Probability and Statistics: Complete Course 2023
Probability and Statistics: Complete Course 2023
Learn the Probability and Statistics You Need to Succeed in Data Science and Business Analytics
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This is course designed to take you from beginner to expert in probability and statistics. It is designed to be practical, hands on and suitable for anyone who wants to use statistics in data science, business analytics or any other field to make better informed decisions.
Videos packed with worked examples and explanations so you never get lost, and every technique covered is implemented in Microsoft Excel so that you can put it to use immediately.
Key concepts taught in the course are:

Descriptive Statistics: Averages, measures of spread, correlation and much more.

Cleaning Data: Identifying and removing outliers

Visualization of Data: All standard techniques for visualizing data, embedded in Excel.

Probability: Independent Events, conditional probability and Bayesian statistics.

Discrete Distributions: Binomial, Poisson, expectation and variance and approximations.

Continuous Distributions: The Normal distribution, the central limit theorem and continuous random variables.

Hypothesis Tests: Using binomial, Poisson and normal distributions, Ttests and confidence intervals.

Regression: Linear regression analysis, correlation, testing for correlation, nonlinear regression models.

Quality of Tests: Type I and Type II errors, power and size, phacking.

ChiSquared Tests: The chisquared distribution and how to use it to test for association and goodness of fit.

Much, much more!
It requires no prior knowledge, with the exception of 2 optional videos at the end of the continuous distribution chapter, in which knowledge of calculus is required).
Probability and Statistics: Complete Course  Udemy Course
Who this course is for:
 Data Scientists
 Business Analysts
 Business Students
 People studying Statistics
 Anyone looking to power their decision making with a thorough understanding of statistics.
What you'll learn
 Descriptive Statistics
 Visualizing Data
 Probability Theory
 Bayesian Statistics
 Discrete Distributions (Binomial, Poisson and More)
 Continuous Distributions (Normal and Others)
 Hypothesis Tests
 Regression
 Type I and Type II Errors
 ChiSquared Test
Requirements
 No prerequisites for most of the course. One small optional section requires knowledge and calculus, but other than that this is suitable for beginners.
Probability and Statistics: Complete Course  Udemy Discount
 Statistics and Probability by Khanacademy.
 Introduction to probability and data on Coursera.
 Data Science: Probability on edx.
 Mathematics for Machine Learning Specialisation by Imperial Collage London on Coursera.
 Learn Statistics with Numpy.
 Visualizing data, including bar graphs, pie charts, Venn diagrams, histograms, and dot plots.
 Analyzing data, including mean, median, and mode, plus range and IQR and boxandwhisker plots.
 Free. Stanford University. ...
 University of Michigan. Statistics with Python. ...
 Imperial College London. Mathematics for Machine Learning. ...
 Johns Hopkins University. Advanced Statistics for Data Science. ...
 IBM Skills Network. ...
 IBM Skills Network. ...
 Coursera Project Network. ...
 Free.
Introductorylevel course teaches students the basic concepts of statistics and the logic of statistical reasoning. Designed for students with no prior knowledge in statistics, its only prerequisite is basic algebra.
 An Intuitive Introduction to Probability: University of Zurich.
 Bayesian Statistics: University of California, Santa Cruz.
 Introduction to Statistics: Stanford University.
 Probability Theory: Foundation for Data Science: University of Colorado Boulder.
Statistics has gotten a reputation for being a very hard class, especially when taken in college, because it combines math concepts in order to form an analysis of a data set that can be used to understand an association in the data (whoo that was a mouthful).
Probability and Statistics: Complete Course  Udemy Deals
The probability of a certain event occurring depends on how many possible outcomes the event has. If an event has only one possible outcome, the probability for this outcome is always 1 (or 100 percent)
Probability theory (or stochastics) is the mathematical theory of randomness. It is a major research subject in pure mathematics where probability interacts with other fields, like partial differential equations, and real and complex analysis
A statistician is a person who works with theoretical or applied statistics. The profession exists in both the private and public sectors. It is common to combine statistical knowledge with expertise in other subjects, and statisticians may work as employees or as statistical consultants.
At an advanced level, statistics is considered harder than calculus, but beginnerlevel statistics is much easier than beginner calculus. Frankly, it mostly depends upon the student's interest as some students find it hard to comprehend statistics while others find it hard to understand calculus
If you choose to learn statistics on your own and devote six to eight hours a day to your studies, you can become a master statistician in just a couple of months. However, if you decide to enroll in a college degree program, it will take anywhere from two to four years, depending on your degree
Probability and statistics are bedfellows. One typically learn probability before building on that knowledge to learn statistics — and probability is the stairway to statistics. A strong understanding of statistics will also enhance one's appreciation of probability.
INSTRUCTOR
Woody Lewenstein