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Must have skills for a Data Analyst include:
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 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.
Thanks to rapid advancements in data availability, ingestion and storage, today’s businesses have more opportunities to harness the increasing amount of data. This data is mostly messy and not profitable, however, there are many ways for companies to benefit from this data. The amount of data that is readily available to businesses has led to an increase in the demand for people who can efficiently deal with it.
Enter the Data Analyst. In short, the role consists of sifting data to find meaningful trends and patterns. Then presenting the findings to the decision-makers with 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.
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 have an impact.
Analysis can form an individuals role where they are silo'd, 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 diving deeper into the data in front of you. It’s the role of a Data Analyst to uncover and synthesize connections that are not always so clear, not take the data at face value and read between the lines.
Strong knowledge of programming is necessary when analysing data. In many cases, Excel can't cope with such 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 visualisations of data 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 language. It has grown 18.7% in the last five years and has grown by 4.6% in the last year alone. With a popularity share of 29.21%, Python beats its closest competitor, Java, by 9.31%.
Not only can it increase your productivity, but it can also have a positive impact on your income. The average salary in the UK for jobs that require Python as a skill is £57,075, compared to £37,504 for jobs using Excel. This means that by learning Python, you can expect to earn a higher salary on average.
At the heart of data analysis lie mathematics and statistics. Strong quantitative skills are therefore an essential part of a data analysts toolkit. Of course, the level of understanding may differ based on job requirements. As 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 these skills will allow you to clean and tidy your data for analysis. 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 visualization software is considered an industry-standard analytics tool, and it’s refreshingly user-friendly. Being able to tell a compelling story can help you earn an average salary of £53,575, for jobs that require Data Visualisation. 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 34 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.
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.”
The majority of 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 salary in the UK for jobs that require SQL as a skill is £50,278.
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 analyzing 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.
Some examples of the jobs you could land as a Data Analyst, with the skills that have been talked about throughout. (Jobs live at the time of writing are now expired, but show the requirements. Information shown is condensed version).
Working with data and helping organisations improve their decision-making process 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 through our Level 4 Data Analyst Apprenticeship. You'll learn the fundamentals of Python programming, through to advanced data analysis. The apprenticeship is done alongside your full-time job and is 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.
Whether you're looking to upskill to access promotions, reskill to remain relevant in your field, or transition to a Data Science career, our Applied Data Science Bootcamp (London) can help you achieve your career objectives.
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