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What is data science, you might be wondering? Well for one, it is **one of the fastest-growing careers on the planet** and this definition could provide clues as to why.

The definition of data includes information about everything around us that is collected and analysed for the purposes of decision making. Today, data is often related to helpful data visualisation, like diagrams and infographics, but it is important to understand the historical development of data.

Learning data science comes with some interesting history. If we go back to 3200 BC, when the ability to write developing in Mesopotamia, scribes would document data from ordinary daily life. Examples of this would be tax and farming information. This would be done to advance their agricultural and accounting systems.

As the natural world and mathematical sciences continued to progress, together with the introduction of improved technology, mathematical measurements changed into something more potent: data science. And this is really the first introduction to statistics.

## An Introduction to Statistics

In a nutshell, learning data science is about combining what we often think of as outdated statistics and computer science so that we can analyse large bodies of data and constantly find new and more effective ways of doing so.

While data analytics utilises mathematical information from a statistics point of view in order to perfect data, data science works mainly as a discipline that is useful for extracting information and drawing new insight from large volumes of data. When it comes to data science for beginners, some of the elementary skills you will need to acquire for properly learning data science are:

- Calculus I & II
- Linear Algebra
- Probability Theory
- Mathematical Statistics
- Computer Science

If you are wondering what is data science or looking for an introduction to statistics, there are many online resources to help you get started. Check out websites like Superprof that offer dozens of tutors who are qualified to teach data science for beginners. Opt for online lessons with a tutor from anywhere in the world or search by location and you could find someone close to you.

## Big Data and Big Data Science Analytics

When trying to find a definition for data science, one needs to first ask what is data science? The first discovery in big data dates back to the Second World War when a group of computers branded as Colossus came onto the scene.

Used between 1943-1945 by British decoders, the ground-breaking framework was used to decrypt codes used by the German Nazi regime. The term big data wasn’t actually coined until about 50 years after this by John Mashey from Silicon Graphics. Today, big data - and the large sets of data that characterise it – still functions in the same way. This means that computer programmes and advanced algorithms are used to analyse immensely large amounts of data in order to find valued patterns and tendencies. This is all essential history for data science for beginners.

Today, part of learning data science requires that the analysis of big data be done by a number of processing software options which is enabled by individuals, corporate organisations and governments. One example is Hadoop, which was released in 2005. This was the original, no-cost, open-source software that both businesses and retailers could make use of in order to accumulate large volumes of data. As a consequence, customers benefitted from better search results.

Some of the most prevalent brands on the internet are using Hadoop today. This includes Facebook, Twitter, Amazon and LinkedIn. While Hadoop is broadly seen as a ground-breaking product in the area of big data and business analytics, there are a variety of different programmes existing today, like Spark, for instance, that are equally as effective. This is some of the interesting socio-economic aspects that form part of an introduction to statistics course.

Something else that is bound to crop up in data analysis for beginners course is the conventional definition of big data which is best expressed by Doug Laney’s 2001 definition, that breaks it down as the “Three V’s”.

**Volume**: the awareness that both organisations and governmental administrations amass big amounts of data from multiple sources, which includes social media and commercial transactions**Variety**: the idea that data is available in a variety of forms, this includes text, email and audio.**Velocity**: the notion that data moves at an extraordinary speed and should be processed efficiently and rapidly.

When it comes to data analysis for beginners, one can also expect to come across one of the most important adaptations to this definition which includes the component of innovation, which of course is the necessary ingredient that is needed to effectively apply the “Three V’s” and make decisions which is ultimately the key result from the processing of big data and something that is covered very early in every data science for beginners course.

## Careers that Result from Learning Data Science

About a decade ago, it was announced by a Harvard Business Review that data science was the “sexiest job of the 21st century.” This declaration was prompted by big data’s cumulative role in both business and government. In 2018, LinkedIn’s Top 5 emerging jobs list included data science specialists. Even at that time, there were still businesses asking, what is data science?

There are many descriptions that describe** the role of a data scientist**, some of these include:

- Collecting, scrubbing and transforming large bodies of data.
- Using computer software in order to accomplish this (e.g. R, SAS, SPSS and Python).
- Finding designs within the big data of organisations and governments with the aim to leverage it profitably.
- Devising better ways of handling big data.
- Automating processes.

The issue of automation is an important consequence of data science. Many businesses employ data scientists to ascertain profitable ways to transform large amounts of customer information into improved business practices. So much so, that some sectors turn to data scientists to determine the future steps of their industry.

**How to Become a Data Scientist?**

Fortunately, whether you just want to find out more about data analysis for beginners or are keen to refine the skills that you already have, there are many resources that help people to become data scientists.

For students who are in the process of career planning and considering a degree or other data science course, there are two obvious questions one should ask:

- Is a career in data science the right choice for you?
- What kind of data analysis for beginners jobs are available out there?

When making a decision about which data science programme is correct for you, it is vital to prudently equate the courses on offer during each step of the programme. Bear in mind that some data science programmes are extremely math-based and can tend to focus more on quantitative theory and applications, while other programmes are geared especially for data-driven business intelligence.

One decision that can benefit you when deciding on which programme will be appropriate is perusing job offers in data science that seem stimulating and pertinent to you and your personal interests. If you notice any recurring skills in the job listings that interest you, make a note to compare them to the degree or course programmes that you are researching. This is an excellent way to make sure that you **select a programme that is going to set you up** for the kinds of jobs you want in the future.

Perhaps you are already a professional who is considering changing careers. If data science is something of interest to you, then make sure that you look at job listings to find out which skills you already have, as well as where there are gaps in your skillset. Once you know this, you can look at ways to master these skills and prepare yourself for a change in careers.

One way to do this would be to find a private tutor on a website like Superprof who could give you specific data analysis for beginners lessons. Alternatively, on Superprof there are hundreds of tutors who can help you refresh your confidence by simply giving you an introduction to statistics. The main thing is that by using a private tutor you can tailor make your lessons to suit you and your career path, without having to revisit your entire first-year university statistics class.

Taking an honest inventory of your skillset and experience, as well as the capabilities required by data scientists today will help you to quickly see where your tuition should be focused.

Some common skills required from data scientists include:

- SQL
- Python
- Hadoop
- Statistics
- Analytical skills
- Data preparation

Fortunately, websites like Superprof offer tutors to address all of these fields. In South Africa, the average fee for statistics tuition is R209/hour and students can usually opt for face-to-face or online lessons.

As an added bonus, the first lesson is usually offered for free which could certainly in establishing whether the tutor is right for you, or not.

For further information on data sets and data science analysis tools, you can check out online resources like Kaggle to inspire you.

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