New York Times considers Data Science as a “hot new field that promises to revolutionize industries from business to government, health care to academia.” However, there are a variety of different jobs and roles under the data science umbrella to choose from. Here is a comprehensive list:
1. Data Analyst:
Data analysts are responsible for a variety of tasks including visualization, munging, and processing of massive amounts of data. They also have to perform queries on the databases from time to time. One of the most important skills of a data analyst is optimization. This is because they have to create and modify algorithms that can be used to cull information from some of the biggest databases without corrupting the data.
How to Become a Data Analyst?
SQL, R, SAS, Python are some of the sought after technologies for data analysis. So, certification in these can easily give a boost to your job applications. You should also have good problem-solving qualities.
2. Data Engineers
Data engineers build and test scalable Big Data ecosystems for the businesses so that the data scientists can run their algorithms on the data systems that are stable and highly optimized. Data engineers also update the existing systems with newer or upgraded versions of the current technologies to improve the efficiency of the databases.
How to Become a Data Engineer?
If you are interested in a career as a data engineer, then technologies that require hands-on experience include Hive, NoSQL, R, Ruby, Java, C++, and Matlab. It would also help if you can work with popular data APIs and ETL tools, etc.
3. Database Administrator
The job profile of a database administrator is pretty much self-explanatory- they are responsible for the proper functioning of all the databases of an enterprise and grant or revoke its services to the employees of the company depending on their requirements. They are also responsible for database backups and recoveries.
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How to Become a Database Administrator?
Some of the essential skills and talents of a database administrator include database backup and recovery, data security, data modelling, and design, etc. If you are good at disaster management, it’s certainly a bonus.
4. Machine Learning Engineer
Machine learning engineers are in high demand today. However, the job profile comes with its challenges. Apart from having in-depth knowledge in some of the most powerful technologies such as SQL, REST APIs, etc. machine learning engineers are also expected to perform A/B testing, build data pipelines, and implement common machine learning algorithms such as classification, clustering, etc.
How to Become a Machine Learning Engineer?
Firstly, you must have a sound knowledge of some of the technologies like Java, Python, JS, etc. Secondly, you should have a strong grasp of statistics and mathematics. Once you have mastered both, it’s a lot easier to crack a job interview.
5. Data Scientist
Data scientists have to understand the challenges of a business and offer the best solutions using data analysis and data processing. For instance, they are expected to perform predictive analysis and run a fine-toothed comb through an “unstructured/disorganized” data to offer actionable insights. They can also do this by identifying trends and patterns that can help the companies in making better decisions.
How to Become a Data Scientist?
To become a data scientist, you have to be an expert in R, MatLab, SQL, Python, and other complementary technologies. It can also help if you have a higher degree in mathematics or computer engineering, etc.
6. Data Architect
A data architect creates the blueprints for data management so that the databases can be easily integrated, centralized, and protected with the best security measures. They also ensure that the data engineers have the best tools and systems to work with.
How to Become a Data Architect?
A career in data architecture requires expertise in data warehousing, data modelling, extraction transformation and loan (ETL), etc. You also must be well versed in Hive, Pig, and Spark, etc.
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A statistician, as the name suggests, has a sound understanding of statistical theories and data organization. Not only do they extract and offer valuable insights from the data clusters, but they also help create new methodologies for the engineers to apply.
How to Become a Statistician?
A statistician has to have a passion for logic. They are also good with a variety of database systems such as SQL, data mining, and the various machine learning technologies.
8. Business Analyst
The role of business analysts is slightly different than other data science jobs. While they do have a good understanding of how data-oriented technologies work and how to handle large volumes of data, they also separate the high-value data from the low-value data. In other words, they identify how the Big Data can be linked to actionable business insights for business growth.
How to Become a Business Analyst?
Business analysts act as a link between the data engineers and the management executives. So, they should have an understanding of business finances and business intelligence, and also the IT technologies like data modelling, data visualization tools, etc.
9. Data and Analytics Manager
A data and analytics manager oversees the data science operations and assigns the duties to their team according to skills and expertise. Their strengths should include technologies like SAS, R, SQL, etc. and of course management.
How to Become a Data and Analytics Manager?
First and foremost- you must have excellent social skills, leadership qualities, and an out-of-box thinking attitude. You should also be good at data science technologies like Python, SAS, R, Java, etc.