The Ultimate Python Library Pandas Training Course
The Ultimate Python Library Pandas Training Course, available at $64.99, has an average rating of 4.75, with 61 lectures, based on 15 reviews, and has 60 subscribers.
You will learn about 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 This course is ideal for individuals who are Data Scientists or Analysts or Python Developers or Anyone who wish to explore advanced data analysis and scientific computing techniques using pandas It is particularly useful for Data Scientists or Analysts or Python Developers or Anyone who wish to explore advanced data analysis and scientific computing techniques using pandas.
Enroll now: The Ultimate Python Library Pandas Training Course
Summary
Title: The Ultimate Python Library Pandas Training Course
Price: $64.99
Average Rating: 4.75
Number of Lectures: 61
Number of Published Lectures: 61
Number of Curriculum Items: 61
Number of Published Curriculum Objects: 61
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- Data Scientists
- Analysts
- Python Developers
- Anyone who wish to explore advanced data analysis and scientific computing techniques using pandas
Target Audiences
- Data Scientists
- Analysts
- Python Developers
- Anyone who wish to explore advanced data analysis and scientific computing techniques using pandas
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:
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Learn How to Access and load data from different sources using pandas
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Master the fundamentals of pandas to quickly begin exploring any dataset
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Isolate any subset of data by properly selecting and querying the data
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Work with a range of data types and structures to understand your data
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Split data into independent groups before applying aggregations and transformations to each group
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Restructure data into tidy form to make data analysis and visualization easier
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Perform data transformation to prepare it for analysis
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Prepare real-world messy datasets for machine learning
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Combine and merge data from different sources through pandas SQL-like operations
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Use Matplotlib for data visualization to create a variety of plots
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Create data models to find relationships and test hypotheses
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Manipulate time-series data to perform date-time calculations
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Utilize pandas unparalleled time series functionality
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Create beautiful and insightful visualizations through pandas direct hooks to Matplotlib and Seaborn
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Optimize your code to ensure more efficient business data analysis
At 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.
Course Curriculum
Chapter 1: Welcome
Lecture 1: Introduction
Chapter 2: Getting Started
Lecture 1: Introduction
Lecture 2: Understanding Why Using Pandas
Lecture 3: Learn About Series in Pandas
Lecture 4: Understanding Numpy
Lecture 5: Understanding Pandas Series
Lecture 6: Understanding Boolean Array and Index
Lecture 7: Understanding Pandas Data Types
Lecture 8: Understanding Pandas Series in Depth
Lecture 9: Introduction to Broadcasting
Lecture 10: Learning Crud – Read Series
Lecture 11: Learning Crud – Update & Delete Series
Lecture 12: Learning Pandas Series – Summary Statistics
Lecture 13: Duplicates in Pandas Series
Lecture 14: A Brief Intro to NaN
Lecture 15: Learn About Plot Series – Plotting in Pandas
Lecture 16: Section Summary
Chapter 3: Learning Pandas Dataframe
Lecture 1: Introduction
Lecture 2: Learning Dataframe – Learn About Similarities
Lecture 3: Learn How to Create a Dataframe
Lecture 4: Learn How to Create a Dataframe From CSV Files
Lecture 5: Understanding Selection
Lecture 6: Understanding Projection
Lecture 7: Understanding Product, Union & Difference
Lecture 8: Dataframes in Depth
Lecture 9: Dataframes – Learn About Statistics
Lecture 10: Creating Histograms & Transposing Data
Lecture 11: Learn How to Tweak Dataframes – 1
Lecture 12: Learn How to Tweak Dataframes – 2
Lecture 13: Learn How to Sort Data
Lecture 14: Learn About Iteration of Dataframes
Lecture 15: Learn How to Set Data
Chapter 4: Understanding Dataframes – Learning Joins & Filtering
Lecture 1: Introduction
Lecture 2: Learning Inner Join & Outer Join
Lecture 3: Learning Left Join & Right Join
Lecture 4: Learning Index Joins
Lecture 5: Learn How to Filter Dataframes – 1
Lecture 6: Learn How to Filter Dataframes – 2
Lecture 7: Learn How to Filter Dataframes – 3
Chapter 5: Understanding Grouping & Serialization
Lecture 1: Introduction
Lecture 2: Learn About Pivoting
Lecture 3: Learn About Stacking
Lecture 4: Learn About CSV IO
Lecture 5: Section Summary
Chapter 6: Learn How to Plot with Pandas
Lecture 1: Introduction
Lecture 2: Creating Histograms
Lecture 3: Learn About Bar Plots
Lecture 4: Learn About Line Plots
Lecture 5: Section Summary
Chapter 7: Dealing With Time
Lecture 1: Introduction
Lecture 2: Learn About Window Functions
Lecture 3: Learn About Plotting
Lecture 4: Machine Learning with Scikit-Learn
Chapter 8: Creating Infographics
Lecture 1: Introduction
Lecture 2: Learn How to Crawl Data
Lecture 3: Learn How to Munge Data
Lecture 4: Learn More Munging
Lecture 5: Learn How to Plot Data
Lecture 6: Section Summary
Chapter 9: Course Summary
Lecture 1: Summary
Chapter 10: Course Material & Source Code
Lecture 1: Course Material & Source Code
Instructors
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Karen Wallace
Data scientist & Passionate Programmer
Rating Distribution
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- 2 stars: 0 votes
- 3 stars: 0 votes
- 4 stars: 3 votes
- 5 stars: 12 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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