The top 10 skills you need to become a Data Analyst in 2022

The top 10 skills you need to become a Data Analyst in 2022

The demand for Data Analysts has increased by 82% over the past year, this is due to organisations finding value in data analysis since it enables them to pinpoint target markets, and possible flaws, helping with strategic business decisions.

A Data Analyst’s job involves collecting, cleaning, and processing data. They need to be able to spot trends and patterns to then create reports that will help optimise a company’s processes.

Data analysis is a good entry-level job that enables you to learn more technical skills including machine learning, artificial intelligence, and algorithms. These are the type of skills that will help you progress in your career, to later become a Data Scientist or a Data Engineer.

As the role becomes more in demand, it’s important to know what sort of skills employers look for in a Data Analyst. We’ve put together some of the most important skills that will help make you more employable.

1. Programming Languages – SQL, Python, and R.

In order to process data, Analysts need to be able to understand programming languages. One of the main programming languages used is Python, as it is a multi-purpose object-orientated language that is easy to read. SQL is also among the most common languages as it is used for extracting and organising data that is stored in a relational database. Another programming language is R, it is an open-source language used in statistical analysis and can be used to identify patterns. These are just a few examples of some of the top programming languages used.

2. Analytical Skills

It is obvious that one of the most important skills is analytical skills. This means the ability to examine data in close detail in order to understand it. You use analytical skills to find patterns and trends, which will help you solve problems and provide business solutions.

3. Business Acumen

To have business acumen means you understand business goals and strategies needed to be put in place in order to be successful. It is important as a Data Analyst to understand these aspects as it will help you know what to look for when analysing the data and gathering insights.

4. Data Visualisation Tools - Power BI/Tableau

Data visualisation tools offer an easy approach to observing and analysing trends, and patterns in data by using visual components like charts, graphs, and maps. Improving your data visualisation skills often means learning about the tools available including Tableau and Power BI which offer centralised data reporting systems.

5. Communication Skills

Communication skills are important for Data Analysts as they need to be able to convey their findings to various teams. They need to be able to explain their insights to audiences who may not understand the technical terminology. They need to have strong communication both verbally and non-verbally.

6. Mathematical Skills

By no means do you need a mathematical degree to become a Data Analyst, however it does help to understand statistics, probability, and basic mathematical formulas to help with data prediction. Strong mathematical skills will also help when it comes to suing applications such as Microsoft Excel.

7. Microsoft Excel

A lot of tech-savvy people know the basic functions of Microsoft Excel; however, Data Analysts can use the software for much more complex tasks. It also has a number of built-in functions which make data analysis quick and easy to understand, some functions include pivot tables, XLOOKUP, sorting, and filtering.

8. Data Modelling

Data Modelling assesses how data is stored, and how it flows in and out of the database management system. It helps to optimise data retrieval and speeds up analysis, it can also decrease the chances of incorrect results as data is often accurately represented within data models, and therefore it becomes a reliable process.

9. Attention to Detail

As a Data Analyst, you need to be able to spot trends and patterns, and this requires strong attention to detail. You also need to be able to spot inaccurate data or mistakes within data to avoid miscommunication and faulty results.

10. Data Cleansing

When raw data is collected, it is unformatted and therefore needs to be cleansed. This includes removing any data that is irrelevant or corrupt otherwise your results will be incorrect. Data Analysts spend approximately 60% of their time data cleansing, as having clean data will increase overall productivity and allow for the most accurate decision-making.

The most convenient way to be sure you learn all these skills to a high level is through a computer science degree, or a degree in a similar field. However, this is expensive and also takes a few years to complete.

Nowadays you can accelerate your learning by completing relevant courses to receive certifications. Many courses are now online, and can be done in your spare time, therefore they are a great option for those who are wanting to upskill or those considering a career change.