Data powers today's world. From unleashing innovations to improving decision-making processes, data holds the potential to unlock the success of every industry. The world, as we know, it has been transformed radically by data such that it’s crippling to function without the insights generated from data in any domain.
With the booming influence of data, several data-related job roles and opportunities have mushroomed across the globe. As per the findings of an industry report, Data Science will make up 28% of all digital jobs by 2020. They are highly lucrative owing to the rapid pace of data creation and the emerging need to make sense of it. However, the same report also highlights the huge scarcity of talent in this field.
The main reason for the talent shortage in this field is the lack of clarity regarding the skills required for each role. Companies are looking to hire for niche, specialized skill sets as opposed to a jack-of-all-trades. If you want to avoid being labeled a generalist, you first need to understand the difference between the three leading data roles — Data Scientist, Data Engineer, and Data Analyst.
It’s a common misconception that the roles mentioned above are interchangeable. Throughout this article, we will explore the job descriptions, roles in an organization, required skill sets, and salary expectations of each of these exciting data careers.
Data Scientist, Data Engineer, and Data Analyst - Job Descriptions and Organizational Roles
A Data Scientist employs advanced data techniques such as clustering, neural networks, decision trees, and the like for deriving business insights. In this role, you will be the senior-most in a team and should have deep expertise in machine learning, statistics, and data handling. You will be responsible for developing actionable business insights after they get inputs from Data Analysts and Data Engineers. You should have the skill-set of both data analyst and data engineer. However, in the case of a data scientist, the skill sets need to be more in-depth and exhaustive.
A Data Analyst occupies an entry-level role in a data analytics team. In this role, you need to be adept at translating numeric data into a form that can be understood by everyone in an organization. Moreover, you need to have required proficiency in several areas, including programming languages such as python, tools such as excel, fundamentals of data handling, reporting, and modeling. With enough experience under your belt, you can gradually progress from a data analyst to assume the role of a data engineer and a data scientist.
Data Engineers are the intermediary between data analysts and data scientists. As a data engineer, you will be responsible for the pairing and preparation of data for operational or analytical purposes. A lot of experience in the construction, development, and maintenance of the data architecture will be demanded from you for this role. Usually, in this role, you will get to work on Big Data, compile reports on it, and send it to data scientists for analysis.
Data Scientist, Data Engineer, and Data Analyst - The Skillsets
Coding skills are central to each of these job roles - data scientists need to have mastery over programming languages like Java, Python, SQL, R, SAS, to name a few. Additionally, you need a working knowledge of Big Data frameworks like Hadoop, Spark, and Pig. Understanding the basics of technologies such as Deep learning, Machine learning, and the like also can propel your career in this role.
The role of a Data Engineer requires you to have a deep understanding of programming languages such as Java, SQL, SAS, Python, and the like. You should also be adept at handling frameworks such as Hadoop, MapReduce, Pig, Hive, Apache Spark, NoSQL, and Data Streaming, at naming a few.
Data Scientist, Data Engineer, and Data Analyst - Salary
As a data scientist, you can earn as much as $137,000 a year. Data analysts can expect an average salary of $67,000 per annum, which is remarkable, considering that it is an entry-level role. At the other end of the spectrum, data engineers can command a salary upwards of $116,000 a year. (Source: Glassdoor)
Data Scientist, Data Engineer, and Data Analyst - Your Responsibilities
The responsibilities you have to shoulder as a data scientist includes:
- Manage, mine, and clean unstructured data to prepare it for practical use.
- Develop models that can operate on Big Data
- Understand and interpret Big Data analysis
- Take charge of the data team and help them towards their respective goals
- Deliver results that have an impact on business outcomes
As a data analyst, you will have to assume specific responsibilities, including:
- Collecting information from a database with the help of query
- Enable data processing and summarize results
- Use basic algorithms in their work like logistic regression, linear regression and so on
- Possess and display deep expertise in data munging, data visualization, exploratory data analysis and statistics
Your responsibilities in this role are:
- Data Mining for getting insights from data
- Conversion of erroneous data into a useable form for data analysis
- Writing queries on data
- Maintenance of the data design and architecture
- Develop large data warehouses with the help of extra transform load (ETL)
Data Scientist, Data Engineer, and Data Analyst - Companies That Will Hire You
Data scientists are highly in demand at companies like Facebook, Citibank, Intel, Amazon, Schneider, S&P Global, Moody’s, to name a few. As a data analyst, you can get into entry-level roles at companies like Infosys, 24/7, Oracle, Southwest, Walmart, VISA, Capital One, Credit Suisse, etc. Lastly, a data engineer can get hired from major companies such as Google, Apple, Cognizant, Spotify, Microsoft, AT&T, CISCO, and FLOWCAST, to name a few, as well as product companies like Intel and Amazon.
Data Scientist, Data Engineer, and Data Analyst - The Conclusion
Regardless of which data science career path you choose, may it be Data Scientist, Data Engineer, or Data Analyst, data-roles are highly lucrative and only stand to gain from the impact of emerging technologies like AI and Machine Learning in the future. However, before embarking on a career in this industry, you need to keep in mind that these roles are not interchangeable and call for distinct skill-sets. You need to learn to differentiate between them as the industry is already saturated with generalists and is now struggling with a scarcity of specialists.
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