Study Applied Statistics
What Is Applied Statistics?
- Data scientist
- Data analyst
- Quality engineer
- Statistical engineer
- Validation engineer
- Risk analyst
- Financial analyst
- Quantitative analyst
- Actuarial director
- Financial crimes analyst
- Compliance officer
- Machine learning researcher
- Intelligent automation associate
- Statistical programmer
- Data architect
- Marketing analyst
- Business analyst
- Marketing research manager
- Clinical informatics specialist
- Health research analyst
- Statistical scientist
- Cognitive AI data scientist
Study Applied Statistics with a Master’s in Data Science
Applied Statistics Course
- Introduction to Data Science Programming: This course is an introduction to the Python programming language. It highlights a range of Python objects and control structures.
- Research Design and Application for Data and Analysis: This course introduces students to the burgeoning data science landscape, with a particular focus on learning how to apply data science reasoning techniques to uncover, enrich, and answer questions facing decision makers across various industries and organizations.
- Statistics for Data Science: This course provides students with an understanding of many different types of quantitative research methods and statistical techniques for analyzing data.
- Fundamentals of Data Engineering: This course delves into the fundamentals of data storage, retrieval, and processing systems in the context of common data analytics processing needs.
- Applied Machine Learning: This course covers the rapidly growing machine learning field. The goal of this course is to provide a broad introduction to the key ideas in machine learning through intuition and practical examples.
- Experiments and Causal Inference: This course introduces students to experimentation and design-based inference. Students are taught to collect data in a way that’s creative and forward looking.
- Machine Learning at Scale: This course teaches students how to apply crucial machine learning techniques to solve problems, run evaluations and interpret results, and understand scaling up from thousands of data points to billions.
- Data Visualization: This course focuses on the design of visual representations of data in order to discover patterns, answer questions, convey findings, drive decisions, and provide persuasive evidence. Students in this course gain the practical knowledge needed to create effective tools for both exploring and explaining data.
- Statistical Methods for Discrete Response, Time Series, and Panel Data: This course takes a more advanced look at both classical linear and linear regression models, including techniques for studying causality, and introduces the fundamental techniques of time series modeling.