Data Science is the science of providing meaningful insights on large amounts of complex data (Big Data). It combines different fields of work in statistics and computation to interpret data for decision-making process. Now we are done with the boring definition, let's talk about the cool stuff. Data Science has a lot of applications that are game changers almost in every industry from medicine to sports. Data Science is an exciting career path and one of the highly paid careers and here is how to become a data scientist in 2022. This is the last part of Data Scientist mini-series and this week I will speak about Data Science competitions provided by Kaggle and other courses helping you to excel in Data Science. I will also talk about the pros and cons of Kaggle competitions and how to overcome it.
Kaggle Pro's:
Kaggle courses and practice competitions:
Kaggle offers multiple courses from basic python to teaching about machine learning, data cleaning and EDA to reinforcement learning and game AI. All courses has a text document that you should read first before hoping into a lesson than after it you will have a chance to practice in a jupyter notebook, run the code written, and see the results. If you get stuck during an exercise , do not worry the jupyter notebook includes hints and even answers to help you with the code so check them out and see what suits you. Click
this link to check out the courses they offer. In addition to Kaggle courses, you will also be introduced to practice competitions. Practice competitions will introduce you to certain topic for example linear regression or GAN. Afterwards, it will give you a starter notebook to enter competition and leave you to start doing research and start coding. They usually do not have a deadline, so you can keep on improving your work. Check
this link to learn more about computer vision using this competition.
Kaggle real-life prized competitions:
Kaggle also offers real-life competition these competitions lies under featured or research competitions and they have a prize money for the top three coders. You need to finish basically top 10% in competitions. Of course, this takes a lot of time and practice but finishing top 10% is a good sign for hard worker data scientist. Competitions such as Covid-19 research, Lyft Self-Driving cars, and Google Landmark recognition to name few. You may find these competition in the link I referred to in the previous paragraph. If you want to check a competition in particular, you can check
OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction from Stanford University.
Kaggle's discussion, datasets, notebooks
You can use discussion tab for discussions for certain topics you do not understand and there also datasets about other topics like football, and news dataset for example. Notebooks that are made by the best of the best to get you started and ready to start building your own work
Kaggle's Cons:
With all the pros of Kaggle comes a really significant con for Kaggle. All the data is already there, You did not need to search and collect the data which is really important part of the data scientist job. However, there is a lot of tutorials online of how to collect data either using web scraping or getting images online for CV.
That's the finish for the Data Science mini-part series. Check my another blog posts if you are interested in learning Data Science or if you are interested in how Data Science is used in football.
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