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11 skills you almost certainly need to become a data scientist
February 14, 2022 | Data science

The first thing you probably did is google search “what skills do I need to become a data scientist” phrase. And what did you get? You were faced with a relatively long list of skills required to become a data scientist, ranging from technical to nontechnical skills – from statistics to programming in Python and R, up to storytelling skills and making presentations. But who has all these skills? The good news is probably nobody. The fact is that two data scientists do have a sharable foundation of knowledge, nevertheless, each of them has their own narrow specialty, which is sometimes so deep that they couldn’t switch their jobs. One data scientist’s job could be close to a job of a statistician’s, while another could be an expert in Python. But, if you want to start with data science, where do you begin? Do you really have to be an expert in Phyton? Should your knowledge of statistical analytics be so deep? The fact is that data scientists have a diverse skill set that is usually not found in a single individual.
In this article, we are going to take a look at nontechnical and technical skills that are required (or at least some of them) to become a data scientist.

1. Nontechnical skills that are required to become a data scientist

Curiosity and lifelong leaning

Curiosity is probably not really a skill but belongs more like to a personality type, nevertheless, it can be learned, at least to an extent you are aware it is one of the most important soft skills of a data scientist and to the point you know you need to ask questions. An aspiring data scientist should be aware she should never stop asking questions like why and how something happened and what could (probably) come up if one of the “variables” along with the line change. If you want to become a data scientist, be aware – lifelong learning never ends. Data science is one of the most fast-growing and developing fields and a skill set that you’ve mastered today may not be needed tomorrow. If you want to become a data scientist, be prepared to ask questions – be curious – and be prepared for lifelong learning.

Communication and storytelling skills

Ah, another one that is hard to pinpoint. Yeah, that’s the deal with nontechnical skills.
As a data scientist, you will not only need to communicate with numbers and machines but also with people. One thing is sure – your employer will tell you his requirement and you will have to understand it and translate it into a problem statement that requires real data in a way that at the end of the day employer’s profit – either through tweaking sales or improving organizational or employee processes – is higher. At the end of the project, you will need to translate the results you’ve come up with into a language your employer will understand, and this is definitely something that requires strong communication and storytelling skills. Therefore, a good data scientist must be (obviously) good with data – you will need to extract data and analyze it. But, in order for your employer to benefit from you, you must be capable to communicate your discovery to people who do not understand the terminology of data science. Yes, for some communication and storytelling skills might be a part of the personality, but others will have to roll their sleeves and learn it.

A strong business awareness and understanding of business strategy

This is definitely a skill that can be learned and can help you pinpoint eventual problems that need to be solved in order for a company to grow. As a matter of fact, a strong business awareness is one of the essential non-technical skills and can help the organization you’re working for build new opportunities. If you’ll understand your company’s business strategy, you’ll be more successful in solving business problems and you’ll be better at conducting analyses.

Recognizing valuable data insight

Recognizing valuable data is not something that is apparent when you’re dealing with a large database. Therefore, a data scientist with appropriate knowledge recognizes data and is intuitive when he needs to look beyond the surface for insightful information. This knowledge is something that comes with experience.

2. Technical skills that are required to become a data scientist

Knowledge in programming languages

You will very probably need to learn at least some basics of programming languages because they will help you work with data sets and help you organize unstructured data. The most common programming languages data scientists learn are Python, JavaScript, R, Perl, Java, C/C++, Julia, Scala, and SQL, with Python being the most required coding language in data science positions.

You will have to dig into statistics and analytics

There are many statistical and analytical tools available and just some of them are SAS, Spark, MATLAB, and Hive. These tools will help you extract information from organized data sets – a priceless skill every data scientist needs. When comparing data science with data analytics, data analytics focuses on viewing the historical data in context, while data science is much more multi-disciplinary and focuses more on machine learning and predictive modeling, while including algorithm development, data inference, and predictive modeling to analytically deal with a complex business problem.

You will have to understand working with unstructured data and cleaning it

According to some sources, more than 50% of any data science professional’s time is spent cleaning and organizing unstructured data. That’s a lot of time, but definitely, the basics if you want to get the results. The most common problems data scientists encounter in the context of cleaning data is dealing with not having a unique identifier, such as a common column, or for example facing different nomenclature, which could be a problem as simple as having issues with a different spelling or having different date formats in tables. A very common problem is having to join data from different file sources, another issue that also appears very often is missing data or dealing with a data architecture that is full of mistakes.

Maybe you’ll learn how to work with artificial intelligence

Maybe you will learn how to manipulate with computers that can control a robot to do tasks that people normally perform because these tasks require human intellect and characteristics, such as the ability to think logically and to generalize or to learn from past experience. Artificial intelligence ranges from performing simple tasks as vacuum cleaner robots do that are practically in almost every home to complex programs that can triumph the best chess players in the world or perform specific tasks such as recognizing voices or carrying out medical diagnoses.

Maybe you’ll work on machine learning

Machine learning belongs to the field of artificial intelligence. Machine learning is a method of data analysis that automates analytical model building. The experts in the field try to achieve machines imitate intelligent human behavior based on the idea that machines (systems) learn from identifying patterns and from data so they can imitate intelligent human behavior and make decisions without humans interfering.

Maybe you’ll get into deep learning

Deep learning is a branch of a machine learning technique. With a deep learning technique scientists teach computers to do what comes naturally to us humans – we learn by example. For example, a computer learns to classify pictures and recognize when a photo contains an animal, let’s say a cat. It has four legs, ears, tail, specific poses, etc. A computer learns what is a cat and what it’s not. A deep learning technique includes statistics and predictive modeling and analytics – it includes collecting, analyzing, and interpreting a large amount of data. Compared to traditional machine learning algorithms, which are linear, deep learning algorithms increase in complexity and abstraction.

You will certainly have to know at least some of the basics of probability and statistics

One thing is sure – probability, estimates, and therefore statistics are the temple stones of data science. You can not make a prediction without the probability theory and the statistical methods are largely dependent on the theory of probability. Therefore, wipe off that dust from the old book of statistics.

You will definitely have to learn to manipulate with data visualization

If you ask data scientists, they will probably answer data visualization is the artsiest and most fun part of data science. Experts of data visualization know how to tell a story with a help of colorful histograms, bar and pie charts, and even advanced charts like waterfall and thermometer charts.

In conclussion

It feels overwhelming, right? From exceeding in statistical modeling and becoming an expert in Python, through communicating effectively and to making great-looking data presentations. You think like you can’t make it – at least not all of it? Don’t worry. One thing is sure – you won’t have to know all of these skills – one job description will describe a data scientist’s role that resembles a role of a statistician, while another employer is looking for someone who has a master’s degree in computer science. Nevertheless, you will have to invest your time and knowledge into building your career and if you want to become a successful data scientist, you will very probably have to master at least a handful of the skills above.