A Data Scientist- Is this what you want to become? But questions like these bothering you:
How to achieve that?
Where to start from?
Which tools or techniques you need to know / you should learn?
What type of skills data scientist possesses?
Then, congratulations! You have landed on the right page. We are here to help you find an answer to all the questions you have. In this blog, we will help you understand the type of skills data scientists should possess.
This blog will act as your check-list in your journey to become a data scientist.
Nothing could be achieved overnight. So, keep learning. I can’t assure you that this journey is going to be smooth but one thing is for sure the outcome will be worth putting effort.
Let me clear the cloud by segregating the skills one should possess into two categories:
- Technical
- Non-technical
Technical Skills
Programming Skills
Landing into this field requires you to know how to use the tools of the trade. This means you need to gain expertise in a statistical programming language. It can be either R or Python programming language, also a database querying like SQL.
Statistical Skills
To have a good understanding of statistics is indispensable for a data scientist. You should be familiar with the following topics:
- Descriptive data statistics (Mean, Median, Range, Standard Deviation, Variance)
- Exploratory Data Analysis
- Percentiles and Outliners
- Probability Theory
- Bayes Theorem
- Random Variables
- Cumulative Distribution Function
- Skewness
- Other statistical fundamentals
Machine Learning
In a nutshell, machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. It focuses on the development of computer programs that can access data and use it to learn for themselves. You need to have a deep knowledge of machine learning. How it works and where to implement it. Understand different types of Machine learning techniques:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Data Wrangling
In general, all the activity you do on raw data to convert it into an organized form to input to your analytical algorithm is data wrangling/ munging. It is an important part of the data life-cycle.
You can use ‘R’ and ‘Python’ packages for that.
As a data scientist, you should be able to understand what all features are important and what all can be removed in the dataset.
Data Visualisation
This is one of the most important skills a data scientist should possess. Every day huge amounts of data are produced by businesses around the world. This makes data visualization an important part of data life-cycle. The data needs to be translated into a format that will be easy to comprehend. Thus, it will be immensely helpful to be familiar with visualization tools like Matplotlib, ggplot, or d3.js.
Tableau has also become a popular data visualization and dashboarding tool as well.
Non- Technical:
Intellectual Curiosity
Curiosity can be defined as something that keeps pushing you to explore new things and acquire more knowledge at the same time. The field of data science is an evolving moment. You need to stay updated in terms of knowledge, technology, and tools, etc. This is one of the skills that help succeed as a data scientist.
Business Acumen
To lead in your career as a data scientist you need to have a solid understanding of the industry you’re working in and the kind of problems the companies are dealing with. To be able to understand how you can solve the problem and the kind of impact it has on the company just adds to your importance as a data scientist.
Communication Skills
When it comes to communicating, it means describing your findings, or the way techniques work to audiences, both technical and non-technical. When communicating pay special attention to the results and values embedded in the data analyzed by you. Learn to focus on delivering value and building everlasting relationships.
Team Work
A data scientist has to work in teams. A teamwork environment promotes an atmosphere that fosters friendship and loyalty. You will need to know the right approach to address the problems. And how to translate and present your results in front of other people you’re working within a manner that can be easily received by everyone involved.
I hope, this helped you to understand the types of skills a data scientist should possess. And help you with a road map to proceed towards achieving your goal.
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