Modern Data Wrangling with AI and Python – Beginner to Pro

سرفصل های دوره

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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تولید کننده: Udemy