· What you will learn
· Develop Python code for cleaning and preparing data for analysis – including handling missing values, formatting, normalizing, and binning data
· Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas & Numpy.
· Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines
· Data Wrangling
In this module, you will learn how to perform some fundamental data wrangling tasks that, together, form the pre-processing phase of data analysis. These tasks include handling missing values in data, formatting data to standardize it and make it consistent, normalizing data, grouping data values into bins, and converting categorical variables into numerical quantitative variables.
· Exploratory Data Analysis
· In this module, you will learn what is meant by exploratory data analysis, and you will learn how to perform computations on the data to calculate basic descriptive statistical information, such as mean, median, mode, and quartile values, and use that information to better understand the distribution of the data. You will learn about putting your data into groups to help you visualize the data better, you will learn how to use the Pearson correlation method to compare two continuous numerical variables, and you will learn how to use the Chi-square test to find the association between two categorical variables and how to interpret them.
