Developing A Good Attitude Towards Data Science

Be Creative

As Data people, we have to know and accept that there is no one way of doing something. We have to avoid sticking to one particular method of doing something but rather, explore the many many tools, frameworks, libraries and packages available to us for specific use cases. Don’t stay commited to one thing, use what makes your work easier and faster. If there’s currently nothing available for you, create something, reach out to like-minded people in your community and build something together. It may be of help to hundreds or even thousands of people in the future. Do not be scared to start, seek help when the need arises and put your creativity to test.

Be Curious

Learning is a never-ending process and as such, you can never know too much. Curiosity is an essential attitude every Data Scientist, AI or ML engineer needs to develop. There is nothing shameful in not knowing something and knowledge can only be found when sought. Develop the habit of asking questions or seeking clarity every time the need arises. It shows you are highly interested in the topic and proves you are willing to learn more to gain more knowledge. As people who work by making inferences from data, we always have to be sure we know how to get our work done, what we need to do to get our work done and why we do certain things to get our work done. Asking technical questions about your work or project from your colleagues or supervisor will help you so much in understanding the core concepts of what you are working on and guide you towards getting your work done.

Be Consistent

One of the most important things for aspiring Data Scientists, AI or ML engineers is consistency. It is very difficult to be on a learning track for so long especially when thing seem to be on a stand-still. Know that every one has a breakthrough time just as every flower has a time to bloom. Keep learning, stick to a learning plan and don’t go against at all costs. Your breakthough will come but make sure to be ready for it when it comes. Everything you learn as a beginner are the things you will use the most in industry so don’t skip the first steps. Sooner than you expect, you’ll be industry-ready if you keep consistent with learning.

Write Clean And Reusable Code

Most of us in this field write code on daily basis. There might be a few people in this field whose specific job tasks doesn’t include writing code but for the majority of us who spend many hours per day writing code, we need to take up a group of ethics that make us stand out as elegant and good programmers. Code ethics are basically an accepted way of writing code that make our work look neat and organized. There are numerous benefits of adhering to code ethics and the most common ones are that it gives you the feel of being a professional programmer and identifies you an organized person. There are many code ethics but I think the basic ones every programmer need to know are:

• Naming variables: Don’t just name any variable as any alphabet or letter or something. Your variable name should tell anyone who looks at the code what the variable stands for. It could be a short form of a word or 2 or more words. You can use an underscore to separate the shortened words or use a style case know as the camelCase. Here, you join the both words starting the first with a lowercase and any new word with an uppercase. Eg. MyVar1 or firstVariableName.
• Indentation: Indentation is a must-do principle in coding as it structures code in a way that makes it more readable and organized.
  Any code that falls within a block, eg. function, subroutine, loops and if statements must be indented to show its scope in the program.
• Comments: A comment is any text that doesn’t affect the outcome of a code, it is just a piece of text to let someone know what you have done in a program or what is being done in a block of code. Comments are not supposed to beautify the code. They are to serve as guidelines. So stick to simple comments. Don’t do

!!#<<!--comment--!>>

Just be simple with a comment eg:

# This is a comment

Read Wide

As a Data scientist, AI or ML engineer, you are only as good as what you learn. Reading blog posts, articles or papers on topics you find interesting in your field will help you so much in widening your knowledge and learning new things. Read often and keep yourself updated with posts from good writers.

Test Yourself

Landing Data Science, AI/ML jobs most often involves the process of taking a coding test. It can be very overwhelming and difficult especially if you are not prepared for it. The best way to keep yourself prepared for a coding test is to test yourself on daily basis. This not only helps you improve your skills but also makes you fast and time conscious. There are many platforms on the internet where you can take coding tests but the one I am most familiar with is HackerRank . Make it a point to take at least one test a day and you’ll be very good with coding tests as time goes on. Also, you can look out for mock interviews or have a friend or colleague interview you once a while. This helps build your confidence for real interviews and prepares you for some of the likely questions you’ll be asked in an actual interview.

Build A Solid Portfolio

A good portfolio as a Data Scientist, AI or ML engineer should comprise of personal or professional projects you have completed, your accomplishments, professional goals and objectives, skills, interests and educational background. A solid portfolio makes you stand out amongst others and easily makes you desirable by recruiters and employers.

Build A Good Network

Associate yourself with people with similar interests as you and keep in touch often. Reach out to those people to ask for help or to share ideas. Get interractive with some people you look up to and ask them for tips and other questions that can help you on your path to becoming a Data scientist, AI or ML Engineer.

Prepare To Work In A Team

In most companies, Data scientists, data engineers, AI and ML engineers work together as a team and as such, anyone aspiring to enter this indutstry needs to equip him/herself with some team work skills that can help bring progress in the team. Team work skills may be in the from of soft skills and technical skills. Soft skills mainly refer to communication and relation to other people in your team, be it colleagues or supervisors. Technical skills are the core skills applied to work to bring in maximum progress. Learning how to use tools for distributed version control such as Git and Github is very essential for working in a team as it gives everyone in your team the liberty to work together as a team whilst away from each other physically. You can learn about using a distibuted version control system like Git and Github from this article by Anne Bonner .

Use Your Domain Knowledge To Your Advantage

As a Data Scientist, your domain knowledge is your super power. — Derek Degbedzui

Data Scientists will work best if they work in fields of expertise. A Data scientist who studied banking and finance will work best in a financial company and a biological science trained data scientist will equally perform best in a conpany that works in the biology sector. Data science is the fastest growing and most diverse field today, you do not need to have a BSc or MSc in computer science to be able to pursue a data science career. No matter the field you find yourself, you can work as a data scientist if you truly want to. Spread your wings, help out as a data scientist in your domain. Switching domains to one where you have very little knowledge about greatly reduces your value as a data scientist in that field. A data scientist trained to work with financial data is almost useless in a company that needs a data scientist to handle climate or geospatial data. People may argue that ‘every data is data’ but as a data scientist, you have to know that understanding your data is key to making any working progress with the data.