5 Things You Need to Become a Data Scientist

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Now that the era of Big Data is finally upon us, there is increased demand for specialists who can put it to good use. After all, companies can only make data-driven decisions if their data can be analyzed, actuated, and presented in a useable format.

As a result, opportunities for talented data scientists are now widely available. In fact, demand for data experts is in danger of surpassing the number of specialists in this area. Due to this, professionals with the right skills and expertise are highly coveted. If you want to advance your career and choose a role that’s future-proof, data science can be a great option. To get started, take a look at these five things you need to become a data scientist:

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1. Programming Knowledge

Data scientists use programming languages to build prototypes and software that facilitates data collection, processing, and analysis. As this is part of a data scientist’s core role, in-depth knowledge of relevant programming languages is essential.

Of course, you don’t have to be proficient in a vast number of programming languages in order to succeed in the industry. Although R is commonly used by data scientists, it is Python that’s the most popular language for professionals in this area.

Not only is Python relatively easy to learn and widely used by IT professionals, it’s also an excellent option when you’re dealing with statistics, math, and scientific functions. Furthermore, it’s capable of processing large amounts of data, which is exactly what data scientists need. If you want to pursue a career in this specialism, in-depth knowledge of Python will certainly be advantageous.

2. Statistics

Statistics essentially uses mathematical formulas and concepts to make sense of data. By applying the appropriate formulas, statisticians can increase the accuracy of data analysis and provide more fine-grained detail. As you might expect, it’s these deep level insights that data owners are looking for, which is why data scientists need to have a good knowledge of statistics. In fact, you’ll find that many trained statisticians are choosing to specialize in data science simply because their skills are in such high demand.

A concept known as statistical features is, perhaps, the most commonly used form of statistics in data science. However, other concepts, such as probability distributions, Bayesian statistics and dimensionality reduction are also widely used. Due to this, anyone who aspires to become a data science should have a good grasp of statistics and be able to identify and apply relevant formulas and concepts.

3. Machine Learning

If you’re already working in the tech industry, there’s no doubt you’ll be familiar with the term, ‘machine learning’. Indeed, the development of machine learning (ML) has been so highly publicized that the term has become somewhat mainstream.

While many people have a vague idea of what machine learning involves, it is data scientists who are truly proficient when it comes to ML. In simple terms, machine learning automates data analysis and applies a variety of solutions to existing data in order to find the one that fits best. By doing so, accurate and actionable insights can be obtained in a short timeframe. Crucially, ML enables vast amounts of data to be analyzed when humans would be unable to perform these activities manually or in instances when it would take humans a significant amount of time to do so.

However, to be used effectively, programs and algorithms need to be created to allow machine learning to occur. When a company owner wants to extract insights from data, for example, a data scientist can create an algorithm that’s designed to collect the exact information that’s required.

As you can see, a successful data scientist will, therefore, need to have intricate knowledge of machine learning and be able to create effective ML tools and algorithms so that they can be used in a meaningful way.

4. Willingness to Learn

If you want to work in data science, you’ll constantly be faced with new problems and evolving technology. In fact, many of the projects you’ll work on will involve finding solutions to problems as they arise for the first time.

For anyone working in tech, a willingness to learn is essential. The industry is evolving at a rapid pace and regular breakthroughs mean that new specialisms are arising. Having a natural sense of curiosity and being a problem solver are certainly good attributes to have if you want to be a data scientist.

5. Professional Expertise

No matter how committed and motivated you are, potential employers will want to see evidence of your professional expertise before hiring you as a data scientist. One of the most effective ways to break into the industry is to complete an online masters in data science, as this will give you the knowledge and experience you need to undertake the role successfully.

In addition to this, gaining real-world experience via internships and secondments can highlight your self-motivation and help you to develop your skillset. While some data scientists will have an undergraduate degree in computer science, others may have majored in mathematics or statistics. However, enrolling in a post-graduate data science program can give you the springboard you need to specialize in the area, regardless of what you studied at undergraduate level.

Launching a Career in Data Science

People have attempted to collect and analyze data for centuries but it’s only relatively recently that we have been able to process vast amounts of data. With this capability comes the potential for significant change. From improving healthcare outcomes around the world to building successful businesses, virtually every sector and industry is beginning to use data science to enhance their operations.

As you launch your career in data science, you’ll have the opportunity to work in any industry of your choice or to work in a variety of sectors on a project-by-project basis. If you’re eager to be at the cutting-edge of IT and to use your skills to bring about real change, a career in data science could be just what you’re looking for.

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