Mastering Data Science with Pandas
Mastering Data Science with Pandas, available at $39.99, with 49 lectures, and has 34 subscribers.
You will learn about Learn about data science tools How Pandas library works with it's building blocks Know a complete Panda's set of tools for data analysis, data manipulation and data visualization Practical examples including time series and the analysis of finantial market Learn advanced tools from Pandas such as Time Series, Text Manipulation, Regular expressions and more This course is ideal for individuals who are Beginners or intermediate users who are interested in develop a data science career or Data scientist students who want to consolidate previous knowledge or increase it It is particularly useful for Beginners or intermediate users who are interested in develop a data science career or Data scientist students who want to consolidate previous knowledge or increase it.
Enroll now: Mastering Data Science with Pandas
Summary
Title: Mastering Data Science with Pandas
Price: $39.99
Number of Lectures: 49
Number of Published Lectures: 49
Number of Curriculum Items: 49
Number of Published Curriculum Objects: 49
Original Price: $29.99
Quality Status: approved
Status: Live
What You Will Learn
- Learn about data science tools
- How Pandas library works with it's building blocks
- Know a complete Panda's set of tools for data analysis, data manipulation and data visualization
- Practical examples including time series and the analysis of finantial market
- Learn advanced tools from Pandas such as Time Series, Text Manipulation, Regular expressions and more
Who Should Attend
- Beginners or intermediate users who are interested in develop a data science career
- Data scientist students who want to consolidate previous knowledge or increase it
Target Audiences
- Beginners or intermediate users who are interested in develop a data science career
- Data scientist students who want to consolidate previous knowledge or increase it
Pandas is the most demanding python library for Data Science, it comes with a plethora of tools. It provides fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.
If you are planning to develop or improve your career in Data Science or Machine Learning, is a must to learn about Pandas.
This Course of Pandas offers a complete view of this powerful tool for implementing data analysis, data cleaning, data transformation, different data formats, text manipulation, regular expressions, data I/O, data statistics, data visualization, time series and more.
What you’ll see in the course?
– Data Series and Dataframes,
– Indexing and Multi Indexing,
– Range slicing,
– Group data by condition,
– Concat, Append, Join,
– Pandas and Categorical Data,
– One Hot Encoding,
– Explorative data analysis,
– Utility functions and custom functions,
– Data cleaning,
– Data visualization,
– Statistics,
– Text Manipulation,
– Regular expressions,
– Data transformation,
– Pivot Tables,
– Stack and Melt,
– Wide_to_long,
– Crosstab,
– Data I/O,
– Datetime functions,
– Time series and more.
This course is a practical course with many examples, because the easiest way to learn is practicing!, then we’ll integrate all the knowledge we have learned in a Capstone Project developing a preliminary analysis, cleaning, filtering, transforming and visualize data using the famous IMDB dataset.
Finally this course is continuously updated with Pandas Pro Features adding new lectures such as:
– Calendar Dates,
– Prettyfing Dataframes,
– Interactive Data Visualization,
– Interactive Data Table in Colab,
– Pandas SideTable,
– Web Scraping with Pandas,
– Sqlite databases with Pandas,
– Pandas and PySpark,
– Memory usage for large datasets with Pandas.
– Terality
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: Codes & Datasets
Lecture 3: Windows Setup – Anaconda
Lecture 4: Google Colaboratory
Chapter 2: Building Blocks
Lecture 1: Data Structures – Series
Lecture 2: Data Structures – Dataframes
Lecture 3: Indexing and Selecting
Lecture 4: Range Slicing
Lecture 5: Multi Indexing
Lecture 6: Group by – Part 1 – MultiIndex by Columns
Lecture 7: Group by – Part 2 – MultiIndex by Rows
Chapter 3: Pandas Tools
Lecture 1: Explorative Data Analysis (EDA)
Lecture 2: Utility Functions – Part 1
Lecture 3: Utility Functions – Part 2
Lecture 4: Custom Functions
Lecture 5: Data Cleaning
Lecture 6: Data Visualization – Part 1
Lecture 7: Data Visualization – Part 2
Lecture 8: Statistics
Lecture 9: Categorical data and One Hot Encoding
Lecture 10: Text Manipulation
Lecture 11: Regular Expressions
Lecture 12: Sorting and Filtering
Lecture 13: Concat
Lecture 14: Append
Lecture 15: Merge
Lecture 16: Pivot
Lecture 17: Stack-Melt
Lecture 18: Wide too long-Crosstab
Lecture 19: Data I/O – Part 1
Lecture 20: Data I/O – Part 2
Lecture 21: Datetime functions – Part 1
Lecture 22: Datetime functions – Part 2
Lecture 23: Time series
Lecture 24: Capstone Project – Part 1
Lecture 25: Capstone Project – Part 2
Lecture 26: End Lesson
Chapter 4: Pandas Pro Features
Lecture 1: Calendar Dates
Lecture 2: Prettyfing Dataframes part 1
Lecture 3: Prettyfing Dataframes part 2
Lecture 4: Interactive Data Visualization part 1
Lecture 5: Interactive Data Visualization part 2
Lecture 6: Interactive Data Table in Colab
Lecture 7: Pandas SideTable
Lecture 8: Web Scraping with Pandas
Lecture 9: SQLite with Pandas
Lecture 10: Pandas and PySpark
Lecture 11: Memory usage for large datasets with Pandas
Lecture 12: Terality
Instructors
-
CARLOS QUIROS
Industrial Engineer and Data Scientist
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