Welcome to the Data Science Fundamentals course. In this course, you will view many Jupyter notebooks (We will explain what these are).
We try to keep some similar structure across all the notebooks. In particular, we try to stick to a similar notation, be it mathematical notation or general notation indicating how you should behave when reading these notebooks.
Before we explain the notation, we would like to state the most important thing you should remember when going through those notebooks:
You can freely modify the notebooks and try things out. Do it. There is a well-known adage that states
I hear, I know. I see, I remember. I do, I understand.
We are firm believers in this mantra. You could simply go through the notebooks and run the code, but you would only learn half as much. Try changing things and adding your custom code. You will run into errors, and that is okay. You will also improve, and in no time, you will become quite good at programming and data science. So do yourself a favor, and try to get your hands dirty as much as possible!
Now that this is out the way, let's quickly go over the notation you will encounter in the notebooks.
In the notebooks, you will find sections with emojis. The emojis can be interpreted as follows:
➡️ ✏️: You have to either write a piece of code, discuss something with your classmates, or write something on paper. It's your turn to solve a problem.
🙀 🤯: This is a section with complicated concepts. These are either involved mathematical notation or advanced programming concepts. It's okay if you don't understand the content on your first readthrough. However, if you are interested in data science, it's good to re-read those sections and ensure you understand them when you have more knowledge.
🤔: Pause and ponder. When learning, it's good to take a step back and think about what we are doing and why we are doing it this way. If you encounter this emoji, you should try to think about something and ensure you understand it.
In general, we try to stick to the following notation in the notebooks, however, there might be some exceptions. If anything is unclear make sure to ask.
-
$x$ : a scalar value, i.e., some real number. -
$\mathbf{x}$ : a vector. -
$\mathbf{X}$ : a matrix. -
$x^{(i)}$ the$x$ -value for the$i^\text{th}$ observation in a set of data points. You will also encounter this superscript on vectors, i.e.,$\mathbf{x}^{(i)}$ .
- 000_introduction-to-python
- 001_data-pre-processing
- 002_data-visualization
- 003_dicts-and-loops
- 004_numpy-random-numbers
- 005_exchange-rate-data
- 006_long-wide-data-formats
- 007_joins-and-merges
- 008_linear-regression-varia
- 009_functions
- 010_string-manipulation
- 011_vectorization
- 101_iris-perceptron
- 102_wdbc-perceptron
- 103_wdbc-perceptron-standardized
- 104_viz-mse
- 105_wdbc-sgd
- 106_ols-train-test-cv
- 107_logistic-regression-roc
- 108_decision-trees
- 109_random-forests
- [201_lin-transforms]
- [202_lin-transforms-SVD]
- 203_svd-pca
- 204_clustering
- 205_my-own-neural-network-1
- 206_my-own-neural-network-2
- 207_neural-networks-in-sklearn