Introduction to Data Science (Public Policy) from Coursera

By the end of the course you should be able to:

1. Know enough Python basics to qualify as, at a minimum, a novice programmer

2. List different types of digital data (e.g., delimited separated files, raw text, json), be able towrite

Python code to input and process each type, and explain how and why you might use each

data type in research

3. Write Python code to collect and structure digitized data, including from APIs, process the

data, and produce visualizations and/or output to explore or analyze the data

4. Explain what the output from computational methods means, and derive a few insights about

the social world from the output and visualizations

5. Feel comfortable learning new techniques and new Python libraries on your own

What's inside

Syllabus

Variables, Expressions, Statements, Conditionals

Data is everywhere. From historical documents to literature and poems, diaries to political speeches, government documents, emails, text messages, social media, images, maps, cell phones, wearable sensors, parking meters, credit card transactions, Zoom, surveillance cameras. Combined with rapidly expanding computational power and increasingly sophisticated algorithms, we have an explosion of digital data around us. Privacy, ethics, surveillance, bias, discrimination are some of the obvious policy issues emanating from these data sources. But there is also incredible potential for better understanding the social world, and the potential to use data for good.In this course we will explore how data and digital material can be leveraged to have a better understanding of social issues. We will devote a substantial component of the course to explore the technical skills necessary to access and analyze data (aka programming in Python!), and best practices re: research design, and the practical knowledge we and others can produce using digital data and methods.In this module, we will introduce Python programming using Jupyter Notebook, accessible via Anaconda or Google Colab. It begins with setting up the environment and executing Python code. Learners will explore fundamental concepts such as printing values, identifying variable types, and working with different data types. The module covers statements, expressions, and operators, including arithmetic, comparison, and assignment operators. There will be a dedicated section on strings introduces string operations and manipulation. Logical and Boolean expressions, along with conditional statements (if, else, elif), will also be explored to understand decision-making in Python, including nested and chained conditionals. Additionally, user input handling will also be covered to enable interactive programming. The module concludes with an introduction to Markdown, helping learners document their work effectively in Jupyter Notebook.

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