Data Analytics

Data Analytics is the process of applying statistical analysis and logical techniques to extract information from data. When carried out carefully and systematically, the results of data analysis can be an invaluable complement to qualitative research in producing actionable insights for decision-making.

If that sounds a lot like data science, you’re right! It’s a closely related field, but there are important differences. Data scientists typically come from computer science and programming backgrounds and rely on coding skills to build algorithms and analytic models to automate the processing of data at scale. Data analysts typically have backgrounds in mathematics and statistics, and frequently apply these analytic techniques to answer specific business problems – for example, a financial analyst at an investment bank.

what skill do I need to become a Data Analytics?

Data analysts don’t do as much coding as data scientists, but it’s still important to know your way around certain programming languages. In particular, SQL (Structured Query Language) is the industry standard for navigating large databases, and statistical programming languages like R or Python are essential for performing advanced analyses on this data.

Data Analytics also relies on more typical business programs. While Microsoft Excel isn’t as powerful as SQL, R, and Python, it can get the job done when working with relatively smaller datasets and maybe the best (and cheapest) tool for the job for early-stage lean startups. Data visualization and presentation skills are also a huge part of the job, which typically requires learning new programs like Tableau as well as mastery of standard business software like Excel and Powerpoint.

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