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Data Analysis, Introduction
Data analysis is the process of transforming data into useful information to support decision making. It is the foundation for data mining, business intelligence, and predictive analytics. This course presents the tools, techniques and common practices used in the industry, including how to obtain, manipulate, explore, model, simulate and present data. It will help you build the essential technical skills to perform as data analyst or data scientist, and to continue other course studies in the certificate program.
The course examines different approaches to a data analysis project, with a framework for organizing an analytical effort. Popular tools for data analysis, such as R and Python can be used to carry out analysis, but R is used primarily in class instruction and examples. The course covers how to obtain and manipulate the raw data for use, as well as the basic exploratory analysis and common data analytical techniques such as regression, simulation, estimation and forecasting. It includes several graphing and visualization tools to understand the data and to present findings and results.
By the end of the course, you will learn a working framework to approach any data analysis project. You will be able to use R (or Python) to complete a large data analysis project, including a write-up with findings, insights and visuals. All tools used are open sourced.
- 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
- Estimation techniques: Multiple approaches based on assumptions, sampling basics
- 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, and Python experience can be helpful.) Basic knowledge of probability and statistics is required (at the level of most basic statistics textbooks; see for example ).
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