Modern Data Wrangling with AI and Python - Beginner to Pro

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Gerhard van Deventer

3:53:58

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1 - Welcome

1 - Welcome to the course.mp4

01:33

2 - Meet Gerhard.mp4

00:54

3 - Is this course for you.mp4

01:01

4 - What will you get out of this course.mp4

01:35

5 - Whats in this course.mp4

03:28

2 - Getting Started

6 - Introduction.mp4

00:56

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.mp4

02:34

8 - Adding Extensions to VSC.mp4

01:20

9 - Getting Started with VSC copilot.txt

9 - GitHub copilot.mp4

00:43

10 - Create a Github account.mp4

00:52

11 - GitHub Copilot chat.mp4

01:54

12 - Getting started with GitHub copilot.txt

12 - GitHub Copilot continued.mp4

00:48

13 - Testing if Copilot is working.mp4

01:16

14 - Github copilot help content.html

15 - Anaconda.mp4

00:35

15 - Install Anaconda.txt

16 - Github Desktop.txt

16 - Installing Github Desktop.mp4

00:36

17 - Getting started with Github Desktop.txt

17 - The GitHub Desktop Interface.mp4

01:17

18 - Code for the course.txt

18 - The repository for this course.mp4

01:25

19 - GIT optional.mp4

00:37

19 - Install GIT.txt

20 - Summary.mp4

01:05

3 - Data Exploration What can you tell me about the data

21 - Introduction.mp4

01:05

22 - A spreadsheet program.mp4

01:22

22 - sales-data-sample.csv

23 - The challenge with spreadsheets.mp4

01:54

24 - Data Wrangling Course Repo.txt

24 - Follow along with me.mp4

00:51

25 - Welcome to Python and AI.mp4

01:11

25 - getting-the-data-you-need.zip

26 - Markdown and Code fields in Jupyter.mp4

00:54

26 - Markdown in Jupyter.txt

26 - Notebook basics.txt

27 - What is Pandas.mp4

01:10

28 - Read a file in Pandas and display it.mp4

01:58

29 - Describe the data.mp4

01:29

30 - Do more with Copilot Jupyter and Python.mp4

01:25

31 - Summary.mp4

00:45

4 - Enough Python to start your journey

32 - Introduction.mp4

01:02

33 - Libraries.mp4

01:56

34 - More Pandas.mp4

01:15

34 - Pandas Getting Started.txt

34 - readcsv.txt

35 - Objects.mp4

01:40

36 - Getting help from Large Language Models.mp4

01:01

36 - What is a Library in Python.txt

37 - Methods.mp4

01:01

38 - Summary.mp4

01:00

5 - My journey to data wrangling

39 - My journey.mp4

00:43

6 - Structured Data Tables

40 - Introduction.mp4

00:38

41 - Structured Data.mp4

01:33

42 - Structured Data in Python.mp4

00:41

43 - CSV files as structured data.mp4

01:30

44 - Excel data as structured data.mp4

01:25

45 - General methods for structured data Excel and CSV.mp4

01:18

46 - SQL Tables.mp4

00:52

47 - SQL data in Python.mp4

01:59

48 - Summary.mp4

01:02

7 - More on Python functions properties and some other goodies

49 - Introduction.mp4

01:17

50 - Functions.mp4

01:13

51 - Function signatures.mp4

02:01

52 - Function bodies.mp4

01:44

53 - Using fucnctions.mp4

01:16

54 - A function in action.mp4

00:52

55 - Properties.mp4

01:10

56 - Properties in code.mp4

02:33

57 - For loops.mp4

01:08

58 - For loops in code.mp4

01:11

59 - Another example of for loops.mp4

01:30

60 - Getting help Docstrings and signatures.mp4

01:38

61 - Getting help online documentation.mp4

00:48

62 - Getting help LLMs.mp4

01:12

62 - OpenPyXL.txt

63 - Getting help Github copilot.mp4

02:19

64 - Summary.mp4

01:23

8 - Unstructured Data

65 - Introduction.mp4

01:11

66 - Installing Tabula in condas.mp4

00:55

67 - Tables in PDF documents.mp4

00:30

68 - Extracting a table from a PDF with Python.mp4

00:50

69 - Accessing particular cells in a dataframe.mp4

01:24

70 - Rename the columns of a dataframe.mp4

00:52

71 - Rename columns 2 and 3 of the dataframe.mp4

01:15

72 - Rename the remainder of the columns and concatenate strings.mp4

00:31

73 - Delete rows from a dataframe.mp4

00:51

74 - Split values in a column.mp4

01:46

75 - Drop columns in a dataframe.mp4

00:54

76 - PDFs with text.mp4

00:38

77 - Extract text from PDFs using Python.mp4

01:23

78 - Summary.mp4

00:59

9 - Web services or Application Web InterfacesAPIs

79 - Introduction.mp4

00:47

80 - Web services or Application Web InterfacesAPIs.mp4

01:00

81 - Cat facts API.txt

81 - JSON View.txt

81 - The cat facts API.mp4

02:03

82 - HTTP response status codes.mp4

01:14

82 - HTTP response status codes.txt

83 - JSON payloads.mp4

01:10

84 - More on JSON.mp4

01:48

85 - Import API call into Postman.mp4

01:26

86 - Making an API call in Postman.mp4

00:47

87 - Generate code in Postman.mp4

00:49

88 - Execute Postman code in a Jupyter Notebook.mp4

00:54

89 - Querying JSON objects in Python.mp4

00:58

90 - Accessing lists and nested values in JSON.mp4

00:47

91 - Converting JSON to Dataframes.mp4

01:52

92 - Summary.mp4

01:21

10 - Lists dictionaries and data types in Python

93 - Introduction.mp4

01:29

94 - Lists.mp4

01:24

94 - List documentation.txt

94 - List methods.txt

95 - More than numbers.mp4

02:04

96 - Lists were only scratching the surface.mp4

00:54

97 - Lists and dictionaries in VSC.mp4

01:07

98 - Lists in action.mp4

01:31

99 - Dictionaries.mp4

01:12

100 - Dictionaries in action.mp4

00:54

101 - Data types.mp4

02:10

102 - Data types in Jupyter.mp4

01:19

103 - Lambdas.mp4

01:49

104 - Lambdas in action.mp4

02:36

105 - Conclusion.mp4

01:29

11 - How do make your data useful Structuring

106 - Introduction.mp4

01:31

107 - Example dataset A Canadian manufacturing company.mp4

00:44

108 - A data dictionary.mp4

00:42

109 - REFDATE changing the data type of a column.mp4

01:32

110 - GEO getting the number of unique values in a column.mp4

01:15

111 - Dropping a column in a dataframe.mp4

00:36

112 - DGUID Renaming and finding the meaning of a column.mp4

01:04

113 - Principal statistics Filtering data.mp4

02:12

114 - Dropping mor than one column UAM and UAMID.mp4

00:26

115 - NAICS VECTOR and COORDINATE grouping by more than one column.mp4

01:48

116 - Status Getting the number of unique values in a column and it the dataframe.mp4

01:53

117 - Exporting the structured transformations to a CSV file.mp4

00:49

118 - Repeating the work weve done.mp4

00:41

119 - Python datetime.txt

119 - The datatime data type.mp4

01:00

119 - todatetime.txt

120 - New methods used.mp4

01:26

121 - Filtering.mp4

01:10

122 - Adding a new column to a data frame.mp4

00:31

123 - Summary.mp4

01:48

12 - How do I clean data

124 - Introduction.mp4

00:55

125 - The Netflix dataset.mp4

01:10

126 - Converting data and extracting digits from columns.mp4

03:23

127 - Missing rows in strings.mp4

02:05

128 - Replacing missing values in strings.mp4

01:32

129 - Replacing missing values in numbers.mp4

02:00

130 - Dropping missing rows.mp4

01:07

131 - Identifying and dropping duplicate rows.mp4

01:44

132 - Extracting numbers out of strings.mp4

01:21

133 - Getting parts of a string slicing and substrings.mp4

00:56

134 - Getting the end of a string and finding help.mp4

00:59

135 - Getting words out of a string splitting.mp4

01:03

136 - Advanced string extraction regular expressions.mp4

03:16

137 - Getting help with regular expressions.mp4

00:43

138 - Applying functions to strings mapping.mp4

01:52

139 - Summary.mp4

01:17

13 - Enrichment Making data valuable

140 - Introduction.mp4

01:04

141 - Columns in dataframes series.mp4

00:49

142 - Getting rows by their number indexes.mp4

00:48

143 - Combining data the concat function.mp4

01:49

144 - Adding columns together using the concat function.mp4

00:33

145 - Combining data by the same column name merge.mp4

01:45

146 - Understanding joins.mp4

01:28

147 - Left join returning all the rows in the left table.mp4

01:07

148 - Right join all the rows in the right table.mp4

00:46

149 - Outer join all the rows in both tables.mp4

00:37

150 - Joining tables by index the Join method.mp4

02:08

151 - Adding a new row to the dataframe.mp4

00:43

152 - Removing the duplicates.mp4

00:21

153 - Adding multiple rows to a dataframe.mp4

00:17

154 - Changing the value of existing rows update.mp4

01:16

155 - Updating rows based on a column setting the indexes.mp4

01:13

156 - Updating a dataframe with merge.mp4

01:37

157 - Summary.mp4

01:31

14 - Validation Making sure the data is correct

158 - Introduction.mp4

01:18

159 - The characteristics of good data.mp4

02:02

160 - Data lacking quality cause PR nightmares.mp4

01:03

161 - Data accuracy.mp4

01:44

162 - Getting help from GitHub copilot chat.mp4

00:43

163 - Identifying duplicate rows reminder.mp4

00:56

164 - Checking for missing valuesreminder.mp4

01:03

165 - Data completeness.mp4

00:43

166 - Data consistancy.mp4

00:56

167 - Data reliability.mp4

01:27

168 - Data relevance.mp4

00:47

169 - Data timeliness.mp4

02:10

170 - Summary.mp4

01:31

15 - Publishing Querying and Presenting the data

171 - Introduction.mp4

01:02

172 - What is data publishing.mp4

01:50

173 - Using Faker to generate fake data.mp4

00:52

174 - Querying the data.mp4

01:43

175 - Getting the total and average revenue by product.mp4

01:23

176 - Displaying revenue by product in a chart.mp4

00:47

177 - Use Matplotlib to generate a scattered plot.mp4

00:53

178 - Displaying data over time using Matplotlib and Pandas.mp4

00:42

179 - Use Seaborn for heatmaps.mp4

01:22

180 - Exporting results to PDF.mp4

02:36

181 - Exporting results to Excel.mp4

01:12

182 - Exporting to CSV.mp4

00:56

183 - Summary.mp4

01:18

16 - Conclusion

184 - Where to from here.mp4

00:46

185 - Congratulations.mp4

00:33

Description

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

What You'll Learn?

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 accurately
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

Who is this for?

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

What You Need to Know?

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

More details

Description
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?Â 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.
Who this course is for:
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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I am an experienced data professional. I've done it all: engineer, architect, analyst and administrator. This culminated in more than 20 years in the industry. I have an honours degree in computer science and many data certifications.I love helping people learn new skills and empowering others. I am blessed that my career allows me to pursue this passion.

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