Statistical Programming in Python

Table of contents

Welcome

Welcome to the Statistical Programming competency courseware for ITSE-1302 Computer Programming: Scientific Python 1 at Austin Community College in Austin, TX. Click here for a course overview.

The college website for this course is: http://www.austincc.edu/baldwin/ .

No conventional textbook

This course does not use a conventional paper or electronic textbook. Instead, this online study guide and the Blackboard learning management system will guide you through a variety of free online resources on topics that you will need to learn in order to succeed in the course.

Four major units

The course is structured into four major units: one review unit and three competency units.

Statistical programming in Python

This is the web page for the competency titled Statistical programming in Python. See the other pages in this online study guide for material that deals with the other two competencies.

One online source describes a data scientist as a person who

Programming is an component of software engineering. Therefore, we might paraphrase the above description and say that a data science programmer is a person who

External online resources

Statistics is a key competency for data science and analytics. This course does not require a course in statistics as a prerequisite for enrollment. However, as an integral part of this course, it is strongly recommended that you enroll in and complete the free Udacity course titled Intro to Descriptive Statistics.

I recommend that course because I believe it is an excellent course and it will teach you everything you need to know to succeed in the statistics competency for this course. However, I have no control over the longevity of that course. In the event that it becomes unavailable, you will find links to numerous other free online alternative courses on the following websites.

There is no shortage of free online statistics courses. An Internet search will likely reveal many more.

Internal online resources

The recommended statistics course, Intro to Descriptive Statistics, does not deal with the programming aspects of statistics. Furthermore, most of the online statistics courses that you find will not deal with the programming aspects of statistics. Computations performed in those courses will likely be performed by hand, with a calculator, or with a spreadsheet. However, the goal of this competency is to teach you how to do statistical programming using the Python Scientific Computing Ecosystem. The following web pages were developed specifically for this course. They provide many statistical programming examples and exercises designed to help you marry your newfound knowledge of statistics with your prior knowledge of Python programming.

You are encouraged to study these programming examples and exercises in parallel with your study of statistics. All of the homework assignments for this competency will deal with some aspect of statistics combined with computer programming.

Use of the NumPy library

Some of the code in the above resources may refer to the NumPy library with statements such as

import numpy as np
x = np.arange(0,10,0.5)

In those cases, in order to understand the code, you will need to skip ahead to the Internal online resources for the competency titled Programming with the NumPy library and study the material on the feature of the NumPy library being used in that instance.

Jupyter Notebook

The pages listed above were developed using Jupyter Notebook in its interactive mode and then downloaded as static HTML files for inclusion in this course material. If you are unfamiliar with the format of Jupyter Notebook, a quick (approximately 19 minutes) tour of the following three videos will teach you everything you need to know to understand the format of the pages listed above.

Assessments

Assessments such as assignments, quizzes, and tests will be administered through Blackboard. Some of the free online resources will also include graded assessments such as exercises and tests. You are encouraged to take advantage of those exercises and tests to enhance your ability to learn and retain the material. However, grades and credits associated with those resources will not be integrated into your grade for this course. Your grade for this course will be based solely on your grades on assignments and tests administered by your ACC instructor through Blackboard.

Review questions

A set of review questions for this competency is provided here. The questions are similar to the questions that you will find on the test for this competency. Therefore, it is strongly recommended that you study the material until you thoroughly understand the material covered by those questions.

Housekeeping material

Author: Prof. Richard G. Baldwin
Affiliation: Professor of Computer Information Technology at Austin Community College in Austin, TX.
File: StatisticalProgramming.htm
Revised: 04/24/18
Copyright 2018 Richard G. Baldwin

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