Data analysis is the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves several steps, such as data collection, data cleaning, data transformation, exploratory data analysis, statistical modeling, and communication of results. Data analysis can be conducted using a variety of techniques, such as statistical analysis, machine learning, and qualitative analysis. It is used in many different fields, including business, health sciences, social sciences, and engineering.
References:
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77-101.
Tavani, J. L. (2018). Ethics and technology: controversies, questions, and strategies for ethical computing (5th ed.). John Wiley & Sons.
Dear Candy Egah
Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions¹. It involves collecting, modeling, and analyzing data using various statistical and logical methods and techniques². The data analysis process typically moves through several iterative phases such as identifying the research question you would like to answer, collecting the raw data sets you will need to help you answer the identified question, cleaning the data to prepare it for analysis, analyzing the data, and interpreting the results of your analysis¹.
Here are some references that might be helpful for you:
1. [What Is Data Analysis? (With Examples) | Coursera](https://www.coursera.org/articles/what-is-data-analysis-with-examples)
2. [What Is Data Analysis? Methods, Techniques, Types & How-To - datapine](https://www.datapine.com/blog/data-analysis-methods-and-techniques/)
3. [Data Analytics: Definition, Uses, Examples, and More | Coursera](https://www.coursera.org/articles/data-analytics)
4. [Data analysis - Wikipedia](https://en.wikipedia.org/wiki/Data_analysis)
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