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Modern Data Wrangling with AI and Python – Beginner to Pro

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Learn how to streamline your data processing and analysis with the power of AI and Python. From beginner to pro.


1 - Welcome
  • 1 - Welcome to the course
  • 2 - Meet Gerhard
  • 3 - Is this course for you
  • 4 - What will you get out of this course
  • 5 - Whats in this course

  • 2 - Getting Started
  • 6 - Introduction
  • 7 - Getting started with Visual Studio Code.txt
  • 7 - Install Visual Studio Code.txt
  • 7 - VSC Key Features.txt
  • 7 - Visual Studio Code Installing and getting Started
  • 8 - Adding Extensions to VSC
  • 9 - Getting Started with VSC copilot.txt
  • 9 - GitHub copilot
  • 10 - Create a Github account
  • 11 - GitHub Copilot chat
  • 12 - Getting started with GitHub copilot.txt
  • 12 - GitHub Copilot continued
  • 13 - Testing if Copilot is working
  • 14 - Github copilot help content.html
  • 15 - Anaconda
  • 15 - Install Anaconda.txt
  • 16 - Github Desktop.txt
  • 16 - Installing Github Desktop
  • 17 - Getting started with Github Desktop.txt
  • 17 - The GitHub Desktop Interface
  • 18 - Code for the course.txt
  • 18 - The repository for this course
  • 19 - GIT optional
  • 19 - Install GIT.txt
  • 20 - Summary

  • 3 - Data Exploration What can you tell me about the data
  • 21 - Introduction
  • 22 - A spreadsheet program
  • 22 - sales-data-sample.csv
  • 23 - The challenge with spreadsheets
  • 24 - Data Wrangling Course Repo.txt
  • 24 - Follow along with me
  • 25 - Welcome to Python and AI
  • 25 - getting-the-data-you-need.zip
  • 26 - Markdown and Code fields in Jupyter
  • 26 - Markdown in Jupyter.txt
  • 26 - Notebook basics.txt
  • 27 - What is Pandas
  • 28 - Read a file in Pandas and display it
  • 29 - Describe the data
  • 30 - Do more with Copilot Jupyter and Python
  • 31 - Summary

  • 4 - Enough Python to start your journey
  • 32 - Introduction
  • 33 - Libraries
  • 34 - More Pandas
  • 34 - Pandas Getting Started.txt
  • 34 - readcsv.txt
  • 35 - Objects
  • 36 - Getting help from Large Language Models
  • 36 - What is a Library in Python.txt
  • 37 - Methods
  • 38 - Summary

  • 5 - My journey to data wrangling
  • 39 - My journey

  • 6 - Structured Data Tables
  • 40 - Introduction
  • 41 - Structured Data
  • 42 - Structured Data in Python
  • 43 - CSV files as structured data
  • 44 - Excel data as structured data
  • 45 - General methods for structured data Excel and CSV
  • 46 - SQL Tables
  • 47 - SQL data in Python
  • 48 - Summary

  • 7 - More on Python functions properties and some other goodies
  • 49 - Introduction
  • 50 - Functions
  • 51 - Function signatures
  • 52 - Function bodies
  • 53 - Using fucnctions
  • 54 - A function in action
  • 55 - Properties
  • 56 - Properties in code
  • 57 - For loops
  • 58 - For loops in code
  • 59 - Another example of for loops
  • 60 - Getting help Docstrings and signatures
  • 61 - Getting help online documentation
  • 62 - Getting help LLMs
  • 62 - OpenPyXL.txt
  • 63 - Getting help Github copilot
  • 64 - Summary

  • 8 - Unstructured Data
  • 65 - Introduction
  • 66 - Installing Tabula in condas
  • 67 - Tables in PDF documents
  • 68 - Extracting a table from a PDF with Python
  • 69 - Accessing particular cells in a dataframe
  • 70 - Rename the columns of a dataframe
  • 71 - Rename columns 2 and 3 of the dataframe
  • 72 - Rename the remainder of the columns and concatenate strings
  • 73 - Delete rows from a dataframe
  • 74 - Split values in a column
  • 75 - Drop columns in a dataframe
  • 76 - PDFs with text
  • 77 - Extract text from PDFs using Python
  • 78 - Summary

  • 9 - Web services or Application Web InterfacesAPIs
  • 79 - Introduction
  • 80 - Web services or Application Web InterfacesAPIs
  • 81 - Cat facts API.txt
  • 81 - JSON View.txt
  • 81 - The cat facts API
  • 82 - HTTP response status codes
  • 82 - HTTP response status codes.txt
  • 83 - JSON payloads
  • 84 - More on JSON
  • 85 - Import API call into Postman
  • 86 - Making an API call in Postman
  • 87 - Generate code in Postman
  • 88 - Execute Postman code in a Jupyter Notebook
  • 89 - Querying JSON objects in Python
  • 90 - Accessing lists and nested values in JSON
  • 91 - Converting JSON to Dataframes
  • 92 - Summary

  • 10 - Lists dictionaries and data types in Python
  • 93 - Introduction
  • 94 - Lists
  • 94 - List documentation.txt
  • 94 - List methods.txt
  • 95 - More than numbers
  • 96 - Lists were only scratching the surface
  • 97 - Lists and dictionaries in VSC
  • 98 - Lists in action
  • 99 - Dictionaries
  • 100 - Dictionaries in action
  • 101 - Data types
  • 102 - Data types in Jupyter
  • 103 - Lambdas
  • 104 - Lambdas in action
  • 105 - Conclusion

  • 11 - How do make your data useful Structuring
  • 106 - Introduction
  • 107 - Example dataset A Canadian manufacturing company
  • 108 - A data dictionary
  • 109 - REFDATE changing the data type of a column
  • 110 - GEO getting the number of unique values in a column
  • 111 - Dropping a column in a dataframe
  • 112 - DGUID Renaming and finding the meaning of a column
  • 113 - Principal statistics Filtering data
  • 114 - Dropping mor than one column UAM and UAMID
  • 115 - NAICS VECTOR and COORDINATE grouping by more than one column
  • 116 - Status Getting the number of unique values in a column and it the dataframe
  • 117 - Exporting the structured transformations to a CSV file
  • 118 - Repeating the work weve done
  • 119 - Python datetime.txt
  • 119 - The datatime data type
  • 119 - todatetime.txt
  • 120 - New methods used
  • 121 - Filtering
  • 122 - Adding a new column to a data frame
  • 123 - Summary

  • 12 - How do I clean data
  • 124 - Introduction
  • 125 - The Netflix dataset
  • 126 - Converting data and extracting digits from columns
  • 127 - Missing rows in strings
  • 128 - Replacing missing values in strings
  • 129 - Replacing missing values in numbers
  • 130 - Dropping missing rows
  • 131 - Identifying and dropping duplicate rows
  • 132 - Extracting numbers out of strings
  • 133 - Getting parts of a string slicing and substrings
  • 134 - Getting the end of a string and finding help
  • 135 - Getting words out of a string splitting
  • 136 - Advanced string extraction regular expressions
  • 137 - Getting help with regular expressions
  • 138 - Applying functions to strings mapping
  • 139 - Summary

  • 13 - Enrichment Making data valuable
  • 140 - Introduction
  • 141 - Columns in dataframes series
  • 142 - Getting rows by their number indexes
  • 143 - Combining data the concat function
  • 144 - Adding columns together using the concat function
  • 145 - Combining data by the same column name merge
  • 146 - Understanding joins
  • 147 - Left join returning all the rows in the left table
  • 148 - Right join all the rows in the right table
  • 149 - Outer join all the rows in both tables
  • 150 - Joining tables by index the Join method
  • 151 - Adding a new row to the dataframe
  • 152 - Removing the duplicates
  • 153 - Adding multiple rows to a dataframe
  • 154 - Changing the value of existing rows update
  • 155 - Updating rows based on a column setting the indexes
  • 156 - Updating a dataframe with merge
  • 157 - Summary

  • 14 - Validation Making sure the data is correct
  • 158 - Introduction
  • 159 - The characteristics of good data
  • 160 - Data lacking quality cause PR nightmares
  • 161 - Data accuracy
  • 162 - Getting help from GitHub copilot chat
  • 163 - Identifying duplicate rows reminder
  • 164 - Checking for missing valuesreminder
  • 165 - Data completeness
  • 166 - Data consistancy
  • 167 - Data reliability
  • 168 - Data relevance
  • 169 - Data timeliness
  • 170 - Summary

  • 15 - Publishing Querying and Presenting the data
  • 171 - Introduction
  • 172 - What is data publishing
  • 173 - Using Faker to generate fake data
  • 174 - Querying the data
  • 175 - Getting the total and average revenue by product
  • 176 - Displaying revenue by product in a chart
  • 177 - Use Matplotlib to generate a scattered plot
  • 178 - Displaying data over time using Matplotlib and Pandas
  • 179 - Use Seaborn for heatmaps
  • 180 - Exporting results to PDF
  • 181 - Exporting results to Excel
  • 182 - Exporting to CSV
  • 183 - Summary

  • 16 - Conclusion
  • 184 - Where to from here
  • 185 - Congratulations
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    مدت زمان: 234 دقیقه
    تاریخ انتشار: 22 دی 1402

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