A big data analyst is a versatile specialist who has knowledge in mathematics, statistics, information science, computer science, business and economics. A Big Data analyst studies large amounts of data containing disparate information, for example, research results, market trends, customer preferences, etc. Research and analysis of such information can lead to new scientific discoveries, increased company performance, new income opportunities, improved customer service etc. The main skill of scientists is to see logical connections in the system of collected information and, on the basis of this, develop certain business solutions and models.
Big Data analysts need to be able to extract the information they need from all sorts of sources, including information flows in real time, and analyze it for further business decision making. It is not only a matter of the amount of information processed, but also of its heterogeneity and speed of updating.
Today, the term is usually used to refer not only to the datasets themselves, but also the tools for processing them and the potential benefits that can be obtained as a result of painstaking analysis. The main characteristics that distinguish Big Data from another kind are three Vs: volume (large volumes), velocity (the need for fast processing), and variety. Hire big data programmer to know more.
There are two main specializations for people who want to work with big data:
- Big Data engineers are more responsible for storing, transforming data and fast access to it;
- Big Data analysts are responsible for analyzing big data, identifying relationships and building models.
The main demand for analysts is formed by IT and telecom companies and large retail chains. Recently, it has been increasingly used in the banking sector, public administration, and agriculture. Engaging a specialist is an opportunity to look at the available data from different angles.
What you need to know and be able to
- Fast learner;
- Critical thinking;
- Analytical mind;
- Attention to details;
- A responsibility;
- Broad outlook;
- Ability to follow through on research despite unsuccessful interim results;
- Ability to explain complex things in simple words;
- Business intuition.
- Thorough knowledge of the industry in which the work takes place;
- Possession of statistical tools SPSS, R, MATLAB, SAS Miner, Tableau;
- Deep knowledge of methods of statistical analysis, construction of mathematical models (neural networks, Bayesian networks, clustering, regression, factorial, variance and correlation analyzes, etc.);
- ETL (Extraction, Transformation, Loading) – extracting from various sources, transforming it for analysis, loading it into an analytical database;
- Ability to set a task for database specialists;
- Fluency in SQL;
- Knowledge of English at the level of reading technical documentation;
- Knowledge of scripting programming languages Python / Ruby / Perl;
- Machine learning skill;
- Ability to work in Hadoop, Google big table.
Any analyst deals with scattered information that needs to be structured correctly, namely, to conduct:
- building a data collection process for the possibility of their subsequent operational processing;
- ensuring the completeness and interconnection from different sources;
- development of solutions to optimize current processes based on the results of the analysis.