The data science industry can now safely claim to be established, having laid the groundwork for the upcoming generations to grow on. the expanse and scope of opportunity is luring many promising data aficionados to contemplate their chances of making it big here. In this post we will discuss the prospects and the kind of profiles that you could end up taking as a data science professional:
Business leaders rely heavily on data that is served to them in order to make insightful decisions. These could include information such as customer feedback, financial analysis, product inventory and likes of the same. The purported post has a considerable “swag” of its own, per se, as the professionals get to meet innumerable top clients and similar co-workers from finance, public relations marketing, so and so forth.
Data science industry, as an avenue has this very sought after position of a business analyst. As the name suggests, such employees are responsible for narrowing down the gap between business and IT. The processes of distribution and productivity could easily be enhanced by technological decisions driven by these people. Defining business cases, communicating information to the top management, running quality testing are all part of an everyday life of such people.
Acquiring large chunks of data, processing them and summarizing them, is what a data analyst is entrusted with. data visualization & munging, make up for a huge chunk of this profile, in the data science industry. among other day to day tasks come optimization, designing and deploying algorithms and recognizing risks.
Typical skills for a data science professional to become a statistician include those of MATLAB, SAS, Python, Stata, Pig, hive, SQL and Peri. On top of these, one should also have hands on knowledge of the concepts related to data visualization, problem solving, machine learning, cloud tools and statistical theories.
The skills required to be mastered for these data science professionals are NoSQL, Data streaming, C++, MySQL, Hadoop, Hive, MongoDB, Java, SAS. SPSS and Matlab. To top it off, one must have data APIs, data modelling and data warehousing solutions on their resumes as well. Data architects look for a linear path to arrive at a solution.
Big data Engineer
The most coveted of positions that data science jobs have to offer. The skills sets that allow for a person to become a big data engineer are SparkML, Mahout, frameworks such as ETL and Flume, messaging systems such as Kafka and RabbitMQ. Further, experience in Cloudera, Hortonworks, MapR and other scripting languages is always appreciated. Such is the competition to land into this seat that people with only 1-3 years of experience handling databases are preferred.