Cocalc Tutorial 01 Basics Ipynb

By the end of this lecture you will be able to: Define variables and differentiate between global and local variables. Identify and use different object types in python. Use some of the python's default functions and define your own functions. Introduction to numpy and matplotlib libraries This is a notebook.

To run it you need Julia and Jupyter/VS Code. To run Julia without a local installation, use (for instance) CoCalc or JuliaHub. Also Colab works, but the support is still (as of mid 2025) a bit tentative. To install Julia on your machine, download and install it. This link contains both instructions for the Microsoft Store and links to binaries for manual download. You may also want to use VS Code with the Julia extension.

It is an IDE (editor and more) that can run script files and notebooks. It works well with windsurf and copilot. To run notebooks from your local installation, you need either (1) VS Code with the Julia extension (see above) or (2) Jupyter/JupyterLab. An expression describes a computation. Python evaluates expressions and generates a value. We say that expressions "evaluate to" their result.

Python's arithmetic operators allows you to use a Python command line or Jupyter notebook cell as a "calculator" Multiplication is done with an asterisk * Normal or "true" division is done with /. This operation will always return a float Python has two other division operators. "Floor" division //, which returns the division rounded down to the lowest integer, and the modulo operator %, which returns the remainder of a division after substracting the next lowest integer.

These are easier to understand by example: If you are running this workshop on Binder then there is no need to install Python. These servers are free and subsequently quite slow. For more advanced applications it is recommended that you install Python locally. The be able to generate and export basic figures Jupyter notebooks are the ideal way to scientific data analysis, it is exactly what they were designed for.

Julia-Pyton-R The code is split up into cells. These cells can be either text or code, and can be run use shift + Enter Any line of text preceded by a hash symbol # is considered a comment and is fully ignored by Python. Any place you see ..., you must fill in the function, variable, or data to complete the code. Replace fail() with your completed code and run the cell!

Reminder: All autograded questions (i.e., questions with tests) are worth 1 point and all hidden test and manually graded questions are worth 3 points. Revision Question Match the following definitions with the corresponding functions used in R: {points: 1} A. Reads the most common types of flat file data, comma separated values and tab separated values, respectively. B. Keeps only the variables you mention.

You can use NetworkX to construct and draw graphs that are undirected or directed, with weighted or unweighted edges. An array of functions to analyze graphs is available. This tutorial takes you through a few basic examples and exercises. Note that many exercises are followed by a block with some assert statements. These assertions may be preceded by some setup code. They are provided to give you feedback that you are on the right path -- receiving an AssertionError probably means you've done something wrong.

https://networkx.github.io/documentation/networkx-2.2/ https://networkx.github.io/documentation/networkx-2.2/tutorial.html Recall that import statements go at the top of your code, telling Python to load an external module. In this case we want to load NetworkX, but give it a short alias nx since we'll have to type it repeatedly, hence the as statement. of return calculations: returns, average returns and volatilities of portfolios. The notebook uses the functions printmat() and printlnPs() for formatted printing of matrices and numbers.

These functions are in the included src/printmat.jl file and call on the Printf package. Also, the lag() function (from src/lag.jl) lags a vector/matrix. The return of holding the asset between t−1t-1t−1 and ttt is Rt=(Pt+Dt)/Pt−1−1,R_t = (P_t+D_t)/P_{t-1} - 1,Rt​=(Pt​+Dt​)/Pt−1​−1, Jupyter notebook Handouts/Handout-01/Handout-01.ipynb Welcome to PHYS 48T!

This is a practical course on data science (DS) and machine learning (ML). We will learn about the ideas underlying the computational tools of these fields, and spend a lot of time applying those tools to various examples. The course will be programming-intensive. There are various programming languages and tools for DS and ML; the most popular are Comes out of the field of statistics; a bit of a quirky language, but very widely used in the DS/ML community, especially among academic statisticians. It is open source, and research ideas are quickly implemented into R packages by volunteers/authors.

We won't use R in this course, but I recommend that you take a quick look at it when you get a chance. A general-purpose programming language that is both highly readable and suitable for large-scale projects. Has a very wide range of packages, not just for DS and ML, but for all sorts of purposes. We will be using Python throughout this course.

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