Credit: Data Science Central
You gave everything that you had in your Data Science training. What is next? The hunt for a suitable job, right? Do you know what all the career opportunities you are likely to have once your training is over? If your answer is a no, then this is the article specially for you. Also, if it is yes, then also you can give this article a read as you may learn something new. This article will list out the myriad of career opportunities that you will have once your training in Data Science is over.
A list of Career Opportunities
Here is a list of some opportunities that you can get in the field of Data Science after your training-
1. The Data Scientist
Data Scientist is without a doubt a champion among the most sizzling job profiles that you can have these days in the data science field, and with a yearly typical pay of $118,700, they are among the best specialists in the data science industry. The data analyst can manage the clustered and non-refined data using the latest advancements and frameworks. They have the ability to play out the indispensable analytics and can show the acquired learning to his accomplices in an educational way.
Expertise in R, Scala, Visualization Tools, Statistics
2. The Database Administrator
As a database administrator, you ensure that the database is accessible to each accomplice in the organization, is performing really and that the fundamental prosperity measures are set up to guard the putaway data secured and. You need to have significant expertise in particular developments going from SQL and XML up to undeniably expansive programming languages like Java to settle down with this job profile.
SQL, Python, TensorFlow, AWS, DevOps
3. Big Data Engineer
For the most part, data designing and structure of the assignment and ensures that they line up with Merkle MS Standards and Data Integration Model Assets in the Phase-End QA Checkpoints. Utilize the Merkle Subject Area Load and Common Component Data Integration Jobs. Help the progression of the data coordination work stream WBS in the masterminding time of an assignment. Assemble and utilize any additional reusable sections in the midst of an association. They are required to choose data joining server limit influence as a result of undertaking requirements. Support colossal quantities of the data coordination adventure desire to ensure consistency with Architectural Standards in following the much-needed QA checkpoint.
SQL, Deep Learning, Apache, Hadoop, Hive, Spark
4. Entry-level Infrastructure Research Analyst
As an Infrastructure Research Analyst, one is expected to keep up ordinary contact with the real-estate and infrastructure community especially the like of– banks, financial institutions, legal industry by telephone and email. Keep up and revive a persistent database of system bargains. Research and source new game plans and trades in the regular business. Make articles on the information accumulated after careful dissemination. Prepare data for market and part reports for appropriation. Help the senior analyst with research for pitching the proposals to the clients. Go to industry events whichever are relevant. Associations are always enthused about signing up the candidates with a strong ability to pay special attention to details and the ability to pass on and create a relationship with senior authorities in the infrastructure business.
SQL, Git, DevOps, Data Mining, Statistics
5. The Data Architect
Data is (being assembled) all around the digital landscape and therefore, a consistently expanding number of companies need a professional data architect. Industries like that of banking and FMCG use proficient data architects to consolidate, concentrate, guarantee and keep up their data sources. These architects regularly work with the latest progressions, for instance, Spark and Git ought to be their forte if they have to be the pioneers of their field.
Deep Learning Visualization Tools, Statistics, Spark, Git, Hadoop, Kafka
6. The Data Analyst
Programming Dialects like R, Python, and SQL are a part of the data analysts fundamental learning. Much like the data scientist’s activity, an extensive scope of capacities is in like manner required for the data analysis work, which merges particular and intelligent learning with innovation This profile is routinely looked for by associations, similar to, HP and IBM.
Expertise in R, Python, SQL, Deep Learning Visualization Tools, Statistics
7. The Statistician
The profile of a statistician is many times ignored by or replaced by fancier sounding work titles. This is fairly a pity, given that the statisticians, with their solid foundation in real theories and frameworks, can be seen as the pioneers of the data science field. More often than not, they are the ones who reap the information from the given get data and changes it into significant bits of learning. Statisticians are always rapidly ahead in the new types of progress that are being made in the field of data science and utilize these to benefit their examination.
Deep Learning Visualization Tools, Tableau Tools
8. Machine Learning Data Science Engineer
As a Data Scientist/Machine Learning Expert professional, you will work with a gathering of Data Science specialists who apply diverse cutting-edge AI and quantifiable procedures to certifiable use-cases and give significant bits of information to the business. You will get the opportunity of working with the most perfectly awesome tremendous data examination stages and my enormous proportions of data using distinctive data science tools like R, Python, Spark, TensorFlow, Keras, OpenCV, Hive, etc.
SQL, AWS, DevOps, R, Python, Spark, TensorFlow, Keras, OpenCV, Hive
9. Data Integration Developers
Data Integration Developers/Tech Leads contribute towards the data linking plan and progression of multi-terabyte publicizing courses of action that consolidate and update our client’s data over different sources. An expert data integration engineer enables its clients to almost certainly pass on viable and important information to their customer and prospects over different promoting channels and media. Data Integration Designers can be usually found immersed in the headway and backing of displaying database answers to meet the client’s specific business targets. You will get an opportunity to work with a cross-disciplinary vertical of various divisions of the business verticals.
SQL, Tableau Tools, Data Visualization tools, R, Scala
You must be thrilled after reading about the myriad of opportunities that you can get after successful completion of your Data Science training. These are just the most trending ones, there is a whole lot more to explore. You must have got some idea about the primary skills that these roles demand and a basic average salary that you can expect. Good luck!