## About the course

This course is designed to guide students through the methods and techniques used in data analysis. Its purpose is to introduce you in the basic steps on how to solve analytical problems and decision-making challenges with data. It is divided into 14 sessions:

**1. Introduction.** We will discuss the characteristics of the course and how important data analysis has become in the last years for any professional both inside and outside academia. I must put clearly that I expect they use quantitative data analysis during the course.

**2. "Houston, we've had a problem."** This famous quotation from the Apollo 13 mission will be the starting point for our discussion on how to identify and define a problem in data analysis. We can use another famous citation from Alice in Wonderland to illustrate the importance of defining the problem before collecting data. In a passage where she is lost in the forest, Alice asks the Cheshire Cat which way she should go. The cat asks her where she wants to go. Alice says she doesn't know. The cat replies that it doesn't matter which way she goes if she doesn't know where she wants to go. The same is true for data analysis. If you don't know what you want to find, you will never find it or, at best, you will eventually find something. In this session, we will start learning how to define a problem that can be solved using data analysis.

**3. Prompt "engineering" or how to avoid AI frying your brain.** Most students use AI tools almost every day. Nonetheless, they have not received proper training to do so. They also are not conscious about the limitations, biases, and risks posed by these tools. In this practical session students will learn how to create useful prompts to guide LLM (Large Language Models, i.e., chatGPT and substitutes) to help them in their academic tasks. They will learn how to design prompts obtain information and help their work and to avoid using AI as a replacement for their brains.

**4. Getting help.** In this session you will learn how to find information about the problem you are trying to solve. Here we will discuss how to search for literature and data that can assist you in your quest. We will also get a hint on how to use LLM prompts to guide us through the process. We will also discuss how to cite properly. In this class, I will introduce the Chicago author-date style and some tools to help you with citations. I will also provide you with a list of useful resources to help you in your research.

**5. Literature review**. In this session, you will learn how to write a literature review, summarize the main findings of different academic texts, and identify gaps in the literature. You will also learn how to write a literature review that is clear, concise, and well-organized. You will also learn how to use LLMs to help you evaluate and improve your literature review (not doing it for you).

**6. Working with data.** In this session, you will learn the basics of working with data sets. Observations, variables, formulas... Here, we will start developing the essential skills for working with data. You will learn the very basics of working with spreadsheets (Excel, Google Sheets, Numbers): how to enter data, remove unnecessary information, use formulas, and create charts. I will show you how to use raw data to create relative indicators and other measures of interests. You will also learn how to explore the information to extract patterns and how to build a table.

**6. Data collection.** In this session, you will learn how to collect data. In particular, we will explore how to build a database using information available in scientific databases and international organizations repositories. It is a development of the previous task, but now we will focus on the data collection and manipulation process. Students will be given a subject and will be required to collect data from different sources. We will also discuss how to use LLMs to help you in the process of collecting data.

**7. Data visualization principles.** This class will introduce students to the principles of data visualization. We will learn the grammar of graphics and how to use different visual elements (such as color, size, shape, font, etc.) to facilitate discovery and communication of patterns in data. We also will introduce the Dear Data Project. In our practice, we will learn how to identify interpret different types of chart.

**8. Data visualization in practice.** During this section, students will be required to create different data visualizations (charts and tables). The purpose is to develop their skills to represent different types of data and to communicate the information effectively. We will also discuss how to use LLMs to help you in the process of creating data visualizations.

**9. Computational Thinking.** Here, you will learn how to think algorithmically. We will discuss how to break down a problem into smaller parts and how to solve each part separately. We will also discuss how to use algorithms to solve problems and how to evaluate the efficiency of an algorithm. Performance, optimization, alternative solutions, and complexity will be discussed.

**10. Principles of Data analysis.** In this session, you will learn how to analyze data. We will discuss the different types of data analysis (descriptive, inferential, predictive, etc.) and how to choose the right type of analysis for your problem.

**11. Data analysis in practice.** In this session, you will be required to analyze some charts and tables. The purpose is to develop your skills to interpret different types and to identify patterns in the data. We will also focus on connecting multiple pieces of empirical evidence into a coherent narrative. As in previous sections, we will also discuss how to use LLMs to help you in the process of analyzing data.

**12. Storytelling with data.** In this session, you will learn how to tell a story with data. We will discuss how to use data to support your arguments and how to present your findings concisely. We will also discuss how to use LLMs to help you in the process of storytelling with data.

**13. What's next?** In this session, I will show you how data analysis really works in a research context. I will introduce you with R and show you some basic analyses using more complex datasets. I will also introduce you to more complex data structures, such as networks and relational data.

**14. Overall review of the course.** In this session, we will revise the knowledge acquired in class during the semester and evaluate the course for improvements and future developments.

## What this course is not

There are some vital knowledge for data analysis that we will not cover in this course. We will not learn statistics here (beyond some very basic concepts). We also will not cover data analysis programming languages such as R or Python. Statistics + data analysis tools are essential for any professional data analysis. However, this course is designed to be a first step for those who are not familiar with data analysis. You can find more advanced courses in the internet for free. For those interested in learning more and completing their training with skills that will not be taught in the university, I can recommend some courses and books.

Consideraciones Generales

The evaluation aims at verifying the advancements of students during the entire semester.

Criterios de evaluación

The evaluation will consist in the delivery of 10 (ten) tasks, most of them made in class. Each task will be performed individually, but you can ask for help from your colleagues and from me. You can work in groups, but the final product must be individual. Very similar products will be penalized severely on their evaluation. Please, do not copy from your colleagues. You will thank me later.

**Rabbids Coding Contest (Optional)** Students who wish to earn an extra point can install and play the game Rabbids Coding (available on both Android and iOS devices for free). The game is designed to teach programming concepts, and it is a fun way to learn while playing.

Any student who completes all levels of the game will receive the extra point. You need to provide a screenshot of the final level completed and show me your device (phone, tablet or computer in class) to receive the point.  **Remember: you need BOTH to send me the screenshot by mail to rodrodr@usal.es AND to show me your devices in class**. Additionally, this extra point will be used to break ties when awarding a "Matrícula de Honor" in cases where two students have both received a final grade of 10. **The deadline is the last class (session 14)**, but I strongly advise you to do it as soon as possible, so you will have more time to study for the final exams of other courses.

**IMPORTANT NOTES:**

Submitting each practice does not automatically guarantee the full grade. Each practice will be evaluated based on its quality and level of development. As a result, the final grade can range from 0 (not submitted or insufficiently developed) to 1 (perfect and well-developed). For instance, a literature review that is incomplete but well-written may receive half of the total grade. In this case, the final mark for the exercise would be calculated as follows: 1 × 0.5 = 0.5/10.

The final grade will be the sum of the grades of each task and will range between 0 and 10. The minimum to pass the course is 5/10.

Recomendaciones para la recuperación.

All content will be evaluated in the second exam. The qualifications of previous exercises, such as the reading reports or bibliographic essays, will not be considered in this phase.