Modern Data Wrangling With Ai And Python - Beginner To Pro

Modern Data Wrangling With Ai And Python - Beginner To Pro - Printable Version

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Modern Data Wrangling With Ai And Python - Beginner To Pro - BaDshaH - 10-17-2023

[Image: e3450f45c8cb8b6a6c19e54c04abef7f.jpg]

Published 10/2023
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.77 GB | Duration: 3h 54m

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

[b]What you'll learn[/b]
Use AI and Python to increase the effectiveness of performing data-related tasks
Employ data wrangling to get to the answers, latent in data, quicker and more accurartely
Reduce the complexity and tedium of maintaining data products, such as adding new rows to a spreadsheet
Publish work done on data for other users to consume
Move from being frustrated with the limitations of spreadsheets to using Python with confidence
Understand the importance and benefits of data wrangling as part of the data lifecycle

[b]Requirements[/b]
No programming experience needed but a familiarity with spreadsheets or database systems will be helpful
A willingness to take your career to the next level by learning 3 things: data wrangling, AI and Python

[b]Description[/b]
Welcome to "Modern Data Wrangling with AI and Python: From Beginner to Pro." This comprehensive course is designed to equip participants with the essential skills and knowledge to effectively wrangle and manipulate data using the power of Python and integrate cutting-edge AI techniques.In today's data-driven world, the ability to wrangle and process data efficiently is fundamental for successful decision-making, predictive modelling, and gaining valuable insights. This course is structured to take learners on a journey from the basics of data wrangling to advanced AI-powered data manipulation, enabling them to become proficient practitioners in the field.This course begins by showing you how to install all the modern tools required for data wrangling. Next, we dive right into data exploration. We immediately start coding. After we've written our first code, we go over the concepts in a theoretical section. After data exploration, we cover structured data. After structured data comes unstructured data, including how to work with PDF files in data wrangling. Next, we cover, semi-structured data and web services, or APIs. In the structuring section, we try to answer the question - how do I make my data more useful? Next, we look at cleaning up our data. After we've cleaned up the data, we learn how to enrich data to make it more valuable. After enrichment comes data validation and we wrap up with publishing.In total, there are more than 180 videos in this course and you'll be well-versed in data wrangling once you've completed it.

Overview
Section 1: Welcome
Lecture 1 Welcome to the course
Lecture 2 Meet Gerhard
Lecture 3 Is this course for you?
Lecture 4 What will you get out of this course?
Lecture 5 What's in this course?
Section 2: Getting Started
Lecture 6 Introduction
Lecture 7 Visual Studio Code - Installing and getting Started
Lecture 8 Adding Extensions to VSC
Lecture 9 GitHub copilot
Lecture 10 Create a Github account
Lecture 11 GitHub Copilot chat
Lecture 12 GitHub Copilot continued
Lecture 13 Testing if Copilot is working
Lecture 14 Github copilot help content
Lecture 15 Anaconda
Lecture 16 Installing Github Desktop
Lecture 17 The GitHub Desktop Interface
Lecture 18 The repository for this course
Lecture 19 GIT (optional)
Lecture 20 Summary
Section 3: Data Exploration - What can you tell me about the data?
Lecture 21 Introduction
Lecture 22 A spreadsheet program
Lecture 23 The challenge with spreadsheets
Lecture 24 Follow along with me
Lecture 25 Welcome to Python (and AI)
Lecture 26 Markdown and Code fields in Jupyter
Lecture 27 What is Pandas?
Lecture 28 Read a file in Pandas and display it
Lecture 29 Describe the data
Lecture 30 Do more with Copilot, Jupyter and Python
Lecture 31 Summary
Section 4: Enough Python to start your journey
Lecture 32 Introduction
Lecture 33 Libraries
Lecture 34 More Pandas
Lecture 35 Objects
Lecture 36 Getting help from Large Language Models
Lecture 37 Methods
Lecture 38 Summary
Section 5: My journey to data wrangling
Lecture 39 My journey
Section 6: Structured Data - Tables
Lecture 40 Introduction
Lecture 41 Structured Data
Lecture 42 Structured Data in Python
Lecture 43 CSV files as structured data
Lecture 44 Excel data as structured data
Lecture 45 General methods for structured data - Excel and CSV
Lecture 46 SQL Tables
Lecture 47 SQL data in Python
Lecture 48 Summary
Section 7: More on Python - functions, properties and some other goodies
Lecture 49 Introduction
Lecture 50 Functions
Lecture 51 Function signatures
Lecture 52 Function bodies
Lecture 53 Using fucnctions
Lecture 54 A function in action
Lecture 55 Properties
Lecture 56 Properties in code
Lecture 57 For loops
Lecture 58 For loops in code
Lecture 59 Another example of for loops
Lecture 60 Getting help - Docstrings and signatures
Lecture 61 Getting help - online documentation
Lecture 62 Getting help - LLMs
Lecture 63 Getting help - Github copilot
Lecture 64 Summary
Section 8: Unstructured Data
Lecture 65 Introduction
Lecture 66 Installing Tabula in condas
Lecture 67 Tables in PDF documents
Lecture 68 Extracting a table from a PDF with Python
Lecture 69 Accessing particular cells in a dataframe
Lecture 70 Rename the columns of a dataframe
Lecture 71 Rename columns 2 and 3 of the dataframe
Lecture 72 Rename the remainder of the columns and concatenate strings
Lecture 73 Delete rows from a dataframe
Lecture 74 Split values in a column
Lecture 75 Drop columns in a dataframe
Lecture 76 PDFs with text
Lecture 77 Extract text from PDFs, using Python
Lecture 78 Summary
Section 9: Web services or Application Web Interfaces(APIs)
Lecture 79 Introduction
Lecture 80 Web services or Application Web Interfaces(APIs)
Lecture 81 The cat facts API
Lecture 82 HTTP response status codes
Lecture 83 JSON payloads
Lecture 84 More on JSON
Lecture 85 Import API call into Postman
Lecture 86 Making an API call in Postman
Lecture 87 Generate code in Postman
Lecture 88 Execute Postman code in a Jupyter Notebook
Lecture 89 Querying JSON objects in Python
Lecture 90 Accessing lists and nested values in JSON
Lecture 91 Converting JSON to Dataframes
Lecture 92 Summary
Section 10: Lists, dictionaries and data types in Python
Lecture 93 Introduction
Lecture 94 Lists
Lecture 95 More than numbers
Lecture 96 Lists - we're only scratching the surface
Lecture 97 Lists and dictionaries in VSC
Lecture 98 Lists in action
Lecture 99 Dictionaries
Lecture 100 Dictionaries in action
Lecture 101 Data types
Lecture 102 Data types in Jupyter
Lecture 103 Lambdas
Lecture 104 Lambdas in action
Lecture 105 Conclusion
Section 11: How do make your data useful - Structuring
Lecture 106 Introduction
Lecture 107 Example dataset - A Canadian manufacturing company
Lecture 108 A data dictionary
Lecture 109 REF_DATE - changing the data type of a column
Lecture 110 GEO - getting the number of unique values in a column
Lecture 111 Dropping a column in a dataframe
Lecture 112 DGUID - Renaming and finding the meaning of a column
Lecture 113 Principal statistics - Filtering data
Lecture 114 Dropping mor than one column - UAM and UAM_ID
Lecture 115 NAICS, VECTOR and COORDINATE - grouping by more than one column
Lecture 116 Status - Getting the number of unique values in a column and it the dataframe
Lecture 117 Exporting the structured transformations to a CSV file
Lecture 118 Repeating the work we've done
Lecture 119 The datatime data type
Lecture 120 New methods used
Lecture 121 Filtering
Lecture 122 Adding a new column to a data frame
Lecture 123 Summary
Section 12: How do I clean data?
Lecture 124 Introduction
Lecture 125 The Netflix dataset
Lecture 126 Converting data and extracting digits from columns
Lecture 127 Missing rows in strings
Lecture 128 Replacing missing values in strings
Lecture 129 Replacing missing values in numbers
Lecture 130 Dropping missing rows
Lecture 131 Identifying and dropping duplicate rows
Lecture 132 Extracting numbers out of strings
Lecture 133 Getting parts of a string - slicing and substrings
Lecture 134 Getting the end of a string and finding help
Lecture 135 Getting words out of a string - splitting
Lecture 136 Advanced string extraction - regular expressions
Lecture 137 Getting help with regular expressions
Lecture 138 Applying functions to strings - mapping
Lecture 139 Summary
Section 13: Enrichment - Making data valuable
Lecture 140 Introduction
Lecture 141 Columns in dataframes - series
Lecture 142 Getting rows by their number - indexes
Lecture 143 Combining data - the concat function
Lecture 144 Adding columns together using the concat function
Lecture 145 Combining data by the same column name - merge
Lecture 146 Understanding joins
Lecture 147 Left join - returning all the rows in the left table
Lecture 148 Right join - all the rows in the right table
Lecture 149 Outer join - all the rows in both tables
Lecture 150 Joining tables by index - the Join method
Lecture 151 Adding a new row to the dataframe
Lecture 152 Removing the duplicates
Lecture 153 Adding multiple rows to a dataframe
Lecture 154 Changing the value of existing rows - update
Lecture 155 Updating rows based on a column - setting the indexes
Lecture 156 Updating a dataframe with merge
Lecture 157 Summary
Section 14: Validation - Making sure the data is correct
Lecture 158 Introduction
Lecture 159 The characteristics of good data
Lecture 160 Data lacking quality cause PR nightmares
Lecture 161 Data accuracy
Lecture 162 Getting help from GitHub co-pilot chat
Lecture 163 Identifying duplicate rows (reminder)
Lecture 164 Checking for missing values(reminder)
Lecture 165 Data completeness
Lecture 166 Data consistancy
Lecture 167 Data reliability
Lecture 168 Data relevance
Lecture 169 Data timeliness
Lecture 170 Summary
Section 15: Publishing - Querying and Presenting the data
Lecture 171 Introduction
Lecture 172 What is data publishing?
Lecture 173 Using Faker to generate fake data
Lecture 174 Querying the data
Lecture 175 Getting the total and average revenue by product
Lecture 176 Displaying revenue by product in a chart
Lecture 177 Use Matplotlib to generate a scattered plot
Lecture 178 Displaying data over time using Matplotlib and Pandas
Lecture 179 Use Seaborn for heatmaps
Lecture 180 Exporting results to PDF
Lecture 181 Exporting results to Excel
Lecture 182 Exporting to CSV
Lecture 183 Summary
Section 16: Conclusion
Lecture 184 Where to from here?
Lecture 185 Congratulations!
Data professionals, such as accountants and analysts, who want to learn about data wrangling,Data professionals who want to use AI to increase their productivity level significantly,Anyone curious about AI and how it can be used in the real world, right now

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