This course provides an overview of modern applied statistics. It will cover an introduction to quantitative research design, exploratory data analysis and data visualisation, generalised linear ...

Statistical modeling lies at the heart of data science. Well-crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In ...

Data modeling is the framework that lets data analysis use data for decision-making. A combined approach is needed to maximize data insights.

Business analytics is the practical application of statistical analysis and technologies on business data to identify trends and predict business outcomes.

Quantitative analysis (QA) seeks to understand behavior by using mathematical and statistical modeling, measurement, and research.

You'll grasp the significance of Python in data science and master programming, data manipulation, visualization, and statistical analysis for building machine learning models.

When used inappropriately, statistical models may give rise to misleading conclusions. Checking that a given model is an appropriate representation of the data is therefore an important step.

Predictive analytics is the use of statistics and modeling techniques to determine future performance based on current and historical data.

Data analytics is a discipline focused on extracting insights from data, including the analysis, collection, organization, and storage of data, as well as the tools and techniques to do so.

This course provides an overview of modern applied statistics. It will cover an introduction to quantitative research design, exploratory data analysis and data visualisation, generalised linear ...