Cocalc Week 1 Exercise 1 Scripts Examples Notebook Tour Ipynb

Jupyter notebook atms391geodata/Week 1/Week 1 Exercise 1 scripts_examples_notebook_tour.ipynb This document will give you a brief tour of the capabilities of the IPython notebook. You can view its contents by scrolling around, or execute each cell by typing Shift-Enter. After you conclude this brief high-level tour, you should read the accompanying notebook titled 01_notebook_introduction, which takes a more step-by-step approach to the features of the system. The rest of the notebooks in this directory illustrate various other aspects and capabilities of the IPython notebook; some of them may require additional libraries to be executed. NOTE: This notebook must be run from its own directory, so you must cd to this directory and then start the notebook, but do not use the --notebook-dir option to run it from another...

You can run shell aliases and magic commands: Welcome to the first optional lab! Optional labs are available to: provide information - like this notebook reinforce lecture material with hands-on examples provide working examples of routines used in the graded labs

Get a brief introduction to Jupyter notebooks This Jupyter notebook, for Week 1 of The Open University's Learn to code for Data Analysis course, contains code examples and coding activities for you. You'll come across steps in the FutureLearn course directing you to this notebook. Once you've done the exercise, go back to FutureLearn to discuss it with your fellow learners and course facilitators and mark it as complete. A variable is a named storage for values. An assignment takes a value (like the number 100 below) and stores it in a variable (deathsInPortugal below).

To display the value stored in a variable, write the name of the variable. Each variable can store one value at any time, but the value stored can vary over time, by assigning a new value to the variable. 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 Learn to implement the model fw,bf_{w,b}fw,b​ for linear regression with one variable Here is a summary of some of the notation you will encounter. |General Notation | Description| Python (if applicable) | |: ------------|: ------------------------------------------------------------|| | aaa | scalar, non bold || | a\mathbf{a}a | vector, bold || | Regression | | | | | x\mathbf{x}x | Training... | y_train | x(i)x^{(i)}x(i), y(i)y^{(i)}y(i) | ithi_{th}ith​Training Example | x_i, y_i| | m | Number of training examples | m| | www | parameter: weight, | w | | bbb | parameter: bias |... NumPy, a popular library for scientific computing

Matplotlib, a popular library for plotting data Now that we have the time series, let's split it so we can start forecasting Let's zoom in on the start of the validation period: You can see that the naive forecast lags 1 step behind the time series. Now let's compute the mean squared error and the mean absolute error between the forecasts and the predictions in the validation period: That's our baseline, now let's try a moving average:

Determine the type of the variable mystery using a built-in function Explain in simple terms what float(var4) does. Explain in simple terms what str(var2) does. Fill in the blanks so that the two programs below run without errors. In words, explain what does material[low:high] do? (where low and high are arbitrary numbers)

In this notebook we will deal mainly with syntax and semantics in Python we explore some basic expressions in Python we introduce some of the most important control flow statements This is a Jupyter notebook. It is an interface allowing us to combine code (in this case Python) and formatted text in a unified way. The basic unit in a notebook is a cell.

You are right now reading the content of a "Markdown" cell, designed to input formatted text. There are also 'Code' cells, designed to input executable code. Jupyter notebook Homework 1/HW 1.1 - Introduction to the IPython notebook.ipynb In this class, you will be using the IPython Notebook to write code and written responses to assignments. The notebook consists of a series of cells. For example, this text is in what is called a "Markdown cell".

The following cell is a "code cell": You can tell what the type of a cell is by selecting the cell, and looking at the toolbar at the top of the page. For example, try clicking on this cell. You should see the cell type menu displaying "Markdown", like this: In the notebook, there are two modes: edit mode and command mode. By default the notebook begins in command mode.

In order to edit a cell, you need to be in edit mode.

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