Introduction to Statistics | 365 Data Science Courses
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Statistics is the driving force in any quantitative career. It is the fundamental skill data scientists need to be able to understand and design statistical tests and analyses performed by modern software packages and programming languages. In this course, we start from the very basics of statistics and gradually build up your statistical thinking, enabling you to understand the more complex analyses carried out later in the program.
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Video Timestamps:
0:00 What does the course cover?
02:20 Population vs Sample
06:23 Types of data and levels of measurement
10:55 Levels of measurement
14:27 Categorical variables. Visualization techniques
19:00 Numerical variables. Frequency distribution table
22:10 The histogram
24:24 Cross table and scatter plot
29:02 Mean, median, mode
33:15 Skewness
35:53 Variance
41:46 Standard deviation and coefficient of variation
46:27 Covariance
49:49 Correlation
53:08 Practical Example
We will discuss why you need to learn statistics, and which are the key skills you will acquire by taking the course. You will learn how to distinguish between the different types of data and levels of measurement. This will help you when calculating the measures of central tendency (mean, median, and mode) and dispersion indicators such as variance and standard deviation, as well as measures of the relationship between variables like covariance and correlation. To reinforce what you have learned, we will wrap up this section with a hands-on practical example.
Also, you will learn what a distribution is and what characterizes the normal distribution. We will introduce you to the central limit theorem and to the concept of standard error.
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