Blog proposal

Big Data - Data Viz/proposal.md

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+**[Step-By-Step Technical Blog Guide](https://hq.bitproject.org/how-to-write-a-technical-blog/)**
+
+### :pushpin: Step 1
+**TITLE:**    
+Learning how to create a Tableau dashboard using COVID-19 data
+
+**TOPIC:**    
+Data Visualization
+
+**DESCRIPTION (5-7+ sentences):**    
+This technical blog will teach how to create a Tableau dashboard from scratch. It will cover how to connect to the dataset, how to create a sheet / dashboard, and how to make filters, date range filters, and bar graph / line graphs. This blog might cover SQL code, depending on the data and the process.
+
+### :pushpin: Step 2
+:family: **TARGET AUDIENCE (3-5+ sentences):**    
+The target audience is anyone who would like to learn Tableau. This tutorial will be useful for students taking Intro to Data Visualization class, or data analyst interns as Tableau is widely used in the industry. 
+
+### :pushpin: Step 3
+> Outline your learning/teaching structure: 
+
+**Beginning (2-3+ sentences):**    
+Explanation of what we are going to cover in the blog. I will explain the dataset and two different sheets I am going to create. 
+
+**Middle (2-3+ sentences):**    
+The first sheet will have a table with state, county, # cases, # deaths columns. In this tab, I will apply a county filter where users can select all or specific counties and I will use NY data only for simplicity. 
+
+The second sheet will have a bar graph or line graph indicating the number of confirmed cases in the states with most coronavirus cases, such as NY, NJ, IL, and CA. In this sheet, the audience will learn how to create a graph and apply a date range filter.
+
+**End (2-3+ sentences):**    
+Summarize the technical skills that we cover in the blog and provide additional resources if needed.

Intro_to_prob/blog.md

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+# Probability and Statistics
+
+## What is a probability? Why do we learn probability and statistics in Machine Learning?
+
+You are probably wondering why probability is a material in Computer Science and Machine Learning. But you are on the right page and here you are about to dive into the fundamental concepts of Machine Learning. Probability is the basis of lots of ML algorithms, such as Naive Bayes Algorithm and Expectation-Maximization algorithm, and much more.
+
+How much do we need to know to implement these algorithms? We can start off with two basic concepts, Conditional Probability and Joint Probability and we can expand our knowledge as we go!
+
+
+## Probability
+
+Probability is an extent to which an event is likely to occur. A probability as a number lies between 0 and 1; 0 means that an event will NEVER happen, and 1 means that an event WILL happen. A probability of 0.3 means an event would happen with a 30% chance and will not happen with a 70% chance.
+
+Often, we use a notation of P(A), which refers to the probability that event A will occur.
+
+<font color='blue'>Pop Quiz:</font>
+Suppose the probability that event B will occur is 0.25. Write this expression in a mathematical notation.
+
+
+
+## Conditional Probability
+
+Conditional probability is a measure of the probability of an event occurring given that another event has occurred. It’s denoted in a form of P(A|B), which refers to the probability that event A occurs given that event B has occurred.
+
+Suppose that the probability that Hannah would bring an umbrella when it rains is 0.85. Let U represent the event that Hannah brings an umbrella and let R represent the event that it rains outside. This statement can be expressed as P(U|R) = 0.85
+
+<font color='blue'>Pop Quiz:</font>
+Suppose the probability that event B occurs given that event C has occurred is 0.7. Write this expression in a mathematical notation.
+
+
+
+## Joint Probability
+
+P(A and B) refers to the probability that event A and event B both occur. In a mathematical notation, we often use P(A ∩ B).
+One thing to note is that P(A and B) = P(B and A) and P(A ∩ B) = P(B ∩ A).
+
+But, P(A and B) will be different depending on the dependence of the two events.
+
+If the outcome of an event is not dependent on the outcome of the other event, they are independent. 
+    P(A and B) = P(A) * P(B)
+
+If the outcome of an event affects the outcome of the other event as in the example of Hannah bringing an umbrella when it rains, they are dependent.
+    P(A and B) = P(A|B) * P(B)
+
+<font color='blue'>Pop Quiz:</font>
+Given the following probabilities, calculate the probability that both events R and U (It rains outside AND Hannah brings an umbrella) occur. 
+R: It rains outside. P(R) = 0.3
+U: Hannah brings an umbrella. P(U) = 0.1
+P(U|R) = 0.85
+
+
+## Summary
+In this chapter, you learned the basic concepts of probability, dependence and independence of events, conditional probability, and joint probability. Now you are ready to learn Bayes’ theorem, which uses all of these probabilities you just learned!
+
+To learn about Bayes’ theorem, here is a helpful resource!
+https://www.youtube.com/watch?v=XQoLVl31ZfQ
+

Intro_to_prob/proposal.md

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+# TEMPLATE
+
+## :fire: Do not edit this file - copy the template and create your own file.
+
+**[Step-By-Step Technical Blog Guide](https://hq.bitproject.org/how-to-write-a-technical-blog/)**
+
+### :pushpin: Step 1
+**TITLE:**    
+Why should we know Probability in Machine Learning?
+
+**TOPIC:**    
+Probability and Statistics
+
+**DESCRIPTION (5-7+ sentences):**    
+This technical blog covers probability, joint probability, and conditional probability. It also covers dependence / independence of events and how the dependency affects the calculation of joint probability. These are the basic concepts for Bayes’ rule and Bayes’ rule is widely used in Artificial Intelligence and Machine Learning. This blog provides definition, notation, example, and a pop-quiz for the audience to try. 
+
+
+### :pushpin: Step 2
+:family: **TARGET AUDIENCE (3-5+ sentences):**    
+Any students who are taking ML class and get stuck on the probability part. At least at my school, prob and stats class is not a prerequisite for an AI class and I have seen lots of friends struggling in the probability part. Target audience could be anyone who wants to start learning ML and does not have a probability and statistics background. 
+
+### :pushpin: Step 3
+> Outline your learning/teaching structure: 
+
+**Beginning (2-3+ sentences):**    
+start with why we should learn probability in ML and list out a few ML algorithms that use probability.
+
+**Middle (2-3+ sentences):**    
+Explain different types of probabilities in detail. Has simple and easy examples for students to follow and a pop-up quiz section.
+
+**End (2-3+ sentences):**    
+Summarize what we have learned and provide next steps. For the next steps, I provide a 5-min Youtube link that covers Bayes’ theorem.