Data Analysis is a process of applying statistical and mathematical techniques systematically to understand, explore, and analyze data to find patterns, and draw inferences that help businesses make data-driven decisions. This typically involves multiple activities such as data collection, exploration, cleaning, pre-processing, and organizing data. Many times, data analysis is an iterative ongoing process where the data is continuously collected and analyzed simultaneously. There are two primary methods for data analysis.
Qualitative techniques and quantitative techniques. Quantitative data analysis techniques involve working with quantitative/numerical data including statistics, percentages, and calculations. These techniques also include working with algorithms, mathematical analysis tools, and software to manipulate data and uncover hidden business value. For example, quantitative data analysis used to assess market data helps a company decide a price for its new product.
Qualitative data analysis involves, working with non-numerical data i.e categorical variables. Qualitative data analysis is also used in many business processes, such as identifying themes and patterns, answering research questions, etc to improve a product.
This course provides an overview of data concepts and what data analysis is and then deep dives into the fundamentals of Data Analysis such as statistics and probability. This course also focuses on widely used data analysis methods such as regression along with detailed steps to perform the same.
*Must have Microsoft Excel in order to complete class activities.
Introduction to Data Analysis
Descriptive Statistics
Inferential Statistics
Introduction to Probability
Data Analysis Process
Introduction to Predictive Analytics
