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[FreeTutorials.Us] [UDEMY] Data Processing with Python [FTU]

[FreeTutorials.Us] [UDEMY] Data Processing with Python [FTU]

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Description
For More Udemy Free Courses >>> https://freetutorials.us/

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Learn how to use Python and Pandas for cleaning and reorganizing huge amounts of data.



Created by: Ardit Sulce

Last updated: 10/2019

Language: English

Subs + Caption: Included

Torrent Contains: 148 Files, 12 Folders

Course Source: https://www.udemy.com/course/python-step-by-step-build-a-data-analysis-program/



What you'll learn



• Build 10 advanced Python scripts which together make up a data analysis and visualization program.

• Solve six exercises related to processing, analyzing and visualizing US income data with Python.

• Learn the fundamental blocks of the Python programming language such as variables, datatypes, loops, conditionals, functions and more.

• Use Python to batch download files from FTP sites, extract, rename and store remote files locally.

• Import data into Python for analysis and visualization from various sources such as CSV and delimited TXT files.

• Keep the data organized inside Python in easily manageable pandas dataframes.

• Merge large datasets taken from various data file formats.

• Create pivot tables in Python out of large datasets.

• Perform various operations among data columns and rows.

• Query data from Python pandas dataframes.

• Export data from Python into various formats such as TXT, CSV, Excel, HTML and more.

• Use Python to perform various visualizations such as time series, plots, heatmaps, and more.

• Create KML Google Earth files out of CSV files.



Course content

all 50 lectures 03:46:33



Requirements



• A working computer (Windows, Mac, or Linux)

• No prior knowledge of Python is required



Description



Data scientists spend only 20 percent of their time on building machine learning algorithms and 80 percent of their time finding, cleaning, and reorganizing huge amounts of data. That mostly happen because many use graphical tools such as Excel to process their data. However, if you use a programming language such as Python you can drastically reduce the time it takes for processing your data and make them ready for use in your project. This course will show how Python can be used to manage, clean, and organize huge amounts of data.



This course assumes you have basic knowledge of variables, functions, for loops, and conditionals. In the course you will be given access to a million records of raw historical weather data and you will use Python in every single step to deal with that dataset. That includes learning how to use Python to batch download and extract the data files, load thousands of files in Python via pandas, cleaning the data, concatenating and joining data from different sources, converting between fields, aggregating, conditioning, and many more data processing operations. On top of that, you will also learn how to calculate statistics and visualize the final data. The course also covers a series of exercises where you will be given some sample data then practice what you learned by cleaning and reorganizing those data using Python.



Who this course is for:



• Those who come from any technology field that deals with any kind of data.

• Those who want to leverage the power of the Python programming language for handling data.

• Those who need to learn Python basics and want to quickly advance their skills by learning how to perform data cleaning, analysis and visualization with Python - all in one single course.

• Those who want to switch from programming languages such as Java, C, R, Matlab, etc. to Python.








File list
  • [FreeTutorials.Us] [UDEMY] Data Processing with Python [FTU]
  • 0. Websites you may like/How you can help Team-FTU.txt 229 B
  • 1. Getting Started/1. Installing Python and Python libraries.mp4 104.3 MB
  • 1. Getting Started/1. Installing Python and Python libraries.srt 9.7 KB
  • 1. Getting Started/1. Installing Python and Python libraries.vtt 8.9 KB
  • 1. Getting Started/2. Python editors - Spyder and iPython.mp4 37.3 MB
  • 1. Getting Started/2. Python editors - Spyder and iPython.srt 4.2 KB
  • 1. Getting Started/2. Python editors - Spyder and iPython.vtt 3.8 KB
  • 10. Putting everything together/1. User interaction.mp4 23.6 MB
  • 10. Putting everything together/1. User interaction.srt 7.2 KB
  • 10. Putting everything together/1. User interaction.vtt 6.5 KB
  • 10. Putting everything together/2. Exercise User interaction.html 1.9 KB
  • 10. Putting everything together/3. Exercise User interaction Solution.html 711 B
  • 10. Putting everything together/4. Practice No. 9 Polishing the Program, I.mp4 27.3 MB
  • 10. Putting everything together/4. Practice No. 9 Polishing the Program, I.srt 6.1 KB
  • 10. Putting everything together/4. Practice No. 9 Polishing the Program, I.vtt 5.6 KB
  • 10. Putting everything together/5. Practice No. 10 Polishing the Program, II.mp4 38.4 MB
  • 10. Putting everything together/5. Practice No. 10 Polishing the Program, II.srt 6.3 KB
  • 10. Putting everything together/5. Practice No. 10 Polishing the Program, II.vtt 5.7 KB
  • 10. Putting everything together/6. Practice No. 11 Creating Python Modules.mp4 34 MB
  • 10. Putting everything together/6. Practice No. 11 Creating Python Modules.srt 5.8 KB
  • 10. Putting everything together/6. Practice No. 11 Creating Python Modules.vtt 5.3 KB
  • 11. Bonus Section Using Python in Jupyter Notebooks to Boost Productivity/1. Getting started with Jupyter Notebooks.mp4 32.2 MB
  • 11. Bonus Section Using Python in Jupyter Notebooks to Boost Productivity/1. Getting started with Jupyter Notebooks.srt 12.8 KB
  • 11. Bonus Section Using Python in Jupyter Notebooks to Boost Productivity/1. Getting started with Jupyter Notebooks.vtt 11.5 KB
  • 11. Bonus Section Using Python in Jupyter Notebooks to Boost Productivity/2. Data cleaning project, Part I.mp4 122.9 MB
  • 11. Bonus Section Using Python in Jupyter Notebooks to Boost Productivity/2. Data cleaning project, Part I.srt 9.5 KB
  • 11. Bonus Section Using Python in Jupyter Notebooks to Boost Productivity/2. Data cleaning project, Part I.vtt 8.6 KB
  • 11. Bonus Section Using Python in Jupyter Notebooks to Boost Productivity/2.1 thefts_and_pop.xls.xls 452 KB
  • 11. Bonus Section Using Python in Jupyter Notebooks to Boost Productivity/3. Data cleaning project, Part II.mp4 240.2 MB
  • 11. Bonus Section Using Python in Jupyter Notebooks to Boost Productivity/3. Data cleaning project, Part II.srt 19.9 KB
  • 11. Bonus Section Using Python in Jupyter Notebooks to Boost Productivity/3. Data cleaning project, Part II.vtt 17.9 KB
  • 11. Bonus Section Using Python in Jupyter Notebooks to Boost Productivity/4. Bonus Lecture.html 2.3 KB
  • 2. Downloading Many Files with Python/1. Section introduction.mp4 7.7 MB
  • 2. Downloading Many Files with Python/1. Section introduction.srt 1.9 KB
  • 2. Downloading Many Files with Python/1. Section introduction.vtt 1.8 KB
  • 2. Downloading Many Files with Python/2. Navigating through FTP directory trees with Python.mp4 21.7 MB
  • 2. Downloading Many Files with Python/2. Navigating through FTP directory trees with Python.srt 8.8 KB
  • 2. Downloading Many Files with Python/2. Navigating through FTP directory trees with Python.vtt 8 KB
  • 2. Downloading Many Files with Python/3. Storing Python code.mp4 18.2 MB
  • 2. Downloading Many Files with Python/3. Storing Python code.srt 5.7 KB
  • 2. Downloading Many Files with Python/3. Storing Python code.vtt 5.1 KB
  • 2. Downloading Many Files with Python/4. Creating an FTP function.mp4 9.3 MB
  • 2. Downloading Many Files with Python/4. Creating an FTP function.srt 3.1 KB
  • 2. Downloading Many Files with Python/4. Creating an FTP function.vtt 2.8 KB
  • 2. Downloading Many Files with Python/5. Downloading an FTP file.mp4 50.5 MB
  • 2. Downloading Many Files with Python/5. Downloading an FTP file.srt 9.7 KB
  • 2. Downloading Many Files with Python/5. Downloading an FTP file.vtt 8.8 KB
  • 2. Downloading Many Files with Python/5.1 FtpPDFDownloader.py.py 346 B
  • 2. Downloading Many Files with Python/6. About the next lecture.html 613 B
  • 2. Downloading Many Files with Python/7. Practice No.1 Creating an FTP File Downloader.mp4 54.9 MB
  • 2. Downloading Many Files with Python/7. Practice No.1 Creating an FTP File Downloader.srt 14.1 KB
  • 2. Downloading Many Files with Python/7. Practice No.1 Creating an FTP File Downloader.vtt 12.8 KB
  • 2. Downloading Many Files with Python/7.1 Ftp Downloader.py 805 B
  • 3. Extracting Data from Archive Files/1. Extracting ZIP, TAR, GZ and other archive file formats.mp4 41.9 MB
  • 3. Extracting Data from Archive Files/1. Extracting ZIP, TAR, GZ and other archive file formats.srt 3.8 KB
  • 3. Extracting Data from Archive Files/1. Extracting ZIP, TAR, GZ and other archive file formats.vtt 3.5 KB
  • 3. Extracting Data from Archive Files/1.1 Four Files Inside Extract Me.zip 48.3 KB
  • 3. Extracting Data from Archive Files/2. Extracting RAR files.mp4 8.6 MB
  • 3. Extracting Data from Archive Files/2. Extracting RAR files.srt 2.1 KB
  • 3. Extracting Data from Archive Files/2. Extracting RAR files.vtt 1.9 KB
  • 3. Extracting Data from Archive Files/3. Practice No.2 Creating a Batch Archive Extractor.mp4 32.3 MB
  • 3. Extracting Data from Archive Files/3. Practice No.2 Creating a Batch Archive Extractor.srt 6.7 KB
  • 3. Extracting Data from Archive Files/3. Practice No.2 Creating a Batch Archive Extractor.vtt 6.1 KB
  • 3. Extracting Data from Archive Files/3.1 Extractor.py 1.2 KB
  • 4. Working with TXT and CSV Files/1. Section introduction.mp4 14 MB
  • 4. Working with TXT and CSV Files/1. Section introduction.srt 1.9 KB
  • 4. Working with TXT and CSV Files/1. Section introduction.vtt 1.7 KB
  • 4. Working with TXT and CSV Files/2. Reading delimited TXT and CSV files.mp4 27.2 MB
  • 4. Working with TXT and CSV Files/2. Reading delimited TXT and CSV files.srt 11.4 KB
  • 4. Working with TXT and CSV Files/2. Reading delimited TXT and CSV files.vtt 10.4 KB
  • 4. Working with TXT and CSV Files/2.1 Comma Separated.txt 33.6 KB
  • 4. Working with TXT and CSV Files/2.2 Space Separated.txt 34.3 KB
  • 4. Working with TXT and CSV Files/3. Reading Excel files.html 612 B
  • 4. Working with TXT and CSV Files/4. Exporting data from Python to files.mp4 21.1 MB
  • 4. Working with TXT and CSV Files/4. Exporting data from Python to files.srt 4.8 KB
  • 4. Working with TXT and CSV Files/4. Exporting data from Python to files.vtt 4.4 KB
  • 4. Working with TXT and CSV Files/5. Reading fixed width TXT files.mp4 9 MB
  • 4. Working with TXT and CSV Files/5. Reading fixed width TXT files.srt 2.3 KB
  • 4. Working with TXT and CSV Files/5. Reading fixed width TXT files.vtt 2.1 KB
  • 4. Working with TXT and CSV Files/5.1 Fixed Width.txt 2.8 MB
  • 4. Working with TXT and CSV Files/6. Exporting data back to HTML and other file formats.mp4 11.7 MB
  • 4. Working with TXT and CSV Files/6. Exporting data back to HTML and other file formats.srt 1.3 KB
  • 4. Working with TXT and CSV Files/6. Exporting data back to HTML and other file formats.vtt 1.2 KB
  • 4. Working with TXT and CSV Files/7. Data Analysis Exercise 1.html 11.6 KB
  • 4. Working with TXT and CSV Files/7.1 Income-By-State-1984.xls.xls 26 KB
  • 4. Working with TXT and CSV Files/8. Data Analysis Exercise 1 Solution.html 255 B
  • 5. Getting Started with Pandas/1. Get started with Pandas.mp4 25.5 MB
  • 5. Getting Started with Pandas/1. Get started with Pandas.srt 7.6 KB
  • 5. Getting Started with Pandas/1. Get started with Pandas.vtt 6.9 KB
  • 5. Getting Started with Pandas/1.1 SmallFile.csv.csv 264 B
  • 5. Getting Started with Pandas/2. Practice No.3 Calculating and Adding Columns to CSV Files.mp4 26.5 MB
  • 5. Getting Started with Pandas/2. Practice No.3 Calculating and Adding Columns to CSV Files.srt 5.4 KB
  • 5. Getting Started with Pandas/2. Practice No.3 Calculating and Adding Columns to CSV Files.vtt 5 KB
  • 5. Getting Started with Pandas/2.1 Add Field.py 1.6 KB

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