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.
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)
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.
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.
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.