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Updated in [June 30th, 2023]

The UT701x: Foundations of Data Analysis course provides students with an introduction to the basics of statistical thinking. Students will learn how to use R and hands-on Labs to answer questions about data in the world around them. The course consists of instructional videos for statistical concepts, tutorial videos for using R, and hands-on Labs to apply the concepts to real-world datasets. Topics covered include Descriptive Statistics, modeling, and Inferential statistical tests. The course is scheduled to run from November 4, 2014 to February 6, 2015, and requires access to a computer with internet access and the ability to install software (R and RStudio). The course text is a custom created open source text embedded into the edX course as PDF readings.

[Applications]
Upon completion of UT701x: Foundations of Data Analysis, students should be able to apply basic statistical techniques to answer their own questions about their own data, using a widely available statistical software package (R). They should also be able to interpret and use linear, exponential, and logistic functions, as well as understand and use inferential statistical tests such as the t-test, ANOVA, and chi-square.

[Career Path]
One job position path recommended to learners of this course is a Data Analyst. A Data Analyst is responsible for collecting, organizing, and analyzing data to help inform business decisions. They use a variety of tools and techniques, such as statistical analysis, data mining, and predictive modeling, to uncover insights from data. They also create reports and visualizations to communicate their findings to stakeholders.

The development trend of this job position is that it is becoming increasingly important in the modern business world. Companies are increasingly relying on data to make decisions, and Data Analysts are in high demand. As technology advances, Data Analysts will need to stay up to date on the latest tools and techniques to ensure they are able to effectively analyze data. Additionally, Data Analysts will need to be able to communicate their findings in a clear and concise manner to ensure that stakeholders understand the implications of their findings.

[Education Path]
The recommended educational path for learners of this course is to pursue a degree in Data Analysis. This degree typically involves taking courses in mathematics, statistics, computer science, and data science. The curriculum of a Data Analysis degree typically includes topics such as data mining, machine learning, data visualization, predictive analytics, and data engineering. Students will also learn how to use various software tools and programming languages to analyze data. Additionally, students will gain an understanding of the ethical and legal implications of data analysis.

The development trend of Data Analysis degrees is to focus on the application of data analysis techniques to solve real-world problems. This includes the use of artificial intelligence, natural language processing, and other advanced technologies. Additionally, the degree is becoming increasingly interdisciplinary, with courses in economics, psychology, and other social sciences being included in the curriculum. Finally, the degree is becoming more specialized, with courses focusing on specific industries such as healthcare, finance, and retail.