LEARNING “CHANCES” Part 2:
UNIT 7:
In this unit you will deep dive into probability and learn:
- Theoretical probability
- Experimental probability
- Compound probability
UNIT 8:
In this unit you will get a hold of concepts of :
- Counting and Permutations
- Permutations
- Combinations
You will understand how these ideas help us to find probabilities.
UNIT 9:
You will calculate probabilities of random variables and calculate expected value for different types of random variables .
You will a understanding of the concepts such as discrete random variables and continuous random variables and difference between .
What are the different ways to calculate them.
You will calculate mean and standard deviation of random variables.
UNIT 10:
In this unit you will learn about sampling distributions — ways to show every possible result if you’re taking a sample — help us to identify the different results we can get from repeated sampling, which helps us understand and use repeated samples.
UNIT 11:
In this unit you will learn what is the meaning of the term confidence intervals , what is it’s significance and ways to calculate it .
You will learn about margin of error and it’s two types:
- Margin of Error 1
- Margin of Error 2
You will learn the conditions for a z interval for a proportion and find critical value z for a desired confidence level .
UNIT 12:
This unit is all about hypothesis testing .
Learn how to conduct significance tests and calculate p-values to see how likely a sample result is to occur by random chance. You’ll also see how we use p-values to make conclusions about hypotheses.
You will learn how to write null and alternative hypothesis and you will also get introduced to types of errors and differences between them.
The final four units of this course delve into advanced topics like two-sample inference, comparing two means, the chi-square method, advanced regression, and more. However, I believe that it’s not necessary to invest too much time in these topics. Instead, one should consider beginning their journey by learning programming languages such as Python and its associated libraries.
In my opinion, it’s beneficial to maintain a balance. You can certainly continue your coursework in parallel with learning programming. After completing around 12 units and gaining a fundamental understanding of probability and statistics, it’s a good point to transition towards practical programming and model-building.
By doing so, you can apply the statistical knowledge you’ve acquired to real-world scenarios, which is often a more effective and rewarding way to learn. Ultimately, a combination of theory and hands-on practice will provide a strong foundation for your data science or analytics endeavors.
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