Frequently Asked Questions about Data Analytics

How can data analytics help business?

Structuring, organizing, analyzing, and reporting data can greatly benefit businesses. Some examples include:

  • Combining disparate data sources to come up with unique insights. For instance, you may have some business data in Excel and other business data in customer relationship databases and e-commerce systems.
  • Data-driven decision making – Companies that analyze the right data at the right times can make decisions on facts rather than hunches or strong opinions. Data can also be an equalizer in situations where the opinions of those with strong personalities win out over more reserved personalities.
  • Customer insights – These days, there is a wide range of quantitative and qualitative data that can be used to gain customer insights. These include website user data, demographic data, buying behavior data, surveys, and many other forms of data. Understanding the structure of all of these types of data and how they can be used to gain accurate insights is an important job for most businesses.
  • Streamlining processes – It is often necessary to gather disparate data in a purposeful way in order to identify bottlenecks and high-cost parts of processes. Doing so can save companies millions in time and money.

Does data analytics require coding ?

Yes – in most cases, data analysts are expected to know some basic coding. This is because analyzing data often requires querying and transforming the data from multiple data sources. Understanding languages like SQL, advanced Excel functions, and Python or R can be extremely helpful for these tasks.

Does data analytics require statistics?

Yes - generally speaking, data analysts are expected to understand the basic concepts of probability, as well as appropriate ways to calculate and present different types of data.

Are data analytics and data science same?

The major difference between data analytics and data science is the scope of analysis. Generally speaking, Data Scientists must be more skilled at statistical models and programming, as they perform sophisticated analyses of data structures. Often Data Scientists have specific domain knowledge (such as Finance or Education). Data Analysts generally perform higher-level analysis – they gather, organize and report on data within a specific scope. Data analysts are not usually as involved in making data predictions as data scientists.