Data analysis is the process of converting data into valuable information to inform decision-making. This course provides a foundation in the tools, techniques, and common practices used in the industry. It covers the full lifecycle of a data analysis project, including how to obtain, manipulate, explore, model, and present data.

We will explore different analytical approaches and frameworks, using popular tools like R and Python. The course emphasizes hands-on application, with R being the primary language for instruction and examples. You will learn to prepare raw data for use, perform exploratory analysis, and apply techniques like regression, simulation, and forecasting. We will also cover various graphing and visualization tools to help you understand and present your findings.

Additionally, the course now includes an introduction to leveraging Generative AI for data analysis. You will use an AI-based tool to generate and validate R programs, helping you streamline your workflow.

By the end of the course, you will be able to apply a working framework to any data analysis project and use R or Python to complete a large-scale project, including a professional write-up with insights and visualizations. All tools are open-source, except for a trial version of the AI tool.

Learning Outcomes 
At the conclusion of the course, you should be able to

• Describe the framework necessary to approach Data Analysis problems
• Discuss the importance of Data Analysis for Data Science, Data Visualization & exploration
• Explain the basic concepts of R and using R for Data Analysis
• Identify the right tools, concepts and functions that are required for Data Analysis
• Leverage Generative AI concepts and how to generate R Programs with the help of AI.

Topics Include:

• Approaches to data analysis: Templates, write-ups and illustrative examples
• Overview of tools for data analysis: R, R-Studio (IDE) and comparison with Python
• Obtaining data: Finding data sets and Web scraping, file formats
• Data manipulation techniques: Data quality, reshaping data, appending and joining data sets
• Plotting and visualization: Exploration and presentation
• Exploratory data analysis: Visual inspection, descriptive analytics, insights
• Regression models: Simple, multiple and logistic
• Analysis report write-up and presentation, including graphs
• Simulation techniques: Fitting distributions, simulating stochastic processes
• Forecasting methods and applications: Smoothing, moving averages, time series, ARIMA

Skills Needed:

Some programming experience is recommended. (R will be covered in class and used in examples. Python experience can be helpful.) Basic knowledge of probability and statistics required, at the level of basic statistics textbooks (see example: www.stattrek.com).