Foundations of Inference in Python

Starting at USD 39.00
4 hours duration

Get hands-on experience making sound conclusions based on data in this four-hour course on statistical inference in Python.

Gain a comprehensive understanding of hypothesis tests and their application in solving real-world problems with this four-hour course on the foundations of inference in Python. Learn how to transition from descriptive statistics to confident decision-making by analyzing data and making sound conclusions. Explore the importance of proper sampling and how improper sampling can affect statistical inference. In this course, you will delve into a broad range of scenarios, including hypothesis tests for normality and correlation, as well as parametric and non-parametric tests. Utilize the SciPy library to run these tests and interpret their output for effective decision-making. Additionally, you will learn how to measure the strength of an outcome using effect size and statistical power, while avoiding spurious correlations through the application of corrections. Furthermore, this course will equip you with the skills to work with big data by employing simulation, randomization, and meta-analysis techniques. You will gain the ability to re-analyze results from other researchers and draw solid conclusions from diverse datasets. Upon completion of this course, you will possess the expertise to utilize big data in making principled decisions that leaders can rely on. Move beyond basic graphs and summary statistics to produce reliable, repeatable, and explainable results.

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DataCamp is an online learning platform that offers interactive courses and tutorials for data science and analytics. It provides a wide range of courses covering topics such as Python, R, SQL, machine learning, data visualization, and more. The platform offers a hands-on learning experience through coding exercises and projects, allowing users to practice and apply their skills in real-world scenarios. DataCamp also offers a personalized learning experience with adaptive learning technology that adjusts the course content based on the user's skill level and progress. It is widely used by individuals, professionals, and organizations to enhance their data science skills and stay up-to-date with the latest trends and technologies in the field.
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