS and so on.

Finally, we have the data engineer. Being a data engineer requires you to be well versed with a bunch of programming languages as well as frameworks. You need to know about programming languages such as Python, R, SQL, SAS, Java and so on while having expertise in frameworks such as Hadoop MapReduce, Hive, Pig, Apache Spark, Data Streaming, No SQL and so on.

Salary

Talking about money or the salary each of these roles get. Firstly we have the data scientists who earn 137 thousand US dollars per annum, then the data analyst who earn 67 thousand dollars per annum. A data engineer which is in the median with a hundred and 116 thousand US dollars per annum.

Roles and Responsibilities

Now, what is the roles and responsibilities that each job has. Firstly we have the data scientist.

  • A data scientist gets to work with a lot of unstructured data so they need to mine and clean the data so that it’s usable.
  • They need to be able to design machine learning models to work on Big Data.
  • They need to infer and interpret the analysis on big data to be able to lead an entire team to achieve the goals of the organization and deliver conclusions that have a direct business impact.

In regards to the roles and responsibilities of a data analyst.

  • Data analysts need to use queries to gather information from a database.
  • They need to process the data and provide summary reports.
  • They need to use basic algorithms for their work such as linear regression logistic regression and so on and have core skills in statistics, data munging, data visualization and exploratory data analysis.

Finally, we have data engineer.

  • Data engineers need to mine through the data so that they can gain insights from it.
  • They need to convert erroneous data into a useable form so that they can be further analyzed.
  • They need to write queries on data.
  • They need to maintain the design as well as architecture of the data and create large data warehouses using ETL or extract transform node.

Companies Hiring

Let’s have a look at some of the companies hiring for this role firstly for data scientists we have Citibank, Facebook, Schneider, Intel, Amazon and so on. For data Analyst we have Infosys Oracle, VISA, Capital One, Walmart and so on. For Data Engineer we have Google, Cisco, FlowCast, Apple, Spotify and much more.

So,,, if all this is inspired you to get started with data science we suggest you take simply learn certification, we choose this certification because it acts as great entry point for starting your career as a data analyst or a data engineer. With this certification it goes through all the important concepts when it comes to data science, and has 68 hours of in-depth learning for Elf industry based projects, interactive learning with Jupiter notebooks and dedicated mentoring sessions from faculty of industry experts.

Some of the concepts are you’ll be going through statistical analysis and business applications, Python environment setup, mathematical computing with Python which is NumPy Scipy, data manipulation with pandas, machine learning with Scikit-learn and so much more. Then, you can start up on your step to becoming a data analyst, a data engineer and then eventually a data scientist.

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If you’re already working as a data analyst or a data engineer you can become a certified data scientist which simply learns data scientist master’s program. This goes through all the important concepts that you need to know so that you can become a successful as well as certified data scientist.

Source: Simplilearn

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