DS4B 101-P: Python for Data Science Automation
27h 6m 1s
English
Paid
Course description
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:
Read more about the course
- Companies are transitioning repetitive business processes to automations to reduce errors, improve scale, and make data products available on-demand.
- 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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/ #1: Python for Data Science Automation: Let's Do This!
All Course Lessons (438)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | Python for Data Science Automation: Let's Do This! Demo | 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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