Data Scientist Vs Data Analyst Vs Data Engineer
I am a person who is willing to pursue a career in the field of Data Science. With the help of ScholarX mentorship program by the Sustainable Education Foundation, I have discussed and learned a lot about the careers in the data science field from my mentor Dr. Sumudu Tennakoon. So in this article, I am writing about the three major careers in the data science field. I hope this would be really helpful for freshers who are getting into this field.
The Need for Data Science
Today’s world is driven by data. Data has the potential to unlock the success of any industry, from unleashing ideas to improving decision-making processes. Data has profoundly changed the world as we know it, to the point where it’s impossible to function without the insights gained from data in any domain.
Because of the vital insights and trust that data provides, there are various professions in the sector today that deal with it. Rather being a jack-of-all-trades, companies prefer to hire for particular, specialized skill sets. Therefore, you must first understand the differences between the three most common data roles - Data Engineer, Data Analyst, and Data Scientist. In this article, we’ll look at the key differences and similarities between a data analyst, data engineer, and data scientist roles.
Data Engineer
Data engineers create and manage data architectures, as well as make data accessible for business operations and analysis. Within the data ecosystem, data engineers extract, integrate, and organize data from a variety of sources. They clean, transform and prepare data design, store and manage data in data repositories. They make data available in formats and systems that can be used by a variety of business applications as well as stakeholders such as data analysts and data scientists. A solid technical background is required of a data engineer.
Roles and Responsibilities
- Develop large data warehouses with the help of extra transform load (ETL)
- Conversion of erroneous data into a useable form for data analysis
- Writing queries on data
- Building APIs for data consumption
- Integrating external or new datasets into existing data pipelines
- Maintenance of the data design and architecture
- Ensures data accuracy and flexibility
- Develop, test & maintain architectures
Skill Sets
- Data Warehousing & ETL
- In-depth knowledge of SQL / database
- Data architecture & pipelining
- Advanced programming knowledge
- Hadoop-based Analytics
Data Analyst
Data analytics refers to the process of extracting information from a set of data. A person who performs this type of analysis is known as a data analyst. A data analyst extracts data using a variety of methods, including data cleansing, data conversion, and data modeling. They analyze mined data and visualize the data to interpret and present the findings of data analysis. A data analyst should be well versed in visualization techniques and they need to have good presentation and storytelling skills.
Roles and Responsibilities
- Using database query languages to retrieve and manipulate information.
- Perform data filtering, cleaning, and early-stage transformation.
- Using descriptive statistics to get a big-picture view of their data
- Analyzing interesting trends found in the data
- Creating visualizations and dashboards to help the company interpret and make decisions with the data
- Presenting the results of technical analysis to business clients or internal teams
Skill Sets
- Possession of problem-solving attitude
- Should have a strong suite of analytical skills
- Scripting & Statistical skills
- SQL / database knowledge
- Reporting & data visualization
- Spread-Sheet knowledge
- Proficient in the communication of results to the team
Data Scientist
Data Scientist is the one who analyses and interprets complex digital data. They analyze data for actionable insights and build machine learning or deep learning models that train on past data to create predictive models. The person should have deep expertise in machine learning, statistics, and data handling.
Roles and Responsibilities
- Carry out data analytics and optimization using machine learning & deep learning
- Increasing the performance and accuracy of machine learning algorithms through fine-tuning and further performance optimization
- Understanding the requirements of the company and formulating questions that need to be addressed
- Involved in strategic planning for data analytics
- Integrate data & perform ad-hoc analysis
- Fill in the gap between the stakeholders and customer
Skill Sets
- Statistical & Analytical skills
- Machine Learning & Deep learning principles
- In-depth programming knowledge (SAS/R/ Python coding)
- Must be familiar with Big Data tools
- Data optimization
- Decision making and soft skills
Summary
So, we have explored three data-driven careers in this article and went through the various roles and responsibilities and the required skill sets of these fields. I hope this article gave you the knowledge to understand the roles and help you to choose the best career path that suits you.
So, what do you have to lose? Start improving yourself and find a good job.
“Choose a job you love, and you will never have to work a day in your life” - Confucius
