Learn Data Exploration Techniques and Data Visualization Tools Content of Data Exploration and Data Visualization : Data Exploration Techniques Descriptive Statistics Data Visualization Tools Exploratory Data Analysis Data Exploration Techniques Data exploration techniques are used to gain an understanding of the data and its characteristics. Some common data exploration techniques include: Summary Statistics : This involves calculating summary statistics such as mean, median, mode, variance, standard deviation, etc. These statistics provide a basic understanding of the data's central tendency, spread, and distribution. Histograms : Histograms are used to visualize the distribution of a numerical variable. They show the number of data points that fall into specific intervals or bins. Box Plots : Box plots show the distribution of a numerical variable and its median, quartiles, and outliers. They are useful for identifying pote
Undrstand the Probability and Statistics in Data Science Contents of P robability and Statistics Probability Basics Random Variables and Probability Distributions Statistical Inference (Hypothesis Testing, Confidence Intervals) Regression Analysis Probability Basics Solution : Sample Space = {H, T} (where H stands for Head and T stands for Tail) Solution : The sample space is {1, 2, 3, 4, 5, 6}. Each outcome is equally likely, so the probability distribution is: Hypothesis testing involves making a decision about a population parameter based on sample data. The null hypothesis (H0) is the hypothesis that there is no significant difference between a set of population parameters and a set of observed sample data. The alternative hypothesis (Ha) is the hypothesis that there is a significant difference between a set of population parameters and a set of observed sample data. The hypothesis testing process involves the following steps: Formulate the null and alternative hypo