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Top Skills Every Data Scientist Needs to Master in 2019 | How to Become a Data Scientist in 2019

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Top Skills Every Data Scientist Needs to Master in 2019 | How to Become a Data Scientist in 2019

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I’m Kaish Ansari, In this video we’ll se the top skills a data scientist needs to master in 2019 and at last he can become a data scientist.
The Job of a Data Scientist is likely to be paid very well. Salaries are as high as $ 100k a year, and there is a high demand for well-qualified data scientist. Many jobs of the 20th century will get disappeared, thanks to Artificial Intelligence, machine learning, and robotics.

Many questions are arising regarding, how to become a data scientist and what are the skills required. Here I’ll help you gain knowledge on how you acquire the skills of a data scientist. The challenge of becoming a Data scientist is that you need to obtain right skills and the profession demands a long list of skills to get hired.
Essential Data Science Skills that need to be mastered:

3.Machine Learning
4.Linear Algebra and Calculus
5.Data Visualization
7.Data Wrangling
8.Software Engineering
9.Data Intuition



Programming skills are required, no matter which role or company you’re interviewing for, you’re probably going to be presumed to know how to use the tools of the trade. This sounds like a database querying languages like SQL and a statistical programing language, like Python and R.


Good knowledge of statistics is vital for a data scientist. You need to have an idea of distributions, statistical tests, maximum likelihood estimators, etc. The Statistics/Math is essential for all company types, but specifically data-driven enterprises where stakeholders will rely on your support to make design and decisions, also evaluate experiments.

[Machine Learning]

If you’re at a massive company with large amounts of data or employed at a company where the data-driven product is present. (e.g., Google Maps, Netflix, Uber), it may be the situation, where you should already be familiar with machine learning methods. This can mean things like ensemble methods, random forests, k- nearest neighbors, etc. It’s a fact that many of these techniques can be executed using Python and R libraries.

[Linear Algebra and Calculus]

Grasping these concepts is crucial for companies where the data define the essence of the product, and algorithm optimization or small enhancements in predictive performance can lead to the success of the company. When you give you interview for a role in data science, your interviewer may ask you some fundamental linear algebra questions or multivariable calculus. Or, you will be asked to derive some statistics or machine learning results you implement elsewhere.

[Data Visualization]

Images often speak more efficiently than either words or numbers, so it enables a data scientist by presenting data in a visually exciting way. This requires you to not only habituate yourself with the principles of visualizing data efficiently but also master data visualization tools.


Data scientist must have the capacity to report technical findings with the end goal that they are comprehensible to non-specialized partners, regardless of whether associates or corner-office execs in the marketing department. Make your data-driven story not merely conceivable but rather convincing, and you could propel your manager to give you a raise.

[Data Wrangling]

Data wrangling is also called as data munging, which is a process of mapping and transforming data from a single raw data form into the different format. Usually, the data you analyze is challenging to work and is going to be messy. Some of the imperfections in data include inconsistent string formatting, missing values, and date formatting. This will be highly crucial at small companies where you’re an early data hire.

[Software Engineering]

If you’re conducting an interview process at a smaller company and are one of the first hires in data science, it is important to have a great software engineering background. You’ll be liable for handling a lot of data logging, and potentially the development of data-driven products.

[Data Intuition]

Companies expect to see that you’re a problem-solver with a data-driven efficiency. At a particular point during the interview process, you’ll likely to be questioned about some high-level problem. For example, about a data-driven product, it may want to develop or the test, which the company may want to run. It’s crucial to consider what things are critical, and what things aren’t.


These are the effective skills that will lead to a successful data science career. It’s a fantastic time to advance in this field, as there will be a need for many data scientists in the near future.




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00:01 hello everyone this is cassia and

00:03 welcome to my youtube channel and in

00:05 this video we are going to talk about

00:06 the top skills every data scientist need

00:09 needs to be mastered because you know in

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