 # The Data Science Course 2020 Q2 Updated: Part 2

What you’ll learn
• Probability tells us how often some event will happen after many repeated trials, we start off with an introduction to this interesting topic.
• You will get more clarity and will learn hands-on by solving several examples on Probability
• You will learn insights on descriptive statistics
• You will learn the degrees of freedom, mathematical operations
• You will learn through hands-on examples on Properties of E(x)
• You will learn the Art of Data Visualization
• You will learn how to compile Histogram and Boxplot with examples
• You will learn through examples on how to create Boxplot and Scatter plot
• You will learn that Covariance gives idea about direction of the data with examples, you will also learn Correlation with examples
Requirements
• No prior experience is required. We will start from the very basics. You will benefit by going through our part 1 course which lays the foundation
Description

Building on the Foundation: Probability, Descriptive Statistics- Part2, Data Visualization, Covariance and Correlation

In this course we build your foundation on Data Science. Now that you have the insights and clarity on what data science is about after going through our Part 1 Course, we will take you through sessions on Probability, Descriptive Statistics, Data Visualization, Histogram, Boxplot & Scatter plot, You will learn that Covariance gives an idea about the direction of the data with examples, you will also learn Correlation with examples.

Probability is the mathematical term for the likelihood that something will occur, such as drawing an ace from a deck of cards or picking a green piece of candy from a bag of assorted colors. You use probability in daily life to make decisions when you don’t know for sure what the outcome will be. Most of the time, you won’t perform actual probability problems, but you’ll use subjective probability to make judgment calls and determine the best course of action.

Descriptive statistics consist of methods for organizing and summarizing information (Weiss, 1999). Descriptive statistics include the construction of graphs, charts, and tables, and the calculation of various descriptive measures such as averages, measures of variation, and percentiles.

Data visualization is a graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions.

Covariance and Correlation are two concepts in the field of probability and statistics. Both are concepts that describe the relationship between two variables to each other. Also, both are tools of measurement of a certain kind of dependence between variables.

Through our Four-part series we will take you step by step, this is our second part which will build your foundation.

Who this course is for:
• The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills
• You should take this course if you want to become a Data Scientist or if you want to learn about the field

Course content
Expand all 11 lectures11:55:38
Probability : An Introduction
59:35
Preview59:35
+Probability: Continued
1 lecture01:00:20
+Descriptive Statistics
1 lecture55:19
+Descriptive stats continued
1 lecture57:36
+Degrees of freedom
1 lecture01:15:06
+Random Variables
1 lecture01:11:09
+Random variables contd
1 lecture01:05:44
+Properties of E(x)
1 lecture01:09:00
+Histogram and Boxplot
1 lecture01:06:34
+Boxplot Contd and Scatter Plot
1 lecture01:13:07
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