Updated: Feb 23, 2020
Who is a Data Analyst?
Nowadays, companies receive a tremendous amount of information every day that can be used to optimize their strategies. To get insights from the massive data collected, they need a highly qualified professional: the Data Analyst.
The task of a Data Analyst is to process the varied data concerning the customers, the products, or the performances of the company, to release indicators useful for the decision-makers. Thus, the information provided by the data analyst enables companies to define the products to be offered to customers according to their needs, the marketing strategy to adopt, or the improvements to be made to the production process.
In other words, being a Data Analyst is an extremely important job in the current data age. They process the almost infinite amounts of data and then refine and process it to obtain some actionable insights that are useful for the decision-makers in the company. And the main thing is that these jobs are Very Highly Paid because of an increase in demand and low supply. In fact, there are currently several thousand analytics and data science positions vacant in Canada due to a dearth of qualified candidates.
And that is the reason this article is a complete guide to becoming a Data Analyst in 2019. This is a roadmap that you can follow if you are interested in learning more about Data Analytics.
But there is still a lot of confusion between the differences in the role of a Data Analyst and a Data Scientist so we’ll start our article with that and move on to other topics like the Education Requirements and Skill Requirements to Become a Data Analyst.
What is the Difference Between a Data Analyst and a Data Scientist?
It’s obvious that both a Data Analyst and Data Scientist have a job description related to data. But what?!! That’s a question that many people have regarding the differences between a Data Analyst and Data Scientist. So let’s clear this doubt here!
A Data Analyst uses the data to solve various problems and obtain actionable insights for the company. This is done by using various tools on well-defined data sets to answer corporate questions like “Why is a marketing campaign more effective in certain regions?” or “Why have product sales reduced in the current quarter?” and so on. For this, the basic skills that a Data Analyst possesses are Data Mining, R, SQL, Statistical Analysis, Data Analysis, etc. In fact, many Data Analysts gain the extra skills required and become Data Scientists.
A Data Scientist, on the other hand, can design new processes and algorithms for data modelling, create predictive models and perform custom analysis on the data according to company requirements. So the main difference is that a Data Scientist can utilize heavy coding to designing data modelling processes rather than using the pre-existing ones to obtain answers from the data like a Data Analyst. For this, the basic skills that a Data Scientist possesses are Data Mining, R, SQL, Machine Learning, Statistical Analysis, Data Analysis, etc. So the reason that Data Scientists are more heavily paid than Data Analysts is their high skill levels coupled with high demand and low supply!
Education Requirements to Become a Data Analyst
There are many paths to reach your goal as a Data Analyst and you can follow any of them! You can either complete a Bachelor’s degree in Data Science as that will obviously teach you the skills required to collect, analyze and interpret large amounts of data. You will learn all about statistics, analysis techniques, programming languages, etc. that will only help in your job as a Data Analyst.
Another way is that you can complete any technical degree that will help in your role as a Data Analyst like Computer science, Statistics, Mathematics, Economics, etc. After completing your degree you can do Masters in Business Analytics, Big Data, Data Science, etc. as they all will contribute towards a job in Data Analytics. Or you can even do an internship after Bachelor’s that will provide you with the practical experience you need.
Skill Requirements to Become a Data Analyst
As a Data Analyst, your skills are the tools you need to find the needle in the data haystack! And so there are multiple skills that you need ranging across different fields. Most of them are mentioned below:
1. Python: Programming Skills are a necessary tool in your arsenal as a Data Analyst! They are essential in performing predictive analytics on big data sets in order to draw useful conclusions. In general, Python and R are the most commonly used languages for this purpose. Python is used because of its capacity for statistical analysis and its easy readability but purely statistically, R is a little more popular as it was created specifically for data analytics.
2. Data Analytical Skills: As a Data Analyst, you should obviously have Data Analytical Skills or what’s the point!!! This is the ability to analyze and interpret large amounts of data and produce actionable insights for your company. So you should be familiar with at least the basics of Statistical Analysis and the knowledge of when to use which statistical techniques for a given data problem. Also, there are many analytical tools that are immensely helpful in Statistical Analysis as a Data Analyst. The most popular of these are Tableau, Spark, etc. so it’s important that you have a thorough knowledge of them.
3. SQL: Data plays a big part in the life of a Data Analyst (Obviously!). And so you need to be proficient in SQL as that is one of the primary tools for Data Management which involves Data Extraction, Transformation, and Loading. This means that you have to extract the data from various sources, then transform it in the required format for analysis and finally load it into a data warehouse. A Data Analyst also needs SQL to run queries in order to find relevant trends in the current data and for data processing that might be of use for a Data Scientist.
4. Advanced Microsoft Excel: You might think that Microsoft Excel is just a spreadsheet, but actually its a very important part of Data Analysis. Even though Excel might not be very useful for big data analytics like R or Python, it is a godsend for smaller analytics with tools like VBA methods. Over the years, Excel has remained hugely relevant in Data Analytics, so make sure to learn as much about the many functions available if you want to succeed in your career choice!
5. Communication Skills: You must be great at Communication Skills as well in order to become an expert Data Analyst! That’s because while you understand the data better than anyone else, you need to translate your data findings into quantified insights for a non-technical team to aide in the decision making. This can also involve data storytelling! So you should be able to present your data in a storytelling format with concrete results and values so that other people can understand what you are saying.
If you wish to start a career in Data Analytics / Data Science and need expert guidance for that, please reach out to us here.