Data is everywhere and we always hear about statistics, even if we do not realize it! Over this course we will shape up our statistical knowledge; going from zero to hero analyzing complex patterns of everyday real-world problems.
Data is everywhere, from the newspaper you read on the subway to the report you are using to analyze yesterday's stock market performance. In this course, Interpreting Data with Statistical Models, you will gain the ability to effectively understand how to tackle problems that appear at your work, understand which is the right statistical analysis to use, and how to interpret the results to obtain insights. First, you will learn the very basics of statistics. Next, you will discover hypothesis testing to compare variables. Finally, you will explore how to make multiple comparisons and detect functional relationships with ANOVA and Regression. When you’re finished with this course, you will have the skills and knowledge of data analysis and statistical models needed to make your data speak for itself.
Axel Sirota has a Masters degree in Mathematics with a deep interest in Deep Learning and Development Lifecycle. After researching in Probability, Statistics and Machine Learning optimization, he is currently working at ASAPP as a Machine Learning Engineer leveraging Customer Experience conversations for making accurate predictions with Neural Networks.
Course Overview Hi everyone. My name is Axel Sirota. Welcome to my course, Interpreting Data with Statistical Models. I am a machine learning engineer at ASAPP, and I am very excited to present this to you. Data is everywhere, from the newspaper you read on the subway to the report you're analyzing about yesterday's top market performance. Over this course, we will start to give meaning to those charts and actually gaining the ability of making the real questions. We will go from zero to hero, analyzing complex patterns of everyday real-world problems. This journey begins at the very basic of statistics. From there, it will be a roller coaster of distributions and p-values, understanding how to make hypothesis testing, how to actually fit your data to a given hypothesis with chi squares, why ANOVA is such a key tool in comparing metrics, and finally, how to get that model to make your data speak for itself with linear regression. By the end of this course, you will be able to effectively understand how to tackle problems that appear at your work, understand which is the right statistical analysis to use, and how to interpret those results to obtain insights. I hope you will join me on this journey to learn how to interpret data with the Interpreting Data with Statistical Models course, at Pluralsight.