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Data mining has been in the news a lot over the past few years, generally with nefarious overtones: your data is being collected and used for profit, your data is being used illegally…
Truth is, data mining has been going on a long time, even before the mainstream use of computers and social media.
Virtually every country in the world conducts a census of their population; a prime example of data mining. Moreover, these population counts have been going on since the earliest civilisations, usually for tax purposes but also to determine its overall wealth – in landholdings, in agricultural products and for the number of males it can send to war.
Today, we need to mine data more than ever before. Societies are more connected than ever and, thanks to challenges posed by climate change and environmental degradation, it is vital that we accumulate and understand the statistics describing the conditions we live under.
That is why we need people who understand the value of data collection, who can intuit the data that needs to be collected – health, wealth and educational standards among others, and can analyse it to help plot the future of human civilisation.
Data analysis can be quite dramatic and impactful but to think of the field as only projecting possible future outcomes sells the discipline far short of all that it encompasses.
Exploratory data analysis involves taking a data set beyond the models already drawn from it to project other possible scenarios or outcomes. Such an analyst might, for instance, focus his/her studies on a single data point – an outlier, and build a completely different statistical model from it.
By contrast, confirmatory data analysis explains why the data reveals what it does, usually by comparing two data sets and drawing conclusions from those given indicators. If you’ve ever read of a study that compared historical statistics to more recent numbers and presented conclusions, you can rest assured that confirmatory data analysis played a part.
Whether you find the field of data analysis exciting or mind-numbing depends completely on how you approach the subject.
For some people, comparing numbers and plotting graphs ranks about as high on their list of things to do as getting a root canal without anesthetic while others consistently marvel at orderly arrays of facts and figures to be arranged and interpreted as they wish.
The data being analysed also plays a part. If you are interested in the downward trend of bee populations, you probably won’t be thrilled at being tasked to examine how many people buy a certain brand of lipstick.
Most likely, as a data analyst, you won’t always get to analyse data of subjects you find fascinating. Just like any other job, there are pleasant and not-so-pleasant aspects so it would be best to love the discipline as a whole, not just the parts that appeal to you.
Indeed, a substantial portion of data analysis involves maths but, fortunately for us, we live in the Information Age, a time when computers can execute algorithms to determine such factors as correlation and causation, and their respective impacts on the data being analysed.
Often, data is represented as a series of plotted points on an X-Y axis, usually representing a change in the X-variable and its impact on Y. Here again, computers can help to draw the graphs… but in both scenarios, humans are needed to interpret what the computer-generated conclusions reveal.
In taking data analysis courses in college or at university, you should expect maths to feature prominently in your degree course’s syllabus.
Also, don’t be surprised if your programme of study involves multiple computer classes; these days, computer applications such as SQL and Tableau are essential tools for every data analyst.
Unfortunately, students are scared away from engaging subjects like science, technology and maths – collectively known as STEM. Like those subjects, the fields of data analysis and data science are routinely shunned.
‘Bad at maths’ is a common assertion, which begs the question: why not get good at maths?
Today, the most durable and lucrative career fields involve an analytical mind and varying degrees of higher maths capability. Telling yourself you couldn’t possibly succeed in such fields because you may lack those skills is selling yourself and your future opportunities short.
The only logical solution to this dilemma is to cultivate your ability to analyse and think critically, and to improve your skills in maths, preferably with a qualified teacher or tutor.
As so much of data analysis is maths, you may find you would be well-served by a tutor who perhaps plots Cartesian coordinates just for fun and finds beauty in balanced equations. Or you may need a tutor who specialises in computer modelling to get an idea of how those tools of data analysis work.
Whether you need a tutor for higher math or data analysis, Superprof has just the right tutor for you.
Even if you’re still in secondary school or in sixth form, if you have any aspirations of becoming a data analyst in our country’s capital, you surely know that the best place to earn your degree is the London School of Economics.
Their undergraduate and graduate Data Science degree programmes, with their foundation of mathematics, focuses on applying data science to aid and improve society and social programs.
Of course, you don’t have to apply to university to learn about data science; there are plenty of short courses, seminars, webinars and open to anyone who wishes to explore the concept of data analysis.