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Essential Statistics for Data Science: A Concise Crash Course is for students entering a serious graduate program or advanced undergraduate teaching in data science without knowing enough statistics. The three part text introduces readers to the basics of probability and random variables and guides them towards relatively advanced topics in both frequentist and Bayesian in a matter of weeks.Part I, Talking Probability explains the statistical approach to analysing data with a probability model to describe the data generating process. Part II, Doing Statistics demonstrates how the unknown quantities in data i.e. it's parameters is applicable in statistical interference. Part III, Facing Uncertainty explains the importance of explicity describing how much uncertainty is caused by parameters with intrinsic scientific meaning and how to take that intoaccount when making decisions.Essential Statistics for Data Science: A Concise Crash Course provides an in-depth introduction for beginners, while being more focused than a typical undergraduate text, but still lighter and more accessible than an average graduate text.
At the frontier of statistics, Data Science, or Machine Learning, the probability models used to describe the data-generating process can be pretty complex. Most of those which we will encounter in this book will, of course, be much simpler. However, whether the models are complex or simple, this particular characterization of what statistics is about is very important and also why, in order to study statistics at any reasonable depth, it is necessary to become reasonably proficient in the language of probability.
Prologue
PART I . TALKING PROBABILIT Y
1. Eminence of Models
2. Building Vocabulary
3. Gaining Fluency
PART I I . DOING STATISTICS
4. Overview of Statistics
5. Frequentist Approach
6. Bayesian Approach
PART I I I . FACING UNCERTAINT Y
7. Interval Estimation
PART IV. APPENDIX
Appendix: Some Further Topics