Prompt Engineering for Data Science

Python is a powerful programming language for data science, and pandas is a popular open-source data manipulation and analysis library in Python. Combined with prompt engineering techniques, working with data in Python is easy and intuitive, which allows you to be more productive and efficient. You will start this course by leveraging prompt engineering to work with pandas. You will explore libraries such as Matplotlib, seaborn, and Plotly, which are used for visualization and charting. With ChatGPT's help you will read data from a CSV file and inspect the DataFrame. You'll delve into pandas Series objects and explore their creation and manipulation. You will leverage prompt engineering techniques to access elements in a Series using index labels through loc, iloc, at, and iat functions and perform operations like modification and visualization. Finally, you will explore how to use pandas DataFrame objects and create basic DataFrames using lists and dictionaries for data assignment and inspection. You will also generate code to perform basic operations on DataFrames using tools such as ChatGPT and Bard.

With DataFrames in pandas you can filter, aggregate, join, pivot, and manipulate data efficiently. These operations enable data analysts and scientists to work with datasets for various data-driven tasks. Prompt engineering tools are adept at generating code to make these tasks simple. You will start this course by exploring the configurations you can apply to read in your data. You'll present your problem statement to ChatGPT and explore the use of arguments to configure various aspects of the file reading, such as defining column names, and specifying which columns to include in the DataFrame. Additionally, you will learn how to read data from different sources, including JSON, Excel, and the Clipboard and write files out to these different formats. Next, you'll delve into common DataFrame operations, examine statistics on your data, rename columns, iterate over, and sort your data. As you encounter issues, you will turn to prompt engineering to help debug them. Finally, you'll explore how you can enhance your data using computed columns. You'll harness the power of two essential functions, apply and map, to transform your records. You will also focus on utilizing generative AI for code generation and you will employ the chain-of-thought prompting method to guide the chatbot in generating code effectively.

Data manipulation involves getting your data in the right format to generate further insights. Prompt engineering allows you to specify your problem statements in natural language and generate code to meet your needs. You will begin this course by applying filters to your DataFrames in pandas. You will use logical and comparison operators to specify filter predicates and filter based on datetime data. Next, you will group and aggregate your DataFrames. You will use prompt engineering to explain your grouping and aggregation requirements and tweak generated code to tailor your solutions. Additionally, you will learn about the split-apply-combine method, a step-by-step technique for grouping and aggregation. You will then tackle data cleaning. You will remove rows with duplicate records and deal with missing values and other inaccuracies in your data. Finally, you will explore the use of pivot tables, which help rearrange and reshape data into a format more suitable for analysis.

Combining data is a key data manipulation technique and is well supported in the pandas library. Exploratory data analysis involves data visualization to understand the relationships that exist in your data. Prompt engineering can help you pick the right visualization for viewing and understanding relationships between variables and can also generate code for these visuals. You will start this course by combining data in DataFrames learning techniques to join DataFrames using different constructs such as the inner join, left and right joins, and the full outer join. Next, you will delve into time-series analysis and visualization. You will use prompt engineering help to visualize your time series data to identify trends and patterns. Finally, you will explore data visualization in Python. You will begin by crafting univariate visualizations that display information about a single variable. You'll see that tools such as ChatGPT and Bard can help you pick the right visualizations for different use cases. You will explore bivariate visuals and use Plotly to generate interactive visualizations which are more user-friendly and intuitive.

Final Exam: Prompt Engineering for Data Science will test your knowledge and application of the topics presented throughout the Prompt Engineering for Data Science journey.