Modern society runs on data. IBM reports that we generate an incredible two and a half quintillion bytes of data every single day and it seems unlikely that metric will decline anytime soon.
According to a new International Data Corporation (IDC) survey, the global big data and business analytics industry has been rapidly expanding over the last few years, rising from $122 billion in global revenue in 2015 to $189 billion in 2019 and expected to reach $274 billion by 2022.
However, the benefits of a career in data analysis can’t be obtained without considerable preparation and commitment. In order to excel in the field, data analysts must have specialised skills, however, you shouldn’t underestimate the necessity of soft skills for a successful career. To become a successful data analyst you need to develop a broad range of skills.
To begin, it is critical to understand what a data analyst does. They sift through vast amounts of raw data and derive useful knowledge in the process. Data analysts are often in charge of cleaning up corrupted data, assessing data accuracy, and writing reports for their employers.
As you would imagine, these activities necessitate that data analysts have a well-developed toolbox of skills. Here are a couple to consider.
1. Data cleaning
Data cleaning and preparation accounts for 80% of the work for data analysts. This makes it a critical skill for anyone serious about getting a job in data. A data analyst will usually need to retrieve data from one or more databases and prepare the data for numerical and categorical analysis. Data cleaning also entails dealing with incomplete and inconsistent data, which can have an effect on the analysis.
2. Data visualisation
Data visualisation is one of the most powerful techniques in any data analyst’s arsenal. It assists in providing quality information to partners and other team members by converting vast volumes of intangible data into readily understood pictures and graphics.
3. R is an essential language
R is an open source software and it is the language of statisticians and data analysts. Its syntax and structure have been explicitly designed to formulate expressions about statistical objects. Particularly useful for data analysis as it contains a number of built-in easy to use commands for organising data and creating both numerical and graphical summaries of data.
Python is a multi-functional, maximally interpreted language with several advantages. It’s scalable and flexible enough to be applied in different fields and for various purposes. Thanks to the pack of graphical options along with visualisation tools that make data more accessible, Python is named as the most preferred language among the data analysts and is ideal for beginning programmers.
Lots of the biggest names in tech use SQL. Uber, Netflix, Airbnb — the list goes on. Even within companies like Facebook, Google, and Amazon, which have built their own high-performance database systems, data teams use SQL to query data and perform analysis.
For data roles, SQL is the most in-demand skill, listed in over 40% of all data job listings. There’s no doubt that if you’re looking for a role as a data analyst, learning SQL should be at the top of your to-do list.
6. Machine learning
As artificial intelligence becomes one of the largest trends in the industry, an understanding of machine learning has been identified as a key component of an analyst’s toolkit. While not every analyst works with machine learning, the tools and concepts are important to know in order to get ahead in the field. You’ll need to have your statistical programming skills down first to advance in this area as this specialisation is poised for growth.
7. Predictive analytics
Predictive analytics is a category of data analysis aimed at making predictions about future outcomes based on historical data and analysis techniques such as statistical modeling and machine learning. The science of predictive analytics can generate future insights with a significant degree of precision. With the help of sophisticated predictive analytics tools and models, any organisation can now use past and current data to reliably forecast trends and behaviors milliseconds, days, or years into the future. Therefore making it an essential skill for anyone trying to break into a career in data analysis.
8. Microsoft Excel
Whilst programming languages such as Python or R are a necessity for data analysts, one cannot dispute the need for Microsoft Excel in data analysis and having basic Excel skills in your repertoire is always a bonus especially when you need to clarify your data.
9. Critical thinking
Data research necessitates logical thinking skills such as questioning, linking disparate facts, synthesising, and so on. Similarly, the ability to think analytically and use tools to derive knowledge and actionable information from evidence encourages strategic thought. Therefore, as a data analyst critical thinking is an absolute necessity.
10. Communication skills
As a data analyst you don’t just communicate through and with data but also with stakeholders, colleagues, data suppliers, system owners and many others in the process of developing insights for decision-making. For this reason, it is important to hone these skills, so that your findings are shared effectively, in a polished manner.
Working with data and helping organisations improve their decision-making processes is an exciting field that offers many career opportunities.
To make a start on improving your skills, identify some areas for improvement and set yourself realistic goals for working on them. Professional growth and development take time and you won’t reach the top level overnight, but it all starts with the first step. With our data courses, you can hone your skills with virtual learning to enhance your opportunities in the world of data.