Data science has turned out be a buzz word in the industry during recent times. Various companies have recognized the potential of data science to generate actionable insights from structured and unstructured data and thus are demand for data science professionals today. Not only data science professionals are well paid but, their career seems to promising as well. Right from the business operations of banks to e-commerce sites are using data sciences in their day to day operations to improve their performance. There are different career paths you can choose as a data scientist.
Data management professional
It is an IT role similar to the database manager. You need to maintain data and infrastructure which supports it. However, there is no role for data analysis to play here so, there no need for you to learn languages such as Phyton or R. You may need to focus on learning skills like SQL as well as Hadoop-related query languages such as Hive or Pig. You can excel your career by learning key technologies like Apache Hadoop & its ecosystem, Apache Spark & its ecosystem, SQL & relational databases, NoSQL databases.
This is again one good non- analytic career path in the data science. Data engineer stays responsible to the design and implementation of the data infrastructure which is maintained by a data manager. If the data manager is a car mechanic, data engineer is an automotive designer. Both of them are required by the companies for a proper functioning of the data in the organization. Both data manager and data engineers focus on same concepts to excel in their career. However, their work profile needs them to understand these concepts at different levels.
As the name refers business analyst role related to analysis and presentation of the data. The role includes reporting dashboards or anything that is related to business intelligence. You may have to interact big data framework as a part of your job responsibility. The role of the business analyst is primarily about pulling more from the data. The business analyst can be contrasted with two roles namely machine learning researcher/practitioner and the data-oriented professional. Both the job roles stay responsible for eliciting insight from data and require some unique skills to perform the job.
This role perfectly defines the reader about what it means to be a data scientist. The data-oriented professional is primarily related to data. Depending on the role played by the data-oriented professional they have to master different skillset, in fact, this role of the data-oriented professional is a jack of all trades in the data world. The role may include you to work many tasks related to data sciences. You need to have a good domain knowledge to excel in such roles which can be learnt from enrolling the data science courses to kick start your career in the year 2017.