Key Points:
- Data science is a field that is constantly evolving
- In the field of data science, learning is lifelong
- A data science professional must continue to improve their knowledge in the field to keep up with new technological developments and software applications
Introduction:
I recall the delight and excitement I felt when I began my data science journey six years ago. Because of my excellent experience in advanced mathematics and computational physics, the transfer to data science was rather easy for me.
But, as I progressed in my data science adventure, I found that I wasn’t making much progress in terms of learning advanced ideas. I got caught up in learning only the fundamentals. Instead of using my existing basic knowledge to real-world data science projects, I continued to take various data science courses and data science specialties on platforms such as DataCamp, Udemy, YouTube, edX, and Coursera.
It almost became an obsession for me at one point, since I was continuously looking for data science courses to enroll in, especially free ones. Most of the courses given on these platforms covered just fundamental principles, with advanced concepts introduced only briefly.
If I had to relive my data science journey, I would place a greater emphasis on project-based learning. Project-based learning, in my opinion, is the most dependable method of studying data science since it allows you to learn as you go. It also aids in the application of your expertise to real-world data science initiatives.
While acquiring as much core knowledge as possible is thrilling, the focus should be on gradually progressing from fundamental concepts to more complex notions. As they progress from beginner to advanced-level data science professionals, beginners must continue to make quantum leaps in their expertise.
In what follows, we will go through some of the fundamental layers of data science.
Level I Data Science
Level I data science is also known as the Basic Level. The data science aspirant should be able to acquire the following skills at level I:
- Ability to work with data in CSV (comma-separated value) file format
- Capable of cleaning and organizing unstructured data
- Ability to work with data frames
- Ability to visualize data using various visualizations such as line graphs, scatter plots, qq plots, density plots, histograms, pie charts, scatter pair plots, heatmap plots, and so on.
- Do simple and multiple regression analyses
- Get proficiency in key Python data science libraries such as numpy, pandas, scikit-learn, seaborn, and matplotlib.
Level II Data Science
Level II data science is also known as the Intermediate Level. The data science learner should be able to do the following at level II:
- Understand and be able to apply machine learning classification algorithms such as logistic regression, KNN (K-nearest neighbors), SVM (support vector machine), decision tree, and so on.
- Ability to create, test, and evaluate machine learning models
- Possess the ability to perform hyperparameter optimization
- Understand advanced concepts including k-fold cross validation, grid search, and ensemble approaches.
- Should be knowledgeable in using the scikit-learn library for machine learning applications.
Level III Data Science
Level III data science is also known as the Advanced Level. The data science student should gain the following competences at level III:
- Ability to work with data in advanced formats such as text, image, audio, or video
- Advanced machine learning techniques, such as clustering, should be familiar.
- Understand deep learning and neural networks
- Understand deep learning libraries like TensorFlow and PyTorch
- Familiarity with cloud-based machine learning deployment platforms such as AWS and Azure
Conclusion
While Level I and Level II competence can be obtained through online courses, mastering Level III (Advanced) ideas requires extensive self-study. The following textbook is an excellent resource that could assist data science aspirants in delving deeply into advanced concepts: PyTorch with Scikit-Learn for Machine Learning.
In conclusion, we explored the three degrees of data science. Because data science is a constantly evolving subject, every data science aspirant should continue to work hard in order to make the quantum leap to the next level.
