6 productivity tips for beginner data scientists

Learning aim

- The Motivation behind this topic
- The 6 Productivity tips explained
- The basic process of problem-solving in data science
- Action process to avoid procrastination in the data field.
- The Ferryman technique in technical writing
- Conclusion - A short story on tiny changes, and remarkable results.

Transcript

1. Abeokuta Gift Ojeabulu Ex Data Scientist, CBB Analytics Speaker Image Placeholder 6 productivity tips for beginner data scientists
2. Who am I? - Co-founder and community lead for Data Community Africa/DatafestAfrica. - Organizer of MLOps Community Lagos. - Ex Data Scientist at CBB Analytics
3. Learning Objective - The Motivation behind this topic - The 6 Productivity tips explained - Basic process to problem solving in data science - Action process to avoid procrastination in the data field. - The Ferymann technique in technical writing - Conclusion - A short story on tiny changes, remarkable results.
4. The story that inspired this topic
5. 6 productivity tips explained
6. Solve problems don’t stick to tools or programming language
7. Every programming languages/tools have their shortcoming, No programming language is perfect. We should know that if I am trying to work on a project that involves using advanced statistics, R is the best programming language in this case while if I am trying to work with machine learning and deep learning then Python is the best tool for me to use. Therefore, if Python is not giving a suitable solution, I should optimize for a better way to solve problems.
8. Basic process of problem solving in data science • Knowing how to clean your data. • Ensuring data quality. • Telling stories with data. • Making reports and recommendations. • Analyzing and making business impacts
9. Collaborate with others
10. In data science, collaboration plays a significant role in your career progression. Doing data science alone may work for Zindi or Kaggle competitions. but this is not the case in the real world because data science entails a lot of data visualization, Data Cleaning, model deployment, and so on. It is hard to be an expert in every aspect of data science. Collaborating with other fellow data scientists will make you go far.
11. Great things in business are never done by one person; they’re done by a team of people Steve Jobs
12. Fundamentals first
13. Learn to master the basics. Mastering the basics will help you learn advanced concepts in data science quickly, as they built all advanced concepts on the basics. It might be so tempting to skip the basics because of the pressure of things developing so fast. Beginner Data scientists want to skip understanding the basics of statistics, linear regression to complex things like computer vision, neural networks, they want to skip machine learning to deep learning.This is like a baby trying to run quickly without crawling or even walking at all. In the long run, this will affect your journey.
14. Winners don’t just learn the fundamentals, they master them. You have to monitor your fundamentals constantly because the only thing that changes will be your attention to them Micheal Jordan
15. Stop procrastination and imposter syndrome
16. Consistency is key. When you are inconsistent, you prolong the time you need to get the job, as you will also stay outdated, since data science is a developing field. Procrastination is one of the biggest hurdles beginner data scientists face. Staying consistent as a beginner is hard, but you will do yourself well when you are consistent. The more consistent you are learning & improving, the easier for you to get your dream job. Also, Everybody gets imposter syndrome at some point. Developing is challenging, and it’s easy to feel like a fraud when you get stuck on a bug or can’t solve a “simple” issue.
17. Steps to avoid procrastination as a beginner data scientist • Plan your task • Remove triggers • Promise to deliver within a time range publicly
18. Build Side Projects
19. The earlier you build side projects, the better for you. You don’t have to know everything before you build side projects. The world we are today is about what you can do. We know musicians for their works the same as an artist. A hundred courses without a project are like just watching movies. No one gets rewarded for that. Build projects. It is hard to get even an intern job having no project to show recruiters. Don’t just stack certificates of completion with no project to show for it. Start small, build mini-projects, then keep scaling to bigger projects with time.
20. Never ask anyone for their opinion, forecast, or recommendation. Just ask them what they have or don’t have in their portfolio Nassim Nicholas
21. Start Writing
22. Writing enforces you to think deep and dig out clarity from the rabbit hole. When you write, you compel your brain to think intuitively through research and strive to know more. There is always something to contribute to a topic, everyone has their perspective on a topic. This is one crucial reason you should start writing even as a beginner.The trick to good writing is to write for yourself. Write about topics you want to learn and explore. If you want to master something, teach it, as teaching a subject topic is learning twice.
23. The Feyymann technique The technique consists of four steps: • Pick a topic you want to understand and start studying it • Pretend to teach the topic to a classroom — In this case, write about the topics, make in-depth research and share, receive feedback. • Go back to the books, videos, or official documentation when you get stuck. • Simplify and use analogies!
24. If you can’t explain it simply, you don’t understand it well enough Albert Einstein
25. Conclusion
26. A short story on the effect of tiny Changes, Remarkable results
27. Thank you! Gift Ojeabulu GiftOjeabulu_