Image from Unsplash
key takeaways
- Data science is a constantly evolving field
- 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.
I can remember the joy and excitement I had when I started my data science journey about 6 years ago. For me, the transition to data science was quite smooth due to my strong background in advanced mathematics and computational physics.
However, as I got further and further along in my data science journey, I realized that I wasn’t making much progress in terms of learning advanced concepts. I caught up on learning the basics. Instead of applying the basic knowledge I already had to real-world data science projects, I kept taking all these different data science courses and data science specializations on platforms like DataCamp, Udemy, YouTube, edX, and Coursera.
At one point, it almost became an addiction for me as I was constantly looking for data science courses to enroll in, especially the ones that were free. Most of the courses taught on these platforms covered only fundamental concepts, as advanced concepts are introduced, but most of the time in a superficial way.
Reflecting on my data science journey, if I were to do it all over again, I would place more emphasis on project-based learning. In my opinion, project-based learning is the most reliable way to learn data science, because it gives you the opportunity to learn as you go. It also helps you apply your knowledge to real-world data science projects.
While it is exciting to acquire as much fundamental knowledge as possible, the focus should be to gradually progress from fundamental concepts to more advanced concepts. Beginners in the field of data science must continue to make quantum leaps in their knowledge as they progress from entry-level to advanced-level data science professionals.
Next, we discuss some of the essential levels of data science.
Tier I Data Science could also be referred to as Basic Tier. At level I, the data science aspirant should be able to acquire the following skills:
- Be able to work with data presented in a CSV (Comma Separated Values) file format
- Being able to clean and organize unstructured data
- Being able to work with data frames.
- Be able to visualize data using different types of visualizations such as line charts, scatter plots, qq plots, density plots, histograms, pie charts, scatter pair plots, heat map plots, etc.
- Be able to perform single and multiple regression analysis.
- Gain proficiency in essential Python libraries for data science, such as numpy, pandas, scikit-learn, seaborn, and matplotlib
Tier II Data Science could also be referred to as Intermediate Tier. At level II, the data science student must master the following:
- Being able to use machine learning classification algorithms such as logistic regression, KNN (K-nearest neighbors), SVM (support vector machine), decision tree, etc.
- Be able to build, test, and evaluate machine learning models
- Be able to perform hyperparameter optimization
- Familiarize yourself with advanced concepts such as k-fold cross-validation, grid search, and set methods.
- You must be an expert in using the scikit-learn library for machine learning applications
Tier III data science could be called Advanced Tier. At level III, the data science student must acquire the following competencies:
- Being able to work with data presented in advanced formats such as text, image, voice or video.
- Familiar with advanced machine learning techniques, such as clustering.
- Familiar with deep learning and neural networks.
- Familiar with deep learning libraries like TensorFlow and PyTorch
- Familiar with cloud-based platforms for machine learning implementation, such as AWS and Azure
The three levels of data science discussed above could be summarized in the image below.
Three levels of data science | Image by Author.
While Tier I and Tier II competencies can be acquired from online courses, a great deal of self-study is essential to learning Tier III (Advanced) concepts. One important resource that could help data science aspirants delve into advanced concepts is the following textbook: Machine learning with PyTorch and Scikit-Learn.
cover of the book
The GitHub repository for this textbook can be found here.
In summary, we have discussed the three levels of data science. Since data science is a constantly evolving field, all data science aspirants should continue to work hard to take the quantum leap to the next level.
Benjamin O. Us He is a physicist, data science educator, and writer, as well as the owner of DataScienceHub. Previously, Benjamin taught engineering and physics at the U. of Central Oklahoma, U. Grand Canyon, and U. State of Pittsburgh.
