Build Your Career in Big Data | What is the best way to build your career in big data Science analytics?

 Build Your Career in Big Data

 What is the best way to build your career in big data Science analytics?

Data Science 

Data Science involves the application of advanced data mining techniques, statistics, and methods, and algorithms from science and other fields to Big Data. 

The goal is to realize business value from data: more targeted marketing, improved decision making, a better understanding of business trends, faster identification of new business opportunities, more timely response to customer needs, etc.

What Skills are Needed?

What are the key skills needed to be a good data scientist?

Data scientists usually have a Ph.D. in hard science, such as physics, applied math, bioinformatics, computational chemistry/biology.  Some do have master's degrees supplemented by additional exposure to advanced algorithms and data analysis methods. Some may be computer science majors, with a focus on working with large data sets, machine learning, or analytic algorithms.

The key aspect is the application of machine learning, and statistical methods, and experience working with very large, heterogeneous, and "messy" data sets (big data).  So data scientists need to be strong in these skills, including statistical analysis packages and languages such as SAS, R, MATLAB, etc.

They also need to have programming abilities: this often includes Java, Python, and other scripting languages, as well as experience with various data management tools.  Depending on the environment, this might mean traditional data warehouses (relational databases such as Oracle, Sybase, SQL Server; SQL querying, etc) or the newer distributed data platforms (Hadoop, Cassandra, Map Reduce, etc).

A good data scientist has a knack for deriving value from data - finding trends, signals, and relating those to the business. They also need strong communication and teamwork skills:  It is not enough just to be good at working with data technically.  They have to be able to communicate findings and ideas from the data with non-technical members of the team, such as marketing people, product managers, and senior executives. Language skills should also not be a barrier.

Finally, it is critical to be able to be creative with data - this is needed to come up with new ways to analyze the data, and to think of innovative data products and data-based solutions to business needs.

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Working in Data Science

Data scientists often work in small teams with other data scientists, data engineers (folks who have expertise in the "plumbing" aspects of data - the low-level infrastructure needed), as well as product managers, marketing managers, or other business people.

Data mining, like science, is both exploratory and ideas-driven at the same time.  As with experimental and observational science, data scientists explore data to find signals and trends from which business conclusions can be drawn.  But also as in theoretical science, they come up with ideas (theories) and hypotheses and test these using the data.  Just like science, good data science follows the scientific method of incremental learning, testing & proof, to ultimately deliver business value (new product ideas, customer insights, data-driven decisions, etc).

This type of data analytics is not computer science, but it does involve a lot of computing.  Data scientists do coding and use statistical software to analyze and work with large, complex data sets on a daily basis.

More About Data Science

I will be adding more information to this site, so please keep coming back to find answers to the following questions and more:

• What is the difference between a data scientist and a data analyst?
• How do you become a data scientist?
• How can you learn more about data mining and analytics?
• What is the best way to build a data mining team?
• How do you find a job in big data mining?  
• What kinds of jobs are available (Data scientist, Data engineer, Data product manager, Database administrator (DBA), Data Analyst, Statistician)
• Why is data science so popular today?
• What is the market like?
• What are the leading companies?
• Who are the key thought leaders/influencers?
• When did the term "data science" first start being used?

Where can you get a degree in big data analytics?