DS4B 101-P: Python for Data Science Automation

27h 6m 1s
English
Paid
April 19, 2024

Python for Data Science Automation is an innovative course designed to teach data analysts how to convert business processes to python-based data science automations. The course is founded on two driving principles:

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  1. Companies are transitioning repetitive business processes to automations to reduce errors, improve scale, and make data products available on-demand.
  2. You (the student) will undergo a complete transformation, learning the in-demand skills that will empower you to help automate business processes for your organization.

Python for data science automation is crafted for business analysts that need to learn Python for automating repetitive tasks and building data analysis software. This includes:

  • BI Professionals: Analysts that are using Business Intelligence (BI) tools like Excel, Power BI, and Tableau that would like to take their skills to a whole new level
  • R Users: Data Scientists and Analysts that use the R Language but need to learn Python for business to help co-integrate with Python teams.
  • Python Beginners: Students that need to learn Python analytical programming through a business-focused course.

This is a project-based course. You are part of the data science team for a hypothetical bicycle manufacturer. Management has charged the team with expanding the forecast reporting by customers, products, and different time-durations. This requires a new level of flexibility that is not currently available in the manual business process. You’ll need to learn Pandas and the Python ecosystem to help automate this forecasting project. 

Transition repetitive business processes to Python automation workflows. Here's an example of the business process automation workflow you create in this course. 

In Python for Data Science Automation, you learn:

  • Learn how to break down business processes
  • Learn how to apply Python and Pandas Coding
  • Learn how to work with databases and create reports

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# Title Duration
1 Python for Data Science Automation: Let's Do This! 02:19
2 The Game Plan: Data Analysis Foundations 00:46
3 The Business Case: Building an Automated Forecast System 00:44
4 Course Project Zip [File Download] 00:45
5 Course Workflow: Tying Specific Actions to the Business Process 02:27
6 Ultimate Python Cheat Sheet: Python Ecosystem in 2 Pages 04:03
7 The Transactional Database Model [PDF Download] 03:35
8 Anaconda Installation 02:38
9 IDE (Integrated Development Environment) Options 02:19
10 VSCode Installation 01:38
11 Connect VSCode to Your Course Project Files 01:13
12 Conda Env Create: Make the Python Course Environment 04:41
13 Python Select Interpreter: Connect VSCode to Your Python Environment 00:41
14 Conda Env Update: Add Python Packages to Your Environment 01:44
15 Conda Env Export: Review & Share Your Environment 01:12
16 Conda Env List & Remove: List Available Environments & Remove Unnecessary Envs 01:22
17 Getting to Know VSCode 01:15
18 VSCode Theme Customization 02:07
19 VSCode Icon Themes 00:44
20 VSCode User & Workspace Settings 04:16
21 VSCode Keyboard Shortcuts 01:17
22 VSCode Python Extensions 03:23
23 VSCode Jupyter Extension - Jupyter Notebook Support 02:05
24 VSCode Jupyter Extension - Interactive Python 03:35
25 [Optional VSCode Setting] Jupyter: Send Selection to Interactive Window 02:31
26 VSCode Excel Viewer 01:01
27 VSCode Markdown & PDF Extensions 02:43
28 VSCode Path Intellisense 01:09
29 VSCode SQLite Extension 00:41
30 [Optional] VSCode Extensions for R Users 01:27
31 Python Environment Checkpoint [File Download] 03:53
32 Getting Started [File Download] 04:08
33 Using the Cheat Sheet 01:26
34 Import: pandas, numpy, matplotlib.pyplot 03:34
35 Importing From: plotnine, miziani 04:42
36 Importing Functions and Submodules: os, rich 02:10
37 Setting Up Python Interactive 02:45
38 [Reminder | Optional VSCode Setting] Jupyter: Send Selection to Interactive Window 02:31
39 Getting Help Documentation 02:47
40 IMPORTANT VSCODE SETTING: File Paths | jupyter.notebookFileRoot 06:35
41 Reading the Excel Files 06:46
42 Reviewing the Data Model 05:10
43 Exploratory 1: Top 5 Most Frequent Descriptions 03:55
44 Exploratory 2: Plotting the Top 5 Bike Descriptions 06:23
45 Preparing Orderlines for Merge: Drop Column 03:05
46 Merging the Bikes DataFrame 03:34
47 Merging the Bikeshops Data Frame 03:27
48 Datetime: Converting Order Date | Copy vs No Copy 04:52
49 Splitting the Description: Category 1, Category 2, and Frame Material 07:27
50 Splitting Location: City, State 03:05
51 Create the Total Price Column 02:55
52 Reorganizing the Columns 04:44
53 Renaming Columns 04:07
54 Reviewing the Data Transformations 01:12
55 Save Your Work: Pickle it. 03:50
56 Pandas Datetime Accessors 02:44
57 Resampling: Working with Pandas Offsets 07:26
58 Quick Plot: Plotting Single Time Series w/ Pandas Matplotlib Backend 01:41
59 Plotnine Visualization: Sales By Month, Part 1 - Geometries 05:53
60 Plotnine Visualization: Sales by Month, Part 2 - Scales & Themes 05:51
61 Resampling Groups: Combine groupby() and resample() 09:23
62 Quick Plot: Plotting Multiple Time Series w/ Pandas Matplotlib Backend 07:24
63 Plotnine Visualization, Part 1: Facetted Sales By Date & Category2 (Group) 08:58
64 Plotnine Visualization, Part 2: Adding Themes & Scales 08:53
65 Writing Files: Pickle, CSV, Excel 04:42
66 Congrats. That was a fun whirlwind. Let's recap. 02:35
67 Getting Started [File Download] 01:22
68 Pickle Files 03:41
69 CSV Files 04:00
70 Excel Files 03:26
71 SQL Databases 01:47
72 Pandas I/O & SQL Alchemy Overviews 03:02
73 Make Database Directory 01:24
74 Create the SQLite Database 04:20
75 Read the Excel Files 03:04
76 Create the Database Tables 07:12
77 Close the Connection 00:54
78 Connect to the Database 02:08
79 Getting the Database Table Names 02:36
80 Reading from the Tables with f-strings 01:48
81 [Bonus] VSCode SQLite Extension 03:05
82 Making collect_data(), Part 1: Function Setup 06:40
83 Making collect_data(), Part 2: Read Tables from the Database 08:39
84 Making collect_data(), Part 3: Test the Database Import 01:15
85 Making collect_data(), Part 4: Joining the Data 08:26
86 Making collect_data(), Part 5: Cleaning the Data 1 07:15
87 Making collect_data(), Part 6: Cleaning the Data 2 06:49
88 Making collect_data(), Part 7: VSCode Docstring Generator 03:59
89 Making a Package (my_pandas_extensions): Adding the database module 04:42
90 🥳Congrats! You're learning really powerful concepts. 01:07
91 Getting Started [File Download] 02:25
92 [VSCode Setting] Jupyter: Send Selection to Interactive Window 01:13
93 Package & Function Imports 01:29
94 My Pandas Extensions: Fix FutureWarning Message (regex) 01:29
95 How Python Works: Objects 05:30
96 Pandas DataFrame & Series 02:52
97 Numpy Arrays 04:09
98 Python Builtin Data Structures: Dictionary, List, Tuple 05:54
99 Python Builtin Data Types: Int, Float, Str, Bool, 03:42
100 Casting Basics: Numeric & String Conversions 04:10
101 Casting Sequences: To List, Numpy Array, Pandas Series, & DataFrame 02:41
102 Pandas Series Dtype Conversion 01:44
103 Pandas Data Wrangling Setup 02:09
104 Subsetting Columns by Name 02:17
105 Subsetting by Column Index (Position): iloc[] 01:36
106 Subsetting Columns with Regex (Regular Expressions) 03:38
107 Rearranging a Single Column (Column Subsetting) 02:17
108 Rearranging Multiple Columns (Repetitive Way First) 01:44
109 Rearranging Multiple Columns (List Comprehension) 02:51
110 Data Frame Rearrange: Select Dtypes, Concat, & Drop 06:33
111 Sort Values 03:07
112 Simple Filters with Boolean Series 03:55
113 Query Filters 03:48
114 Filtering with isin() and 03:42
115 Index slicing with df.iloc[] 02:42
116 Getting Distinct Values: Drop duplicates 01:44
117 N-Largest and N-Smallest 02:15
118 Random Samples 01:53
119 DataFrame Column Assignment: Calculated Columns 02:26
120 Assign Basics: Lambda Functions 03:11
121 Assign Cookbook: Making a Log Transformation 03:32
122 Assign Cookbook: Searching Text (Boolean Flags) 05:27
123 Assign Cookbook: Even-Width Binning with pd.cut() 03:46
124 Visualizing Binning Strategies with a Pandas Heat Table 03:01
125 Assign Cookbook: Quantile Binning with pd.qcut() 02:36
126 Aggregation Basics (Summarizations) 05:49
127 Common Summary Functions 04:11
128 Groupby + Aggregate Basics (Summarizations) 05:27
129 Groupby + Agg Cookbook (Summary DF 1): Sum & Median Total Price By Category 1 & 2 03:14
130 Groupby + Agg Cookbook (Summary DF 2): Sum Total Price & Quantity By Category 1 & 2 03:24
131 Groupby + Agg Details: Examining the Multilevel Column Index 02:01
132 Groupby + Agg Cookbook (Summary DF 3): Grouping Time Series with Groupby & Resample 04:12
133 Groupby + Apply Basics (Transformations) 03:42
134 Groupby + Apply Cookbook: Transform All Columns by Group 02:35
135 Groupby + Apply Cookbook: Filtering Slices by Group 03:25
136 Renaming Basics: Renaming All Columns with Lambda 04:28
137 Renaming Basics: Targeting Specific Columns 01:21
138 Advanced Renaming: Renaming Multi-Index Columns 05:57
139 Set Up Summarized Data: Revenue by Category 1 05:00
140 Pivot: To Wide Format 06:42
141 Export a Stylized Pandas Table to Excel (Wide Data) 06:09
142 Melt: To Long Format 03:31
143 Plotnine - Making a Faceted Horizontal Bar Chart (Tidy Long Data) 04:34
144 Intro to Categorical Data: Sorting the Plotnine Plot 06:09
145 Pivot Table (An awesome function for BI Tables) 07:42
146 Unstack: A programmatic version of pivot() 04:10
147 Stack: A programmatic version of melt() 02:25
148 Merge: Data Frame Joins 04:12
149 Concat: Binding DataFrames Rowwise & Columnwise 04:27
150 Splitting Text Columns 03:08
151 Combining Text Columns 01:07
152 Set Up Summarized Data: Sales by Category 2 Daily 03:02
153 Apply: Lambda Aggregations vs Transformations 02:22
154 Apply: Broadcasting Aggregations 01:53
155 Grouped Apply: Broadcasting 02:24
156 Grouped Transform: Alternative to Grouped Apply (Fixes Index Issue) 02:03
157 Making a "Data Frame" Function: add_columns() 06:08
158 Pipe: Method chaining our custom function using the pipe 03:12
159 Challenge #1: Data Wrangling with Pandas [File Download] 01:17
160 Method 1: Jupyter VSCode Integration 02:25
161 Method 2: Jupyter Notebooks (Legacy Method) 02:07
162 Method 3: JupyterLab (Next Generation of Jupyter) 03:16
163 Challenge Objectives 03:08
164 Getting Started: Syncing Your JupyterLab Current Working Directory (%cd and %pwd) 05:10
165 Challenge Tasks 03:18
166 Challenge Solution 08:40
167 Congrats! You've finished your first challenge. 01:37
168 Automating Time Series Forecasting 01:40
169 Getting Started [File Download] 01:49
170 VSCode Extension: Browser Preview 01:41
171 Package Imports 01:40
172 The ProfileReport() Class 01:10
173 Section 1: Profile Overview 03:19
174 Section 2A: Numeric & Date Variables 06:03
175 Section 2B: Categorical (Text) Variables 05:02
176 Sections 3-6: Interactions, Correlations, Missing Values, & Sample 02:52
177 Pandas Extension: df.profile_report() 03:09
178 Exporting the Profile Report as HTML 01:36
179 Getting Started 00:50
180 TimeStamp & Period Conversions 02:59
181 Pandas Datetime Accessors 01:56
182 Date Math: Offsetting Time with TimeDelta's 02:39
183 Date Math: Getting Duration between Two TimeStamps 03:28
184 Creating Date Sequences: pd.date_range() 03:09
185 Periods (In-Depth) 07:58
186 Resampling (In-Depth): bike_sales_m_df 06:24
187 Grouped Resampling (In-Depth): bike_sales_cat2_m_wide_df 06:38
188 Reorganizing: Adding Comments 01:31
189 Differencing with Lags (Single Time Series) 05:39
190 Differencing with Lags (Multiple Time Series) 02:05
191 Difference from First (Single Time Series) 01:43
192 Difference From First (Multiple Time Series) 00:58
193 Cumulative Expanding Windows (Single Time Series) 03:21
194 Cumulative Expanding Windows (Multiple Time Series) 01:39
195 Moving Average (Single Time Series) 08:15
196 Moving Average (Multiple Time Series) 04:37
197 Next Steps (Where we are headed) 01:17
198 Getting Started [File Download] 01:37
199 Setup: Python Imports & Data 00:46
200 Function Anatomy: pd.Series.max() 03:53
201 Errors (Exceptions) 01:03
202 Function Names 01:17
203 Function Anatomy: **kwargs 05:13
204 Detect Outliers: Function Setup 02:19
205 IQR Outlier Method, Part 1 03:37
206 IQR Method, Part 2 04:07
207 New Argument: IQR Multiplier 01:47
208 New Argument: How? (Both, Upper, Lower) 02:36
209 Checking for Pandas Series Input 02:11
210 Checking IQR Multiplier for Int or Float Type 02:54
211 Checking that IQR Multiplier is a Positive Value 01:10
212 Checking that How is a Valid Option: both, lower, upper 02:19
213 Informative Help Documentation: Adding a Docstring 07:12
214 Testing Our Function: Detecting Outliers within Groups 03:05
215 Extending the Pandas Series Class 02:12
216 Summarize By Time: A handy function for time series wrangling 04:00
217 Setting Up the "Summarize By Time" Function 04:56
218 Handling the Date Column Input 01:31
219 Handling Groups Input 02:03
220 Handling the Time Series Resample 04:14
221 Handling the Aggregation Function Input 03:16
222 Handling the Value Column Input 01:40
223 Forcing the Value Column Input to a List (to generate a data frame) 02:44
224 Bug! Thinking through a solution 02:26
225 Solution: Converting to a Function Dictionary with Zip + Dict 03:52
226 Handling the Unstack 02:02
227 Handling the Period Conversion 02:51
228 Add Fill Missing Capability 02:25
229 Review the Core Functionality 01:25
230 Check Incoming Data: Raising a TypeError 01:50
231 Adding the Docstring 07:28
232 Pandas Flavor: Extending Pandas DataFrame Class 06:23
233 Getting Started [File Download] 03:03
234 Sktime Documentation 04:36
235 How to Google Search like a Pro 01:35
236 Set Up & Imports 02:41
237 Summarizing to get Total Revenue by Month 05:00
238 Summarizing to get Total Revenue by Category 2 & Month 02:42
239 What is AutoARIMA? 04:59
240 AutoARIMA Applied: Forecaster, Fit, Predict 08:25
241 Adding Confidence Intervals (Prediction Intervals) 02:41
242 Tuple Unpacking (Predictions, Confidence Intervals) 02:39
243 Forecast Visualization 05:28
244 Code Housekeeping 00:24
245 Multiple Time Series Forecasting: AutoARIMA() 03:10
246 For Loop: Iterate Across the DataFrame Columns 02:20
247 For Loop: Modeling AutoARIMA() 05:23
248 For-Loop: Getting the Confidence Intervals 01:32
249 For-Loop: Combine with DataFrame | Actual Values, Predictions, & CIs 04:12
250 For-Loop: Storing the Results (as a Dictionary) 03:36
251 Housekeeping: Appending Variable Types to Variable Names 01:53
252 Visual Forecast Assessment 02:43
253 TQDM: Progress Bars 03:41
254 Setting up the ARIMA Automation Function 03:45
255 Making arima_forecast() | Function Definition 03:19
256 Function Body | Setting Up the Iteration 04:41
257 Training the AutoARIMA() Models 03:02
258 Controlling Progress Bars: tqdm(min_interval) 01:12
259 Making Predictions and Confidence Intervals 02:09
260 Combine Results into a DataFrame 02:24
261 Compose a Prediction Dictionary 01:50
262 Return Results as a Single DataFrame | Rowwise Concatenation 02:37
263 Setting the Column Names of the Output 09:16
264 Drop remaining columns beginning with "level_" 02:51
265 Testing the arima_forecast() function 02:05
266 Creating the forecasting.py module 03:44
267 Docstring: arima_forecast() 01:32
268 Adding Checks: arima_forecast() 06:35
269 Finally - Check Your Forecasts with Grouped Pandas Plotting 02:29
270 Recap: You've just made an ARIMA Forecast Automation! 01:10
271 Introduction to ETS Forecasting (Exponential Smoothing) 02:07
272 Challenge 2 [File Download] 06:07
273 Solution 05:18
274 Part 3: Visualization & Reporting 01:25
275 Getting Started [File Download] 00:32
276 Plotnine Documentation 03:15
277 Plotnine Anatomy: Imports 02:57
278 Data Summarization: For Plotting Annual Bike Sales 02:54
279 The Plot Canvas: Mapping Columns to Plot Components 07:13
280 Plotnine Geometries 04:00
281 Adding a Trend Line: geom_smooth() 03:00
282 Formatting Plots 01:53
283 Expand Limits 01:42
284 Scales: Dollar Format for Y-Axis 03:52
285 Scales: Date Format for X-Axis 02:15
286 Labs and Themes 02:58
287 Saving the ggplot 01:18
288 Exploring the Plotnine Object 02:24
289 Setting Up 02:20
290 Scatter Plot: Data Manipulation 02:52
291 Scatter Plot: Visualization 03:18
292 Line Plot: Data Manipulation 02:08
293 Line Plot: Visualization 05:29
294 Data Manipulation, Part 1: No Categorical Ordering 02:49
295 Visualization, Part 1: Without Categorical Ordering 01:35
296 Aside: Introduction to Plotting using Categorical Data Type 09:55
297 Finalizing the Horizontal Bar Chart 01:20
298 Histogram: Data Manipulation 02:48
299 Histogram: Visualization 02:15
300 Histogram: Using Fill Aesthetic to Explore Differences by a Category 02:51
301 Histogram: Using Facet Grids to Compare Distributions by Category 02:48
302 Density Plots: Kernel Density Estimation (KDE) using geom_density() 02:55
303 Box Plot: Data Manipulation 02:20
304 Box Plot: Visualization 07:24
305 Violin Plot with Jitter: geom_violin() and geom_jitter() 03:32
306 Data Manipulation: Add a Total Price Text Column with USD Dollar Format 06:09
307 Creating the Bar Plot: geom_col() and geom_smooth() 03:11
308 Adding Text to a Bar Plot: geom_text() 05:35
309 Highlighting an Outlier with a Label: geom_label() 05:58
310 Finalizing the Plot with Scales and Themes 03:39
311 Sales by Month and Category 2: Data Manipulation 04:41
312 Facets: Adding subplots "facets" with facet_wrap() 06:55
313 Scales: Applying scales to alter x, y, and color mappings 04:34
314 Themes: Theme Customization with Pre-Built Themes | theme_matplotlib() 03:54
315 Theme Elements: Customization with theme() 05:33
316 Plot Title and X/Y-Axis Labels: labs() 04:44
317 Getting Started 01:21
318 Package Imports 02:10
319 Our Forecasting Workflow Recap 05:00
320 Data Preparation: Melting the Value and Prediction Columns 04:51
321 Data Preparation: Fixing the FutureWarning 03:04
322 Visualization: Setting up the canvas with ggplot() 03:35
323 Visualization: Adding geoms and facets 05:43
324 Visualization: Scales and Theme Minimal 05:12
325 Visualization: Customizing the Theme Elements 04:22
326 Making the plot_forecast() Function Definition 03:23
327 Data Wrangling: Implementing the Melt 04:38
328 Handling the Time-Based Column: Converting to TimeStamp 08:52
329 Visualization: Parameterizing the Plot 08:56
330 Testing the Forecast Plot Function Parameters 07:10
331 Testing the Automation Workflow 01:30
332 Reordering the Subplots using Cat Tools 06:15
333 Adding the plot_forecast() function to our forecasting module 03:37
334 Docstring | Testing Our Imported plot_forecast() Function 03:30
335 Getting Started [File Download] 01:56
336 Package Imports 02:33
337 Reviewing Our Files 01:37
338 Generating the Forecasting Workflow 05:26
339 Generating the Forecast Visualization 01:39
340 Overview of the Database I/O Process 01:27
341 Preparing the Forecast for Update 05:44
342 Validating the Column Names 06:11
343 Testing the Prep Forecast for Database Function 01:15
344 Setting Up the Write Forecast to Database Function 05:28
345 Modularizing the Data Preparation Step 01:19
346 Specifying SQL Data Types 06:47
347 Write to Database 06:50
348 Close Connection 00:55
349 Testing Our Function 04:29
350 Creating our Read Forecast Function 06:00
351 Adding Functions to Database Module 04:19
352 Docstrings 03:07
353 Automation Workflow with Database I/O 02:44
354 Forecasting 1: Total Revenue 04:43
355 Fix #1: Reorder Columns in Prep Data Function 04:30
356 Plotting Total Revenue Forecast 01:25
357 Forecasting 2: Revenue by Category 1 05:33
358 Forecasting 3: Revenue by Category 2 04:37
359 Forecasting 4: Forecast Quarterly Revenue by Customer 05:34
360 Fix #2: Prep Data | Add timestamp conversion 01:33
361 Rerun Our Workflow: Success! 03:20
362 Writing to the Database 03:15
363 Pro-Tip: Saving Intermediate Data 01:46
364 Utility Function: Convert to Datetime 07:00
365 Rerun the Forecast Workflow 03:46
366 Read Forecast from Database 02:02
367 Recap: Debugging is a Skill 03:42
368 Jupyter Automated Reporting 01:16
369 Getting Started [File Download] 02:54
370 The Updated Database Script: Automatically Run Forecasts 05:49
371 python update_database.py 03:28
372 SQLite Explorer 01:23
373 Setting Up the Working Directory 06:14
374 Importing Data and Parameterizing a Header with Markdown 06:12
375 Parameterizing a Paragraph with Markdown 03:56
376 Performance Summary: Pivot Table, Part 1 05:35
377 Performance Summary: Pivot Table, Part 2 02:14
378 Plotting the Forecast: plot_forecast() 02:04
379 Papermill Setup 01:16
380 Package Imports 02:12
381 Papermill Documentation 03:19
382 Developing Parameters: Game Plan 02:11
383 Making ID Sets, Part 1 03:21
384 Making ID Sets, Part 2 04:32
385 Part 1: Intro to Pathlib and OS 03:02
386 Part 2: Detecting Directories Exist & Making New Directories 04:42
387 Jupyter Template Setup 02:57
388 Parameterizing the Jupyter Template 03:49
389 Finishing the Juyter Template Parameterization 03:47
390 The pm.exectute_notebook() function 04:06
391 Setting Up Key Parameters 06:02
392 Iterating without a For-Loop 06:09
393 Iterating with a For-Loop 06:01
394 Getting Started 01:08
395 Setting Up the Report Parameters 03:33
396 Creating a Resource Path 01:29
397 String Transformation: Make File Names from Report Titles 06:32
398 Setting Up run_reports() 02:46
399 Make the Report Directory 04:49
400 Setting Up the For-Loop Parameters 06:23
401 Setting Up Jupyter Notebook Execution (Inside of For-Loop) 04:02
402 Package Resources: Setting Up the Template Path 06:04
403 Integrating the Run Reports Function into Our Package 04:35
404 Getting Started [File Download] 03:32
405 NB Convert Documentation & Installation Requirements 01:50
406 Step 1: Pandoc Installation 01:06
407 Step 2: Tex Installation (MikTex Windows Shown | Mac Use MacTex) 01:06
408 HTML Report Conversion 01:34
409 PDF Report Conversion 01:04
410 Setup & Imports 04:22
411 Making the Config() 04:23
412 Locating Files with Glob 03:21
413 Exporting an HTML Report Programmatically 06:39
414 HTML Automation: Using a For-Loop to Convert All 4 Reports 05:51
415 PDF Automation: Using a For-Loop to Convert All 4 Reports 06:00
416 Getting Set Up 02:42
417 Integrating glob: Pulling the Jupyter Notebook File Paths 02:47
418 Integrate "Convert to HTML" Report Automation 04:12
419 Test "Convert to HTML" Report Automation 02:32
420 Integrate "Convert to PDF" Report Automation 01:47
421 Test "Convert to PDF" Report Automation 03:40
422 My Pandas Extensions: Upgrade reporting.py with HTML & PDF Reports, Part 1 06:32
423 My Pandas Extensions: Upgrade reporting.py with HTML & PDF Reports, Part 2 04:01
424 Run Forecast Reports Py: Part 1 - The main() function 05:08
425 Run Forecast Reports Py: Part 2 - Adding Timestamps to Folders 06:02
426 Run Forecast Reports Py: Part 3 - Running Reports 02:57
427 Run Forecast Reports Py: Part 4 - Adjusting Folder Automation 03:14
428 Scheduling Python Scripts Bonus!!! 00:28
429 Making the Batch File (.bat) to run our Python Script 02:26
430 Setting up Automated Tasks with Windows Task Scheduler 02:14
431 Debugging Windows Task Scheduler Tasks with Pause 00:40
432 Fixing the SQL Alchemy Connection 01:53
433 Removing the Automation: Disable & Delete 00:24
434 Python Script Setup | SQL Database Absolute Path 02:31
435 The Mac Automator 03:23
436 Scheduling the Automator App with Calendar 02:01
437 Congratulations!!! 01:11
438 Forecasting 100 Time Series in Python with Sktime 01:33:11

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