The ability to read, work and analyse data (data literacy) is only getting more and more important. Whether or not you’re a data analyst or data scientist, you’re definitely going to need data skills in a variety of careers and industries.
If you’re thinking about studying data analysis, but are on the fence, here’s four myths you need to be aware of:
1. ’Data analysts just crunch numbers’
People think data analysis it’s only statistics, just looking at numbers while someone else is making the decisions. But a regular data analyst job requires so much more. If you’re a curious problem solver, even the process of preparing and cleaning data will be exciting!
Plus, data analysis is being used in eeeevery industry at the moment! Whatever industry you’re interested in, they probably need a data analyst, from sports to pharmaceuticals, any start up, video games industry, in marketing, finance, etc.
One of our Data Analysis instructors, Jamie, before he joined CodeClan he worked in very different industries, he started in Scottish Waters, then worked with Manchester City for a while and then spent a couple of years working in insurance.
Employers are now realising the power of harvesting data and using it efficiently.
2. ‘Software Development is fun, Data Analysis is just stats and numbers’
When people are thinking about applying for one of our immersive courses and they’re between Software Development or Data Analysis, we’ve realised that Software Development is more attractive to people because of how creative it is. When people hear ‘Data Analysis’ the first thing that comes to their head is ‘NUMBERS’.
But a data analysis job is much more than that and can be very exciting. On many occasions a data analyst is like a detective! Marketing, Operations, Finance, HR are your standard ‘crime scenes’ depending on from what level you are looking. Things have happened here that need explanations and clarifications to the Leadership.
Data analysis is all about finding out the truth and being able to communicate the information hidden behind data. And there’s definitely a creative side to it, we’re not talking about data dashboards (that’s another blog!), we’re talking about problem solving. Being able to think out of the box and come up with creative solutions will make you a great data analyst.
3. ‘It’s too hard’,’You need a PHD to be data scientist’, ‘You need maths’
While data analysts need to be good with numbers, and a foundational knowledge of Math and Statistics can be helpful, much of data analysis is just following a set of logical steps. As such, people can succeed in this domain without much mathematical knowledge.
Sure, if you are creating new methods, approaches and algorithms, you’ll need to be an expert in both mathematics and statistics. But the truth is, data science is much more about understanding business questions and being able to decide which tools and algorithms you require and use them, rather than the mathematical details of those tools.
Most data scientists are the applied type: they aren’t creating algorithms or statistical tests, they’re using them. And for this all you really need is an understanding of the fundamentals of statistics (some calculus, some linear algebra, basic probability, experimental design, descriptive statistics and inferential statistics). You don’t necessarily need any more complex maths than that.
4. ‘Data Analysts just collect personal information’
Everyday we produce huge amounts of data (roughly 2.5 quintillion bytes) from which we can get valuable insights that can improve the efficiency and competitiveness of any business. But on some occasions organisations can abuse and misuse this information.
And this is precisely one of the biggest criticisms of data analysis, the privacy, security and commercial use of data by companies and governments, (like Facebook’s data breach that leaked the personal information of millions of people.)
But, when put in good use, data analysis can be the key to addressing some of the main social and environmental challenges we’re facing, such as climate change. Data analysis allows us to effectively determine the cause of problems and look for a more effective solution.