Top Tools and Skills Every Data Scientist Should Know

The data science industry, today, is full of very valuable tools to help you succeed and be productive. The process of choosing which tools are most beneficial for a particular data science project can be daunting. Some organizations may prefer some tools over others, for instance, choosing Python over R. Knowing what tools and languages are in demand is essential to channeling your energy and time into learning them. This is why I hosted a Twitter Spaces Talk with 3 amazing data scientists; Naa Ashiorkor Nortey, Derek K Degbedzui and Jerry Buaba to share their take on some of the In demand tools and skills for data scientists.

Here are some key takeaways and note-worthy points from our discussion:

You do not need a Math, Statistics or Computer Science Degree to get started with Data Science

Anyone can get started in data science regardless of your background. However, a background in Mathematics as well as passion for the field can really be a good push. Regardless, this is just a fragment of the whole data landscape. Someone could decide to focus on learning about data visualization and can eventually become very good when it comes to presenting data.

One recommendation for people who haven’t studied Math or Statistics is, there are tons of free courses that you can take online that span introductory content down to advanced Data Science concepts/topics. While taking these courses, you could choose to go deeper into Machine learning and Mathematics or you may want to focus more on areas that have less of these.

Having the right attitude and drive can help you get started

For anyone that wants to go into data science, be open minded and ready to learn.

Attending data science conferences as much as you can, can help you to get a general overview on what data science is all about as well as specialized views from industry experts. At these conferences, you may also get the opportunity to network with a lot of people with varying expertise and ask as many questions as you may have. Some of these experts may even be open to mentoring you if you ask them.

Python, R and SQL are good starter Tools to learn

If you are just starting out, you can consider the use of Python or R. It is recommended that you pick one of these languages rather than double in both; although you can have a go at both of them and pick the one you would feel most comfortable using. Taking a closer look at the data science industry and considering the quantity and quality of data being collected, you would notice that the most used of these two languages is Python. This means you may get as much support as needed when you get stuck on anything.

Having some SQL background is essential as most organizations depend on databases. As a data scientist, it is advised that you should be able to step into any work environment with the required tools for querying databases in order to assist you with your data related work. Even though you can also call out SQL commands with R or Python, having an understanding of how to go into a database and being able to select what variables to use for your analysis is very important. Therefore SQL is a base skill to have and most job postings for data scientists require it.

Soft Skills are paramount in succeeding as a data scientist

According to the panelists, the most important skills you need for starting out in data science are problem solving and analytical thinking. You constantly need to seek out problems to solve. You need to be able to identify what the problem is and be able to reason about the problem and come up with solutions. For instance, trying to build a model to identify employees in a company that are very likely to leave the company.

Communication is also a very important skill as this is required to work efficiently with team members. You may not always be on the same page regarding methods and approaches to be used, however, you should be able to communicate your opinions effectively to others. A data scientist should be able to communicate with their project lead as well.

Communication is not only limited to technical jargons and it is likely that after building your model or doing an analysis, you would have to present your findings to different stakeholders. It is possible that some of these stakeholders would be technical people who would understand the algorithms you used, how you tested the models amongst others, however, there could be some non-technical stakeholders like a CEO, who may not understand the nitty gritty of the different data sciences techniques used.

A good data scientist should be able to express their work and the insights derived in an easy to understand format when communicating with different kinds of audiences. Communication can be in the form of collaboration in order to track activities and catch up with team members.

Yes there are challenges, but you can overcome them with the right approach

Some challenges raised during the session that one may encounter as data scientist include:

• Despite your computer lacking required computational power to process datasets and train models, there are online tools like Google Colab and Jupyter Notebook which can augment the shortcomings of your computer’s computational power by providing added computational power in the cloud.
• The existence of little or no knowledge in Excel may become a challenge for most beginners in the field of data science. The ability to use Excel is an important skill as a data scientist because most of the data you may end up working with would be stored in formats that Excel can run like CSV files. Therefore learning how to manipulate data in Excel is a great skill to have. Watching tutorials on YouTube or taking a crash course in Excel can help you get started.
• Another challenge that data scientists face is time as a limited resource. Most data science projects would have some strict deadlines which may not allow you to follow every necessary step in the data science project cycle. However, it is important that as much as possible, you follow all the required steps in undertaking a data science project as skipping steps may lead to inaccurate results and conclusions.

You can follow the latest trends in the data science industry but, be prudent.

It is very easy to make a transition into using the latest tools when you are very comfortable in a particular domain or area, however, it is not recommended that you learn every new piece of software that is released. Having a basic knowledge of the trends in the industry can come in handy but you should stick to what works for you and what you are comfortable with. You can explore trends out of curiosity, however, it ultimately comes down to knowing what you’re doing and making sure you’re not just hopping onto just any bandwagon.

It is my hope that sharing these points and notes with you would help you in choosing the right tools and skills that would guarantee success on your data science journey.

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