Build Your Career in Big Data

Data Science

Data Science involves the application of advanced data mining techniques, statistics, 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, and 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, and 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.

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 daily.

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Swanintelligence

Data Analytics Expert at Swan Intelligence

Dr. Alexander Turing is a visionary in the field of AI and big data analytics. With a Ph.D. in Computer Science and over a decade of experience in technology research, Dr. Turing brings a wealth of knowledge and insight into the evolving world of business intelligence. His passion for uncovering hidden patterns and driving innovation makes him a leading voice in the industry. At Swan Intelligence, he explores the intersection of AI, big data, and business strategy, offering readers a glimpse into the future of technology-driven decision-making.

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