Homework for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

Module DescriptionThis educational module provides an engaging introduction to two pivotal topics within the field of information technology: Machine Learning and Data Science. Designed for students, the module emphasizes the practical applications, processes, and ethical considerations of these technologies while equipping learners with the foundational knowledge necessary to understand their roles in today’s data-driven world. By exploring both machine learning and data science, students will gain insights into how these fields work together to drive innovation across various industries.Section 1: Machine LearningMachine learning is a branch of artificial intelligence that enables systems to learn from data and make predictions without being explicitly programmed. This section will highlight its transformative impact on industries such as healthcare, finance, and entertainment, demonstrating the increasing relevance of machine learning in everyday applications.Section 2: Data ScienceData science is an interdisciplinary field that combines statistics, computer science, and domain expertise to derive insights from data. This section will explain how data science underpins machine learning by ensuring that data is collected, cleaned, and analyzed effectively.

Information visualization is concerned with the visual and interactive representation of abstract and possibly complex datasets. As we encounter growing datasets in various sectors there is an increasing need to develop effective methods for making sense of data. Information visualization relies on computational means and our perceptual system to help reveal otherwise invisible patterns and gain new insights. Across various fields, there is great hope in the power of visualization to turn complex data into informative, engaging, and maybe even attractive forms. However, it typically takes several steps of data preparation and processing before a given dataset can be meaningfully visualized. While visualizations can indeed provide novel and useful perspectives on data, they can also obscure or misrepresent certain aspects of a phenomenon. Thus it is essential to develop a critical literacy towards the rhetoric of information visualization. One of the best ways to develop this literacy is to learn how to create visualizations! The tutorials offer a practical approach to working with data and to create interactive visualizations.

The tutorials require basic familiarity with statistics and programming. They come as Jupyter notebooks containing both human-readable explanations as well as computable code. The code blocks in the tutorials are written in Python, which you should either have already some experience with or a keen curiosity for. The tutorials make frequent use of the data analysis library Pandas, the visualization library Altair, and a range of other packages. You can view the tutorials as webpages, open and run them on Google Colab, or download the Jupyter notebook files to edit and run them locally.

This course explores the ultimate limits to communication and computation, with an emphasis on the physical nature of information and information processing. Topics include: information and computation, digital signals, codes and compression, applications such as biological representations of information, logic circuits, computer architectures, and algorithmic information, noise, probability, error correction, reversible and irreversible operations, physics of computation, and quantum computation. The concept of entropy is applied to channel capacity and to the second law of thermodynamics.

This subject is aimed at students with little to no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems. It also aims to help students, regardless of their major, feel justifiably confident in their ability to write simple programs that allow them to accomplish useful goals. The class will use the Python 3 programming language.

This is an introductory course on computational thinking. We use the Julia programming language to approach real-world problems in varied areas, applying data analysis and computational and mathematical modeling. In this class you will learn computer science, software, algorithms, applications, and mathematics as an integrated whole. Topics include image analysis, particle dynamics and ray tracing, epidemic propagation, and climate modeling.

This class uses revolutionary programmable interactivity to combine material from three fields – Computer Science + Mathematics + Applications – creating an engaging, efficient learning solution to prepare students to be sophisticated and intuitive thinkers, programmers, and solution providers for the modern interconnected online world.
Upon completion, students are well trained to be scientific “trilinguals,” seeing and experimenting with mathematics interactively as math is meant to be seen, and ready to participate and contribute to open source development of large projects and ecosystems.

6.0002 is the continuation of 6.0001 Introduction to Computer Science and Programming in Python and is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.

This half-semester course introduces computational thinking through applications of data science, artificial intelligence, and mathematical models using the Julia programming language. This Spring 2020 version is a fast-tracked curriculum adaptation to focus on applications to COVID-19 responses.
See the MIT News article Computational Thinking Class Enables Students to Engage in Covid-19 Response

6.00 Intro to CS and Programming has been retired from OCW. You can access the archived course on DSpace – MIT’s digital repository. Please see the list of introductory programming courses and other programming courses from recent years.

Objectives
Part 1: An Introduction to Data Analysis Using Excel To interpret, summarize and present numerical data using the digital tool Microsoft program Excel. To plot numerical data as a graph and determine an equation of a line. In addition, using the appropriate formatting functions to label your graph and creating a best fit line.
Part 2: Lab Report Writing Using LaGCC Institutional Data To communicate your interpretations of research data. This is done writing discussions and conclusions (using scientific language) and is often accompanied by data tables and graphs. To use your Microsoft Excel graphing skills to interpret, inquire and extrapolate meaning data to support your lab report conclusions To structure your written lab report in the format of: Abstract, Introduction, Material, Methods,Results, Discussion/Conclusion and References

The goal of these videos is to provide students with tools and concepts for working with R, a free software environment for statistical computing and graphics. The students will learn the basics of R, how to navigate the R interface and deal with different data formats, how to run and interpret linear models with R, and how to use Geographic Information Systems (GIS) in R. These practical sessions were developed as part of the course 1.845 Terrestrial Carbon Cycle and Ecosystem Ecology but will be useful for anyone looking to learn about R and GIS.

This workshop aims to help students and teachers of Humanities and Social Science learn the basics of text-mining using Python. It is meant as an introduction to the use of computational techniques for analysing data for Humanists and Social Scientists. It contains a "Jupyter Notebook", which is basically a website where students will be taught how to write and execute code that will help them solve research problems that Humanists and Social scientists face. Additionally, this lesson also contains a video that demonstrates how to use that website. The total expected time to use this resouce is around 2 hours. 

This is an open-source and open access book on how to do Data Science using Julia. The book describes the basics of the Julia programming language DataFrames.jl for data manipulation and Makie.jl for data visualization.

You will learn to:

- Read CSV and Excel data into Julia
- Process data in Julia, that is, learn how to answer data questions
- Filter and subset data
- Handle missing data
- Join multiple data sources together
- Group and summarize data
- Export data out of Julia to CSV and Excel files
- Plot data with different Makie.jl backends
- Save visualizations in several formats such as PNG or PDF
- Use different plotting functions to make diverse data visualizations
- Customize visualizations with attributes
- Use and create new plotting themes
- Add LaTeX elements to plots
- Manipulate color and palettes
- Create complex figure layouts

This curriculum was designed for high school students with no prior coding experience who are interested in learning Python programming for data science. However, this course material would be useful for anyone interested in teaching or learning basic programming for data analysis.

The curriculum features short lessons to deliver course material in “bite sized” chunks, followed by practices to solidify the learners' understanding. Pre-recorded videos of lessons enable effective virtual learning and flipped classroom approaches.

The learning objectives of this curriculum are:

1. Write code in Python with correct syntax and following best practices.
2. Implement fundamental programming concepts when presented with a programmatic problem set.
3. Apply data analysis to real world data to answer scientific questions.
4. Create informative summary statistics and data visualizations in Python.
5. These skills provide a solid foundation for basic data analysis in Python. Participation in our program exposes students to the many ways coding and data science can be impactful across many disciplines.

Our curriculum design consists of 27 lessons broken up into 5 modules that cover Jupyter notebook setup, Python coding fundamentals, use of essential data science packages including pandas and numpy, basic statistical analyses, and plotting using seaborn and matplotlib. Each lesson consists of a lesson notebook, used for teaching the concept via live coding, and a practice notebook containing similar exercises for the student to complete on their own following the lesson. Each lesson builds on those before it, beginning with relevant content reminders from the previous lessons and ending with a concise summary of the skills presented within.

Lecture for the course "CSC 59970 – Intro to Data Science" delivered at the City College of New York in Spring 2019 by Grant Long as part of the Tech-in-Residence Corps program.

Lecture for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

Lecture for the course "CSC 59970 – Intro to Data Science" delivered at the City College of New York in Spring 2019 by Grant Long as part of the Tech-in-Residence Corps program.

Lecture for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

Lecture for the course "CSC 59970 – Intro to Data Science" delivered at the City College of New York in Spring 2019 by Grant Long as part of the Tech-in-Residence Corps program.