Today, data has become an integral part of our lives. Companies use the data produced every day for optimizing their strategies. And to help them make sense of the data and get insights, they need a Data Analyst.
Data analyst is responsible for processing the data related to the products, customers, and performance of the company. Once this is done, they are able to release the indicators used by decision-makers. It is the information provided by the data analysts that allow the companies to define their strategies and products to meet the needs of the customers.
You can say that Data Analyst is one of the most important jobs in the data age. It’s their job to process data, refine it, and then obtain actionable insights that can be used for decision-making. And another popular aspect behind the popularity of this job is the high income that comes along with it. The reason behind this is increased demand and low supply. Because of this, we have created this guide to becoming a Data Analyst in 2020. To begin with, we should first understand the difference between a Data Analyst and a Data Scientist as there is a lot of confusion between the two roles.
Difference between a Data Analyst and a Data Scientist
Both of these roles have a data-related job. What exactly is that job?
As a Data Analyst, you will be using the data to solve problems and get actionable insights for the company. To do this, you will apply different tools on well-defined datasets for answering questions like “Why did the sales reduce in the last quarter?” or “Why has a certain marketing strategy been effective in certain areas?” and so on. To answer these questions, you need to have basic skills like Statistical Analysis, R, SQL, Data Analysis, Data Mining, etc.
The job of a Data Scientist, on the other hand, involves designing new algorithms and processes for data modeling, creating predictive models, and performing custom data analysis as per the requirements of the company. To be a Data Scientist, you need to have all the basic skills of a Data Analyst along with others like Machine Learning, Deep Learning, AI programming, etc.
The primary difference between the two roles is that a Data Scientist uses heavy coding to design modeling processes unlike a Data Analyst, who uses pre-existing processes for obtaining insights from data.
Skills needed to become a Data Analyst
A Data Analyst must have the skills of finding a needle in a large volume of data haystack. Here are some of the skills that you will need to do the same:
As a Data Analyst, you must have programming skills. You will use it to perform predictive analysis on big data sets so that you can draw useful insights. The most common languages used for this are Python and R. Python is used for data analytics because of its easy readability and capacity for statistical analysis. R is more popular because it was created for data analytics specifically.
- Data Analytical Skills
Now, this one is obvious. What is the point of being a Data Analyst if you don’t have Data Analytical Skills? But what exactly are Data Analytical Skills? It is the ability to analyze and interpret large volumes of data and produce actionable insights for the organization. To do this, you will need the basics of Statistical Analysis. There are many analytical tools like Spark and Tableau that can help in Statistical Analysis. So, you should also have a deep understanding of them.
Since data plays a big part in Data Analysis, so does Data Management. For this, you must be proficient in SQL, the most common tool used for Data Management involving Extracting, Transforming, and Loading. It will be your job to extract data from different sources, transform it in the required format so that it can be analyzed, and load it into the data warehouse. Apart from this, SQL is also used for running queries that help in finding relevant trends in the data.
- Advanced Microsoft Excel
Microsoft Excel is more than a simple spreadsheet. It is an important Data Analysis tool. Now, you can’t use Excel for big data analytics like Python or R. But, it is the perfect tool for smaller analytics with other tools like VBA methods. It is important that you learn about the functions available in Excel. It has remained relevant in Data Analytics for several years and is essential for a successful Data Analysis career.
- Communication skills
Communication skills are also a must to become a Data Analyst. The reason behind this is that you will be the one who understands the data better than everyone. So, it will be your responsibility to translate the data findings into quantified insights that a non-technical team can use for decision-making. Consider this as data storytelling. You will be presenting the data in the format of a story with values and concrete results so that other people can understand it.
Career Paths for becoming a Data Analyst
There is no single, fixed path for becoming a Data Analyst. There are several paths for reaching your goal and you can follow any of them. The most basic and common path is completing a Bachelor’s degree in Data Science. The course will teach you all the skills needed for collecting, analyzing, and interpreting large volumes of data. You will also learn about programming languages, analysis techniques, and statistics, etc. as they help you in your job.
Another option that you have is to obtain any technical degree in any subject related to Data Analysis like Computer Science, Mathematics, Statistics, Economics, etc. After this, you can go for a Master’s degree in Data Science, Big data, Business Analytics, etc. All of these streams will aid you in your Data Analytics job. You can also do an internship in Data Science after your Bachelor’s degree for getting the practical experience you need. It is a great way for learning on the job and making connections. Some candidates go for a Data Analytics certification course. A benefit of going through this path is that you will be able to learn about the field while working a full-time job. No matter which path you choose, as long as you are motivated, you will be able to become an expert Data Analyst.