oaxino
28 noviembre 2020, 14:17
https://i114.fastpic.ru/big/2020/1128/db/913a444e367129383c8d882d76eadadb.jpg
Data Science using Pandas (from basics)
Duration: 4h19m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 2.23 GB
Genre: eLearning | Language: English
Hands-on with Pandas
What you'll learn
The students will learn what is DataScience, how to do data processing using Pandas (with Python)
Requirements
Python language
Description
The following topics are covered:
What is Data Science
How to get (real) stock data , how to Description data in Jupyter
SQL and MySQL interaction using Pandas
read csv file and do various operations. Stocks data is considered as example
write csv file and associated operations
Handling missing data,
Data filtering / Wrangling using Pandas
Group by support in Pandas
Concat support in Pandas
Merge of Dataframes
Pivot Table support in Pandas
Stack / Unstack support in Pandas
Reshape with Pandas
Cross Tab with Pandas
excel support
Time series Support for calendar ( Business Days, Holidays)
Time series Support for resampling, indexing time series data
Hands-on / Practical with various datasets like: stock /share data, weather data etc
Who this course is for:
Beginner Python developers, Data Science students, Students who have some exposure to data analytics
This course is meant typically for those who want to learn Data Science and who have some exposure to python programming, like freshers.
Download link:
***Contenido oculto. Abra la versión completa del tema para visualizar los enlaces.***
Data Science using Pandas (from basics)
Duration: 4h19m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 2.23 GB
Genre: eLearning | Language: English
Hands-on with Pandas
What you'll learn
The students will learn what is DataScience, how to do data processing using Pandas (with Python)
Requirements
Python language
Description
The following topics are covered:
What is Data Science
How to get (real) stock data , how to Description data in Jupyter
SQL and MySQL interaction using Pandas
read csv file and do various operations. Stocks data is considered as example
write csv file and associated operations
Handling missing data,
Data filtering / Wrangling using Pandas
Group by support in Pandas
Concat support in Pandas
Merge of Dataframes
Pivot Table support in Pandas
Stack / Unstack support in Pandas
Reshape with Pandas
Cross Tab with Pandas
excel support
Time series Support for calendar ( Business Days, Holidays)
Time series Support for resampling, indexing time series data
Hands-on / Practical with various datasets like: stock /share data, weather data etc
Who this course is for:
Beginner Python developers, Data Science students, Students who have some exposure to data analytics
This course is meant typically for those who want to learn Data Science and who have some exposure to python programming, like freshers.
Download link:
***Contenido oculto. Abra la versión completa del tema para visualizar los enlaces.***