Visualizations Contd
 Refer Here for the jupyter notebook, where we have explored matplotlib and seaborn visualizations
An Overview of Statistics
 Statistics is all about working with data, be it processing, analyzing or drawing a conclusion from the data we have.
 Statistics has two main goals
 describing the data
 drawing conclusions from it.
 These two goals coincide with two main categories of statistics
 descriptive statistics:
 Questions are asked about the general characteristics of a dataset (What is average price?, What is minimum value and max value)
 The answers to these questions (kind of) help us get an idea of what te dataset constitutes.
 inferential statistics:
 Goal is to go a step further: after gaining approximate insights from a given dataset. we’d like to use that information and infer on unknown data (Predictions for the future from observed data)
 This is typically done via various statistics and machine learning models.
 descriptive statistics:
Types of Data in Statistics

There are two main types of data:
 Categorical data
 Numerical data

Summary of Categorical and Numerical data
 Features  Categorical data  Numerical data 
 ——–  —————– ————– 
 Characteristic  Discrete Values  Continuous Values 
 Ordinality  No  yes 
 Models  Categorical/discrete probability distributions  Continuous Probability distributions 
 Data Processing  Onehot encoding  Scaling and Normalizations 
 Descriptive Stats  Mode  Mean and standard deviation 
 Predective Modeling  Classification  Regression 
 Visualization Techniques  Pie Charts and Bar graphs  Histograms, line graphs and Scatter Plots 