Being able to code a working computer program is the new superpower to have in this decade. However, for a Big Data Analyst, it is an essential skill as one needs to perform statistical and numerical analysis on mammoth data sets. While knowledge of Python, R, Java and C++ among others is necessary, the more you know the better you grow is the simple rule to success in this field.
Technologies
Technical knowledge is not limited to programming alone. A data analyst must be well versed with a range of technologies including tools, platforms, hardware and software. Microsoft Excel, R and SQL are some of the basic tools that you ought to know while beginning as a data analyst. While working at enterprise level it is important to learn Scala, Hadoop, Linux, Scala, SAS and HIVE among others. The actual technologies you will work on will depend on the business environment and requirements.
Statistical and Quantitative Analysis
While programming helps you do what you need to do, but quantitative skills will tell what you are actually supposed to do. The various numerical skills that you need to know include matrix algebra and multivariable calculus. Probability and Statistics are other two mathematical concepts that should be very strong if you want a stable foundation in big data. People who have a degree in mathematics or statistics are already at an advantage over others. Even if you do not have such a degree, it is always advised to pursue a short-term certification in SAS, SPSS, MATLAB or Stata.
Machine Learning and Data Mining
Machine Learning lets you design algorithms that can help make accurate predictions based on data. It is one of the most common techniques in use today for data mining. These two terms are often found next to each other as many of their characteristics overlap with each other significantly. Machine learning has come to be recognised as one of the hottest fields in big data. Professionals who can harness machine learning to build predictive analytics apps are in huge demand and will continue to remain so in the near future as well. Knowledge of technologies like Apache Mahout, or Neural Networks can help you tame the beast of Machine Learning and prove a huge asset for you over other candidates since these are harder to learn and more specialised skills.
Data Interpretation
Interpreting huge hordes of data combines hard science with mathematics in addition to creativity, curiosity and ingenuity. A majority of businesses across the globe do not have a clear understanding of their own company’s data. These companies rely on preconfigured dashboards and analytic tools for understanding consumer behaviour which is not only misguiding but also dangerous for business prospects as it doesn’t provide a complete picture. Any data is only as useful as the people analysing and interpreting it. Therefore, without proper data interpretation, one cannot expect to become a big data wizard.
Creativity and Problem Solving
One can pursue as many certifications and learn as many technologies but nothing can ever replace the ability to think your way through tough situations, which you will find in plenty. With creative thinking and problem-solving abilities, you can find solutions to even the most stubborn complications. Being a dynamic field, while technologies and tools will keep on changing in Big Data, if you possess a steely resolve to overcome challenges, a well-paying job will never be too far from you.
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