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9 Must Skills You Need to Become A Data Scientist
The use of big data as an insight generating engine has augmented the demand for data scientist at organizational level, across all industries. Whether it is used to bolster the process of product development, improving customer retention or either leveraging to drive business strategy of the company.
Consequently, as the demand for data scientist’s augments, the profession has a very alluring career path for both students and professionals. This also includes those who are not data scientists but have a desire to pursue their career in this field. Furthermore, acquiring a high school or university degree is imperative to step in this field however, there are certain skills as well which the incumbent should possess to excel in this field, it has been said by many experts that there are 9 top most skills which the incumbent should possess, those are:
A good understanding of statistics is vital for a data scientist. You should be familiar with statistical tests, distributions, likelihood estimators, etc. This will also be the case for machine learning, but one of the more important aspects of your statistics knowledge will be understanding when different techniques are the right approach. Statistics is important in all roles, but especially data-driven companies where stakeholders depend on your help to make decisions to evaluate the experiments.
If you’re working at a company where the product itself is especially data-driven (e.g. Google Maps, Netflix, Uber, Air Bnb), it may be the case that you will have to be familiar with machine learning methods. This can mean things like k-nearest neirandom forests, ensemble methods, and more. It’s true that a lot of these techniques can be implemented using R or Python libraries, but it’s not necessary to become an expert on how the algorithms work. The most important aspect is to understand the broad strokes and really understand when it is appropriate to use different techniques
No matter what type of company or role you’re interviewing for, you’re likely going to be expected to know how to use the tools of the trade. This means a statistical programming language, like R or Python, and a database querying language like SQL.
Often, the data you’re analyzing is going to be messy and difficult to work with which is why it’s important to know how to deal with imperfections in data. Some examples of data imperfections include missing values, inconsistent string formatting and date formatting. This will be most important at small companies where you’re an early data hire, but this skill is important for everyone to have.
If you’re interviewing at a smaller company and are one of the first data science employees, it can be important to have a strong software engineering background or have a data science certification. You’ll be responsible for handling a lot of data logging and potentially the development of data-driven products as well.
Companies want to see that you’re a data-driven problem-solver. At some point during the interview process, you’ll probably be asked about some high level problem—for example, about a test the company may want to run, or a data-driven product it may want to develop. It’s important to think about what things are important, and what things aren’t. How should you as the data scientist communicate with the engineers and product managers? What methods should you use? When do approximations make sense?
Immaculate Communication Skills
A data scientist should understand data better than anyone else. However, for you to be successful in your role, and for your organization to benefit from your services, you should be able to successfully communicate your understanding with someone who is a non-technical user of data. You need to have strong communication skills as a data scientist.
Data scientists are the unicorns of the technology domain, calling them professionals is undermining the work they do as they possess a diverse skill set that is not commonly found in a single individual. From software making, analyzing huge amounts of data, extracting meaningful information which contributes to the overall strategy and business of the company and many more are some of the noteworthy jobs these data scientists do. This is the reason because the incumbents should have the skills to perform these rigorous tasks in their daily routine and this also explains why data scientists are so valued, and why becoming one is so challenging.