Data science and Analytics professionals are in high demand!!!

I have been discussing the anxious inquiries from my Transformation Team members and their Peer groups on the exciting career Opportunities in Data Science.

Data Science is evolving as a very promising career with growing demand in the Industry and is being leveraged for applications viz. Fraud detection, Network security, Disease control, Climate changes to name a few.

Data science and analytics professionals are in high demand. Therefore, the salary potential will be higher than or comparable to data of other high-demand IT professionals such as cyber security experts. As per the recent compensation benchmarks per payscale.com, Entry level data scientists receive above INR 40 Lakhs per year to Senior data scientists bringing home close to INR 80 Lakhs. The bottom line here is that the salary prospects of data science and analytics professionals are excellent. 

Data science is a highly comprehensive term. It encompasses a multitude of disciplines and concepts. Including big data, machine learning, data mining, and data analytics.

Big data analytics leverages distributed computing technologies and data analytics techniques to overcome computational challenges presented by big data sets. Distributed computing means an approach used in computer science to break down a task into smaller pieces that are easier to process. 

Cloud computing provides a platform on which distributed computing can be implemented with low-cost and scalable methods. To simply put it, cloud computing offers a bunch of computers housed in data centers. In addition to the hardware, a software solution is necessary to manage various aspects of distributed computing. This is why we need software tools such as Hadoop and NoSQL databases. 

Regarding the Skills needed to be successful in data science and analytics careers, it helps to start with some obvious ones, such as data mining, machine learning, natural language processing, statistics, and visualization. Data mining is a broad term referring to the practice of examining a large amount of data for the purpose of finding meaningful patterns and establishing significant relationships to help solve a problem. Machine learning is a subfield of artificial intelligence. It focuses on optimizing ways to use algorithms to conduct data analysis and analytics tasks with as little human supervision as possible. Natural language processing allows a computer to make sense of its interactions with human beings through linguistic means such as spoken and written languages. Statistics is a foundation for data analysis and analytics in general. Visualization is usually the last step of data science and analytics projects. The goal here is to communicate the results of analysis or analytics in the most intuitive way so that stakeholders can quickly get the gist of the meaning and significance of the report. All these skills are essential in becoming a well-rounded data scientist. 

There are a number of opportunities that can be taken advantage of to play an active role and contribute to data science and analytics fields. To name just a few, there are job titles such as Data Scientist, Data Engineer, Business intelligence architect, Machine learning specialist, Data analytics specialist, and Data visualization developer. Each of these roles is critical in effectively leveraging data and its potential despite numerous challenges. 

As the field of data science and analytics matures, we're seeing more certification opportunities becoming available. Just like any professional out there, data scientists can establish more credibility in what they're capable of by earning well-known certifications in their field. Many of the certifications also require significant industry experience. If anyone is a working industry professional who is interested in enhancing their marketability, a more specialized form of certification may be appropriate. These can be directly tied to, software products you are required to use on a daily basis. And include certifications such as Microsoft Azure Data Scientist Associate, Cloudera Data Platform certifications, EMC Data Science Associate, AWS, Google, and SAS certifications. 

If you'd like to advance your knowledge of data science further, here is a progression of courses I recommend you take: Excel, Python, R, Hadoop Fundamentals, Data Visualization Fundamentals, Data Mining, and Big Data Analytics. 

Hope the post is helpful for all aspiring Data Techies.