What do data analysts do and study?

In math classes at school, you were constantly working with data: adding, multiplying, dividing in your mind or a column. Probably you’ve used some homework help. Perhaps you also keep a family budget in a notebook or an Excel table and use simple formulas: find the amounts, differences, averages. That is a simple example of data processing, and mostly manually. When there are only a few pieces of data, it is relatively easy to cope with such tasks. Similarly, it is very easy to write an essay with essay help, big data processing may bring some challenges.

Big data is when there is a lot of information: there is no clear boundary, but usually, we are talking about gigabytes, if not terabytes. These arrays can come from many sources at once: online stores and social media, industrial quality management systems, video surveillance systems, and Internet of Things devices.

The data differs in structure, can be sorted out or not. For example, the history of credit card transactions is sorted out by time, and the characteristics of smartphones in stock can be stored without strict order.

The data density can also vary: some systems take measurements every hour, while others take measurements several times a second. Accordingly, the amount of information varies: from a few kilobytes to hundreds of gigabytes.

Working with big data manually is difficult: it is long, expensive, and inefficient. Therefore, automatic processing tools are used to analyze such arrays.

Why does a business need to analyze data?

Imagine that you are running a grocery store. How do I find out what a customer wants? Ask him – and you will hear what products he buys more often, at what time he usually goes shopping.

Unfortunately, a lot of details will remain behind the scenes. For example, it is the analysts who know how full shelves, bad weather, and background music affect purchases.

All this and other data can be collected and analyzed. This will help the supermarket to arrange the goods so that the buyer stays in the shopping place as long as possible and pays attention to the necessary offers, and to revise the work schedule of cashiers to reduce queues at the checkout. By learning more about the interests of its customers, the store will be able to optimize purchases and logistics. As a result, a company will better off, since its revenue will increase.

You can find applications for big data in any field:

  • In factories, a computer vision system monitors workers. The system will notice if someone forgot about the helmet, and remind you of the safety rules.
  • In banks, big data analysis dictates the terms of loans and deposits, identifies hacker attacks and suspicious transactions.
  • Cities are also driven by big data. Smart traffic lights reduce traffic jams, computer vision searches for criminals in the crowd. Analysts are consulted before building a new road or a public services center or changing the bus route.
  • Based on the data, you can build models and test hypotheses. A model is a mathematical description of any situation that helps to predict the future. For example, a demand forecasting model in a retail network will predict how the demand for individual products will change, and help you adjust prices and purchase volumes. The use of mathematical descriptions provides support for decision-making at every step: a specific result of working with data — an accurate forecast of the future.

How does the work of a data analyst differ from that of a data scientist?

In simple situations, you can do without analyzing big data and use banal logic. For example, if you notice that customers with children in the store often buy a certain cookie, then you can simply put baby juice next to it and thereby increase sales.

But in practice, everything is usually much more complicated. For example, how to create the optimal package of services of a mobile operator and determine the price that will be affordable for the subscriber and will bring the maximum benefit to the company?

The analyst can structure and process data on the mobile market, existing packages, and subscriber spending. It will formulate and test hypotheses, find patterns and draw conclusions: it will offer a specific package composition and its price.

More complex tasks, as well as the search for non-obvious patterns in the data, are already handled by another specialist – a data scientist. So, you may not even suspect that the purchases are related to each other.

To solve such problems, machine learning and artificial intelligence are used. Data scientist selects specific methods that allow the system to learn from disparate data, make logical conclusions, and make predictions.

What knowledge and skills do a data analyst need.

First of all, technical (hard skills):

  • Fundamentals of mathematical statistics. Many methods of analysis are based on statistical rules. For correct conclusions, data alone is not enough, you need to use statistics: cut off outliers, correctly calculate the average or median, and check statistical hypotheses.
  • Ability to create algorithms for data analysis. The most common programming language used in this area is Python. It has a simple and logical syntax, and there are many ready-made libraries — so that you don’t have to reinvent anything, but build a program from existing functions and blocks.
  • Understanding the principles of relational (tabular) databases. Arrays of information are most often stored in them. To get information from such sources, you need to know the SQL language and be able to make database queries in it.

But human qualities (soft skills) also matter. They determine how effective you are as a data analyst and whether you will be comfortable working in such a position. Come in handy:

  • The desire to find the roots of problems. If you want to understand the causes of events and phenomena, it will be easier and more interesting to learn and work.
  • Ability to think outside the box. Very strange hypotheses sometimes find confirmation and help companies earn millions.
  • Courage. You can doubt your ideas as much as you want, but it is better to test them on the data.
  • The skill of asking the right questions to get useful information. This is accumulated with experience.

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