The Skills You Need to Become a Data Analyst
Scroll through any job listing for a Data Analyst role and you'll come across some combination of the following list of critical, must-have skills:
- A detailed understanding of Python programming
- Intermediate mathematical understanding
- Knowledge of Structured Query Language (SQL)
- Knowledge and experience of Pandas and time series
- An ability to analyse and interpret data
- Critical thinking skills
- Strong communication skills
- Business acumen
- ...and last but not least- a creative flair.
These skills are ones that are mostly necessary to get a job as a Data Analyst but you'll constantly develop with experience. Don't worry if you don't understand all of them as in this article we'll go through each one, and why they're applicable to the role, as well as showing some examples of real job posts that require these skills.
Enter the Data Analyst
Thanks to rapid advancements in data collection and storage methods, today’s businesses have a wealth of potentially useful information at their fingertips. A lot of this data can be messy and unprofitable, however, there are many ways for companies to benefit from this data. The large amount of data that is readily available to businesses has led to an increase in the demand for people who can efficiently extract value from it.
Enter the Data Analyst. In short, the role consists of sifting and sorting data to find meaningful trends and patterns. The findings are then presented to the decision-makers with recommended actionable insights. What skills do you need to be a Data Analyst? This overview will cover some of the most important skills that a Data Analyst needs.
The skills have been split into soft and hard skills. Soft skills are a combination of people skills, social skills, communication skills, character or personality traits, attitudes, career attributes, social intelligence and emotional intelligence. Hard skills include the specific knowledge and abilities required for success in a job.
The soft skills a Data Analyst needs to succeed
As with any job, there are soft skills which are important to be an effective Data Analyst.
The title of 'Data Analyst' brings with it power and decision-making responsibility. You must be able to facilitate meetings, make the right requests and be an active listener in order to understand new information. You’ll need to effectively get your messages across to decision-making colleagues in order to make an impact.
Analysis can form an individual's role where they are siloed, focusing on a particular area of a business or offerings. In larger organisations, an analyst may work as part of a team, alongside the likes of developers, data scientists and data engineers, working together towards the same goals and outcomes.
Having a creative flair is important in being able to represent data in a visually stimulating way for non-technical people, as it’s important for clarity and effectiveness across the business. This is linked to both data visualisation and reporting.
Business Acumen, or business savvy, is a keenness and quickness in understanding and dealing with a "business situation" (risks and opportunities) in a manner that is likely to lead to a good outcome. By improving your understanding of the company’s data, you'll be able to to identify early warning signs, and seek out the right people to answer questions and share information with.
An effective Data Analyst will ask the questions: what is the business strategy, what is its position in the market, and how does it differentiate itself from its competitors? Business acumen has emerged as a vehicle for improving financial performance and leadership development. The commercial value of data analysis is that by leveraging the insights gained from past trends, a business can more accurately implement new strategies for the future.
Critical thinking is the ability to analyse information objectively and make a reasoned judgment. It involves going (and thinking) above and beyond that task at hand. When you ask yourself questions like ‘what does this mean?’ and ‘what impact could this have on process x?’ you start going off the beaten track and dive deeper into the data in front of you. It’s the role of a Data Analyst to uncover and synthesise connections that are not always so clear, not take the data at face value and read between the lines.
The hard skills you'll need to future-proof your career as a Data Analyst
Strong knowledge of programming is necessary when analysing data. In many cases, the likes of Excel can't cope with the large amounts of data that businesses have available to them. This is why programming in Python is an important skill for a Data Analyst.
Python has become an increasingly more important skill to have because of its better data analysis capabilities over Excel.
Python is useful for automating repetitive tasks and creating data visualisations and it can go beyond what Excel or SQL (Structured Query Language, explained later on) can do. Programming skills will only become more important for Data Analysts in years to come, as companies face the challenge of extracting more and more sophisticated insights from ever-larger amounts of data.
If you want to future-proof yourself and ensure you have the skills required in the industry, you cannot afford not to learn to Python.
According to the ‘Popularity of Programming Language’ index, Python is the world’s most popular computing language. It has grown 11.4% in the last five years. With a popularity share of 28%, Python beats its closest competitor, Java, by 10%.
Not only can learning Python increase your productivity, it can also grow your personal income. According to IT jobs website CWJobs, the average salary in the UK for jobs requiring Python skills is £67,500 compared to just £37,500 for jobs requiring Excel skills. Aside from growing your salary by learning Python, it’s also a great way to future-proof your career by keeping your skillset up-to-date and relevant.
Mathematic and statistical ability
At the heart of data analysis lie mathematics and statistics. Strong quantitative skills are therefore an essential part of a data analyst's toolkit. Of course, the level of understanding may differ based on job requirements.
At a minimum, professionals should have a basic understanding of statistics and maths (GCSE Grade C and above). A theoretical understanding is not enough, to be a data analyst you will be required to apply this knowledge to business situations.
Real-world business data is often incomplete, so data visualisation skills will allow you to present your data in a way that makes it a lot easier to analyse. Data Visualisation is all about communicating your findings to a wider audience, which is an important part of being a Data Analyst. The better you’re able to convey your points visually, the better you can leverage that information.
Analysts use eye-catching, high-quality charts and graphs to present their findings clearly and concisely. Tableau’s visualisation software is considered an industry-standard analytics tool, and it’s refreshingly user-friendly. Equally, you can create good interactive visualisations with Python, using libraries like Seaborn and Bokeh.
Tableau is an interactive data visualisation software platform that has been used predominantly in Business Intelligence and Analytics software. The benefit of Tableau is that it can automate the majority of data wrangling, data cleansing, importing and exporting, and visualisation. Tableau enables an analyst to create exciting visuals, making collaboration with other departments more effective.
Microsoft Excel has been going strong for over 35 years. Its skills are still in high-demand, the seasoned spreadsheet is still used a lot in the financial sector to organise and present data. Excel is also used by roughly 800 million people, which means that chances are someone in your business will be using it. Therefore, you need to know how to interact with Excel.
However, Excel has its place for low volume data analysis and basic visualisation. The software is prone to being slow when working with larger volumes of data, which in the modern era is a given. If you're after simple calculations then Excel is perfect, but any serious data analysis will require Python or similarly powerful tools.
Structured Query Language (SQL)
SQL has been referred to as the ‘graduated’ version of excel. It’s an industry standard for Data Analysts and one of the top skills you need to know. In a recent interview with Alex Zhivotov, Data Analyst at TransferWise, he spoke about the importance of learning SQL;
Every Data Analyst needs to learn SQL, if you imagine most of the world’s data sits in relational databases and SQL is the key to extracting that data, it’s pretty important to have an understanding of it.”
Many companies store their datasets in SQL databases which means that knowledge of SQL is virtually a universal requirement if you wish to work as a Data Analyst. Learning SQL is one of the first steps in acquiring a job as a Data Analyst and will allow you to customise your queries and pull detailed data from relational databases.
Bootcamp Alumni, Andras Rabai, Associate at Goldman Sachs, mentioned that he uses SQL daily in his role as a Financial Analyst and would consider it an important skill for anyone working with data. Learning SQL can also boost your salary expectations. The average base level in the UK for data analyst jobs that require SQL as a skill is £30,244.
Time Series & Pandas
A lot of business data is based on time, such as financial prices, weather and home energy usage to name a few. Python allows you to do things with time-stamped data a lot more easily, aggregating things by month or day. Time-series analysis helps a Data Analyst understand what the underlying forces are leading to a particular trend in the time series data points. It helps in forecasting and monitoring the data points by fitting appropriate models to it.
Pandas is a software library written for Python for data manipulation and analysis. Pandas is a game-changer when it comes to analysing data with Python. It's one of the most preferred and widely used tools in data wrangling (cleaning), if not the most used one. Further Pandas looks at developing your skills in advanced data analysis, grouping, aggregating data in advanced pivot tables, for example.
Types of jobs that require these skills
Some examples of the jobs you could land as a Data Analyst, with the skills that have been talked about throughout this article are as follows:* (Jobs live at the time of writing are now expired, but show the requirements. Information shown is condensed version).BBC Data Analyst
- Deliver analysis and insights on BBC content
- Build reports for stakeholders
- Present reports to stakeholders
- SQL & Python skills desired
- An understanding of data analytics, measurement and media
- Estimated Salary of £31,500/yr
- Work as a product analytics consultant to the ITV Hub cross-functional development teams, providing them with insights about how users are interacting with the product, and ultimately influencing their decision making via data.
- Experienced in data and analytics.
- Advanced SQL and the ability to efficiently analyse large amounts of data, with experience of Tableau.
- Experience with Python - statistical analysis and inference, data cleansing, probability, linear regression, trend analysis, data visualisation, clustering and segmentation
- Good verbal and written communication skills, able to explain analysis clearly and concisely.
- Strong client-facing skills and stakeholder engagement skills.
TransferWise Data Platform Analyst
- Build data transformations, own data quality and controls.
- Support the rest of the team
- You have experience working with statistical software (e.g. R, Python, MATLAB) and database languages/tools (e.g. SQL, Tableau).
- You’re a good communicator - skills and ability to articulate complex and technical concepts to non-technical audiences.
- Estimated Salary of £45,700
*Jobs live at the time of writing are now expired, but show the requirements. Information shown is condensed version.
Looking to kick-start your Data Analyst/Data Scientist career?
Working with data and helping organisations improve their decision-making processes is an exciting and rewarding career with plenty of opportunities. So, how do you get the knowledge and skills necessary to break into Data Analysis?
Find out how you could improve your data analytic capabilities with Cambridge Spark. We offer a range of apprenticeships and training courses to help you upskill in data analytics including our Level 4 Data Analyst apprenticeship, our Data Analysis Foundations Certificate or our more advanced Level 7 AI and Data Science apprenticeship. These programmes teach the fundamentals of Python programming, through to advanced data analysis. Apprenticeship courses are done alongside your full-time job and are fully paid (or 95% subsidised) by the UK government.
To find out more about the benefits of apprenticeships and the opportunities for people looking to upskill into Data Science roles, click here. Alternatively, you can contact us below.
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