Language: English | Size: 980.72 MB | Duration: 2h 48m
This course will teach you how to tackle modern data problems and derive value from complex datasets using pandas.
What you'll learn
Up and running with pandas
Pandas and Data Science and Analysis
Representing univariate data with the Series
Representing tabular and multivariate data with the DataFrame
Manipulation and indexing of DataFrame objects
Indexing Data
Categorical Data
Numeric and Statistical Methods
Grouping and Aggregating Data
Combining, Relating and Reshaping Data
Requirements
Access to a computer with an internet connection
A PC or Mac
Description
Welcome to this course. The pandas library is massive, and it’s common for frequent users to be unaware of many of its more impressive features. Pandas is a popular Python library used by data scientists and analysts worldwide to manipulate and analyze their data. This course presents useful data manipulation techniques in pandas to perform complex data analysis in various domains. This course will teach you how to be more productive with data and generate real business insights to inform your decision-making. You will be guided through real-world data science problems and shown how to apply key techniques in the context of realistic examples and exercises. Engaging activities will then challenge you to apply your new skills in a way that prepares you for real data science projects.You’ll see how experienced data scientists tackle a wide range of problems using data analysis with pandas. You will learn how to use pandas to perform data analysis in Python. You will start with an overview of data analysis and iteratively progress from modeling data, to accessing data from remote sources, performing numeric and statistical analysis, through indexing and performing aggregate analysis, and finally to visualizing statistical data and applying pandas to finance.In this course, you'll learn:Learn How to Access and load data from different sources using pandasMaster the fundamentals of pandas to quickly begin exploring any datasetIsolate any subset of data by properly selecting and querying the dataWork with a range of data types and structures to understand your dataSplit data into independent groups before applying aggregations and transformations to each groupRestructure data into tidy form to make data analysis and visualization easierPerform data transformation to prepare it for analysisPrepare real-world messy datasets for machine learningCombine and merge data from different sources through pandas SQL-like operationsUse Matplotlib for data visualization to create a variety of plotsCreate data models to find relationships and test hypothesesManipulate time-series data to perform date-time calculationsUtilize pandas unparalleled time series functionalityCreate beautiful and insightful visualizations through pandas direct hooks to Matplotlib and SeabornOptimize your code to ensure more efficient business data analysisAt the end of this course, you’ll have the knowledge, skills, and confidence you need to solve your own challenging data science problems with pandas.
Overview
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