Every time you try to apply to a new Data Science job you must have noticed the amount of experience they ask for, the main reason is the number of skills this job requires is vast and vital. Doing some Data Science courses or MOOC won’t teach you all the required skills.
So let’s check out the few skills data science courses don’t teach you.!
Storytelling along with presentation skills
You might be an expert in your data science skills but not all the people or the stakeholders you work with are going to understand the data like the way you do.
So showcasing your knowledge in just technical terms or presenting slides won’t be a good choice.
You need to understand the importance of storytelling. Every Data has a story hidden behind it.
No business leaders expect a great machine learning model or a lot of coding. All they expect from you is to tell them a story that is fruitful to their business and easy to understand.
No technical jargons or evaluation metrics will give you the required growth until you know how to create a story revolving around Data which no data science courses will teach you.
“Facts might bring you popularity but stories bring wealth“
A good data scientist has great technical skills
But an amazing Data Scientist has good Storytelling Skills
Now the question is how to learn Storytelling Skills?
Learn from Vox. If you haven’t heard about Vox, check their YouTube video by clicking this Link.
They are amazing at explaining complex issues in simple words. It is very important to deliver core messages with great storytelling skills.
Communication skills more than technical skills
The complete process from data collection to deployment of a product cannot be implemented by a data scientist sitting in isolation. They have to work with Project stakeholders and directors to solve their business problems and communicate the solution in a non-technical way.
Data scientists have to ensure how to communicate clearly what kind of data should be fed to the model, and any issues or precautions to take care of while doing the same.
So, it’s essential to be excellent communicators, as the project might fail if the information is interpreted in a wrong manner or is not clearly understood.
Developing communication skills is all about practice. One of the best talked about techniques is the Feynman Technique which is all about teaching whatever you learned to someone in the most simplified manner.
You can create the best data dashboard in the world.
You can build the best Machine Learning model in the world.
But until you can use your results to make business decisions and convince people of your goals, your results would never make an impact.
Write blogs, book reviews, or create youtube videos. Anything that adds to your communication skills can take you far away in your journey as a Data Scientist along with enrolling Data Science Courses.
Software Engineering skills
Until a few years, data science was completely focused on having a background in maths or statistics. A master’s degree or Ph.D. was mostly a requirement.
But now the skills required are more related to programming and software engineering skills like knowing Github, SQL, and Python.
The reason is Business experts have now realized that no matter how sophisticated the model is If you cannot deploy the model then the model is useless.
Also with the increase in auto ml tools theoretical knowledge of machine learning techniques is no longer required for building models. The real challenge is getting these models into production.
Data scientists who can write production-quality code and use and interpret the code well are the most preferred candidates in the present scenario.
So let’s see how we can acquire these skills:
1. Enroll in software engineering courses, reading books, writing complex codes, and participating in hackathons.
2. Now put those skills into practice Build a project which covers end to end model building and deployment.
3. One more great way to learn is to contribute to open-source projects else you could just read documentations of well-known libraries like Scikit-learn
Data Science can be applied in a lot of domains but having domain knowledge of every field is next to impossible. But what is possible is acquiring a skill called business acumen
However, one skill that can be honed is business intuition.
e.g. Someone in the HR department will have complete knowledge about the problems that arise as an HR which are being solved through data science.
Therefore, it is important to have a basic understanding of how a business is run and what are the important decisions to be made to run a successful business.
Now how do you grab this business knowledge?
- Learn a little daily through blogs, articles, or courses about business areas. Learning more about digital marketing skills and SEO based skills can be more beneficial.
- If you are working somewhere, sit with other teams to understand how they work and what problems they are dealing with. If you are not working create your opportunities to learn like creating a website or small e-commerce to learn business processes practically.
There is a major difference between developing models and developing models projects to solve real business issues. Make sure you know the difference and work accordingly.
The skill of Embracing the Data
You nailed a Kaggle project and might feel like you can work with any data but in the real-world data is not as simplified as the Kaggle data. most of the time you‘ll have to deal with messy data and 80% of all your work might just be Data Cleaning.
In the worst cases, you won’t even have data. It will be scattered all around the web and you need to learn skills to acquire it. Data collection is most important in dealing with any kind of data that most data scientists don’t care about and end up with poor models.
It is important to know where to get data based on business requirements and goals. Even if you are successful in getting the data, the real struggle starts when you deal with something called data integrity.
You need to verify overall data by understanding business needs from different stakeholders and make sure that the data is validated and makes sense. In the absence of clean and useful data, all data cleaning, machine learning skills are unimportant.