Build a Supply Chain Inventory App with Python & Azure.

Learn to set up Anakonda and Jupiter Notebook's, and cover Python basics, including data structures, dictionaries, functions, conditions, and for loops, for the inventory algorithm and data manipulation.

Explore why Python remains a leading data science language for machine learning and how to install and use Anaconda with Jupyter Notebook and Spyder for data science work.

Learn how to download and install the Anaconda distribution across Windows, Mac, and Linux to start using Python for inventory applications.

Install the Anaconda distribution together, note the two to three minutes to install, and then discover which applications will work with Anaconda.

Explore Spyder and notebooks in Anaconda, set up a supply chain data science project folder, run simple Python scripts, manage files, and customize keyboard shortcuts for efficient coding.

Explore Jupiter notebook and Spider within Anaconda to write Python code, run cells, and see outputs instantly.

Discover how Python libraries and packages enable data science tasks, import pandas, matplotlib, and numpy, and run them in Jupyter or Spyder within an Anaconda environment.

install the latest inventories python package with pip using the command pip install inventories, and note ongoing iterations and updates during development.

Explore Python data types and structures, including numbers, strings, dates, booleans, arrays, lists, dictionaries, and tuples, and learn data cleaning, categorical data handling, and type checks.

Demonstrates using parentheses for function calls, explains assignment with equals, distinguishes strings vs objects, and introduces data frames with rows as observations and columns as attributes when importing tables.

Explore basic Python arithmetic in a Jupiter notebook, creating variables for addition and multiplication, and build a prime numbers list while learning zero-based indexing and simple slicing.

Learn to create and subset Python lists, apply zero-based indexing, and select elements to include or exclude, using lists of names.

Explore dictionaries by understanding keys and values, creating a dictionary with word keys and their meanings, and using dot notation to get keys, get values, and retrieve elements.

Learn arrays as a multi-dimensional data structure, distinct from lists and dictionaries, and perform element-wise operations like adding a value to all elements or dividing elements.

Import data in python using pandas in a Jupyter notebook, loading the uk retail dataset. Explore with head, tail, shape, and describe for fields invoice, quantity, date, price, and country.

Learn to subset dataframes in python for inventory analysis using index location and label-based selection (iloc and loc). Create new columns, like price in euros derived from usd.

Explore how to use conditions in Python with data frames, including greater than, less than, and equal tests, combining multiple criteria with and/or, and applying them in loops and functions.

Learn how to define a Python function to determine a person’s status (child, teenager, or adult) using def, if/elif, indentation, and print output for a list.

Learn to use Python's map function to apply a transformation to every element in a list, creating a map object and a resulting status list for inventory data.

Explore core iteration with for loops in Python using class list examples and print statements. Compare for loops to the map function to obtain outputs.

Learn how to loop a function over a list in Python, using for loops and map to apply a status function, store results, and prepare for dataframe operations.

Apply a mapping function to a dataframe to flag United Kingdom customers with a new boolean column, counting United Kingdom vs non-United Kingdom observations in retail data.

Demonstrate looping on a dataframe by applying a row-wise function to reproduce a prior map, using the first 10 rows, and compare map, for loop, and apply for efficiency.

Master python fundamentals for data handling by working with lists and dictionaries, importing data with pandas, subsetting with loc and iloc, and applying conditions, functions, and loops to models.

analyze a 400-car dataset in python to compute rows, columns, and horsepower stats. create a price category with a color label and count budget and expensive cars.

Explore a cars dataset using Python and pandas to inspect shape and columns, analyze unique cylinders, and compute horsepower and price with describe, identifying the most and least expensive cars.

Rename the car name column to name, create a car pricing subset, and implement a pricing_category function to classify prices as budget, suitable, or expensive.

Learn to manipulate and unify data of different shapes for supply chain analytics using Python, including binding, concat, join, merge, group by class and grade for averages, and indexing.

Clean retail data with Python and pandas by dropping duplicates, assessing and imputing missing values, and converting dates for reliable inventory analysis.

Learn practical data cleaning in Python with pandas: convert date columns to datetime, strip commas from numeric fields, and cast strings to category or numeric types.

Drop unnecessary columns and duplicates to clean the data. Convert the invoice date to datetime, create a new datetime column, and enable time-based insights like recency and average purchasing.

Explore how to filter data in Python by selecting subsets with single and multiple conditions, including country filters and value checks, using equality and logical operators.

Learn to perform imputations by filtering for multiple date ranges and updating column values, such as standardizing country names and adjusting retail descriptions in inventory data.

Explore how indexing assigns addresses to rows and columns, enabling fast data retrieval, and learn single and multi-level indexes, including set and reset index for filtering by country and date.

Use multi-level indexing to slice and filter inventory data by country and year, sorting by location and country to retrieve customer and price data for specific markets.

Learn three core data manipulations: pivot tables, melt, and group by, in Python, to reshape data for time series forecasting and to compute aggregates and custom summaries.

Group inventory data by country (and description) using group by, compute total sales with sum, and derive mean prices, noting that grouping variables become indices for multi-feature aggregations.

Learn to use the aggregate function to apply mean and other aggregations across multiple columns, such as quantity and price, and to slice data with group by and indexing options.

Explore indexing, aggregation by quantity and price, and slicing to shape inventory data. Drop levels to convert the index into columns and reveal country and description.

Learn how to use aggregate with explicit column naming to compute mean quantity and price while preserving column identity, and reset the index for a clean, usable result.

Pivot tables reshape supply chain data by turning country into columns and keeping date as the index, with quantity as values. United Kingdom emerges as the top sales country.

Apply aggregate functions in pivot tables to sum quantity and price by date and country, creating multi-level views that show total sales and correct variances across countries.

Connect level zero and level one columns with an underscore to form a single level, then melt the data to long format with one variable representing the collapsed columns.

Apply a left join to combine two tables, using a common key, so the left table's rows stay intact while adding department data where available.

Learn inner join and outer (full) join concepts for inventory data in Python, combining left and right tables by common keys to yield matching rows or all rows.

Learn to build a two-column data frame from a dictionary of names and designations, then perform inner, outer (full), and left joins to combine the tables.

Learn inner, left, and full (outer) joins to merge tables by common keys, preserving left rows and bringing in matching right data, or returning all keys with attributes.

Learn data manipulation for supply chain applications with Python: perform conversions, drop columns and rows, use aggregate, pivot longer and melt, pivot tables, and join data for analysis and visualization.

Explore the New York flights 2013 data set, detailing flights, weather, and aircraft specs, to answer questions on destinations, busiest months, punctuality, delays, and aircraft models.

Import and explore datasets for flights, planes, airlines, and airports, examining fields like departure and arrival times, delays, origin, destination, and tail numbers to analyze schedule data.

Count flights by destination to identify the most popular city from New York. Left join with airports to map destinations to names using FAA keys.

Identify the busiest month by counting flights, with July as the peak, and compare carrier punctuality by total delay (arrival plus departure), highlighting Hawaiian Airlines as likely most punctual.

Aggregate origin–destination flight airtime to identify the longest duration, then join with airports and carriers to analyze delays and punctuality, noting Frontier Airlines as the worst performer.

Analyze airline data to identify the carrier with the highest total seats. Also determine which airplane model, notably the Airbus A320-232, is most used and by which manufacturer.

Explore building web apps in Python using Flask and Dash with Plotly visualizations; learn simple callbacks for interactive, browser-based inventory applications tailored for supply chain professionals.

Build a simple inventory web app with Dash, core components, and html components; create a dummy fruit data frame and visualize it with a graph and a table on localhost.

Launch your first dash application by creating a resources folder, installing dash, building a dummy data frame of teams with games won, and plotting a simple color-coded chart.

Builds a Python app layout using a division with two children, including a header, a graph, and a data frame, transforming the data to dictionary records for display.

Run the Python file locally to start the server, fix errors on the fly, and view a simple application with a header, a graph, and data from the dataset.

Explore the car 04 data set from Python basics. Build an app that shows a scatterplot and a bar plot, and displays the resulting data frame.

Build a functional Python app with three plots—box plot for cylinders, scatter plot for horsepower vs retail price, and a data table—using a declarative, reactive approach with callbacks.

Explore how callbacks in Dash enable interactive graphs by declaring inputs and outputs, using decorators like @app.callback to update figures based on a slider and year data from Gapminder.

Explore a callback example that updates a country data view as a year slider moves, with attributes like country, year, population, life expectancy, and GDP.

Create interactive dropdowns to filter an inventory dataset by car attributes, using options as label and value dictionaries, including an all option, and prepare for future table callbacks.

Explore implementing a callback function in a Python inventory data workflow, handling input and output, and updating data tables with three input arguments.

Develop a dynamic filtering function in Python using if statements and callbacks to control data selection and update graphs for inventory data visualization.

Develop a completely dynamic car app in Python, wiring multiple figures and outputs to update in real time, forecast 18 months, and lay groundwork for inventory development.

Learn to create simple commands and a car data app in Python, using callbacks and decorators, then preview the inventory algorithm's core tasks: data cleaning and recalculations.

Clean and merge sales, stock, and orders data to fuel an inventory algorithm; apply ABC segmentation and Pareto analysis to identify drivers and set minimum order quantities with cron scheduling.

Split the inventory workflow into modular pieces and schedule a daily algorithm using stock and sales data to generate statistics and actionable stock recommendations stored in a database.

Develop a real-time inventory app by building a database of purchasing, stock, pricing, location, sales, and customer data, and implementing the inventory algorithm with a Python server deployment.

Learn to install a MySQL server on macOS or Windows, set up the database for inventory data, and start the server to fetch data from the database.

Install my sequel server, accept the license agreement, and click continue. Enter the password, complete the installation, and close by moving the installer to the trash.

Install and start the MySQL server and workbench, create a new data directory for the selected instance, set a password, initialize the database, and connect to see an empty database.

Investigate apparel sales, stock, and purchasing data to build a Python-driven inventory algorithm that minimizes stock costs while boosting service levels in Mango's real-time database environment.

Create a mango database and schema, then open a notebook to load sales and stocks data for inventory analytics using the last three months to update min, max, and seasonality.

Prepare the algorithm by converting stock data back to its normal form and organizing it into an inventory table, then split the key into size and subfamily and drop key.

Connect to the database by creating an engine and a connection, configure host localhost, user Ruth, and password, and send mangosteens data to the stocks table in 1000-row chunks.

Identify algorithm assumptions for inventory data, including warehouse lead time and seasonality. Handle out-of-stock, replenishment, and returns; ensure data is transferred to the database with outputs stored in a table.

Analyze inventory pipeline, in-transit orders, and lead times to prevent double counting; extract unique items from 90-day sales to inform replenishment.

Fix data errors by dropping the schema and data, recreating tables, and rerunning the data generation for orders and in-transit items to simulate a real enterprise resource planning inventory environment.

Fetches the latest inventory data from the database using SQL queries, retrieving last three months and two years of stock, orders, and sales, and processes them with pandas for seasonality.

Clean and harmonize four datasets by removing index keys and creating a common label to join stocks, three months sales, two years orders, and sales three months for inventory analysis.

Explore seasonality across monthly, weekly, and daily patterns and forecast replenishment by applying seasonal averages to category data.

Develop seasonality in inventory forecasting by averaging monthly sales by category and month across two years, then apply the seasonality to improve reordering with lead time considerations.

Learn how to calculate beginning and ending inventory from daily sales data, accounting for received stock and items in transit. Use the inventory pipeline to determine correct order quantities.

Learn to calculate beginning and ending inventory in a three-month window using Python and pandas, applying groupby and transform to shift beginning stock and align endings with the following day.

Create a stock status tool using a three-month inventory dataset, join by date, handle missing days, and apply a stock status function to evaluate beginning, received, and sales.

Calculate product attributes, revenue, cost, and profit, then merge with stock data to identify in-transit items by filtering orders in the last 10 days.

Learn to compute the current stock from inventory data using group by and aggregation in Python, set up the dataset, and explore ABC classifications for stock management.

Explore ABC analysis to segment products by importance, highlighting A items as critical for safety stock and revenue, and see how the inventories package automates the calculations.

Learn to perform ABC analysis on inventory, map drivers to volume and margin, and identify slow-moving and high-profit segments to optimize product mix.

Explore inventory calculations in a supply chain context, including calculating average demand during sales days, standard deviation, lead times by tier, days of sales, inventory turns, and filter-based analysis.

Explore inventory calculations in Python, including demand averages for in stock days, days of sales and stock, stock cost, and standard deviation using lambda-based filtering.

Consolidate inventory data into a final dataset and compute profit, unit revenue, and three-month sales using ABC, product mix, and recalculations for inventory analysis.

analyze inventory calculations by tracking total requested, current stock, and last stock on hand to compute three months of profit and explore data joining strategies.

Adjust for seasonality in inventory calculations by merging revenue, profit, and average demand from product attributes and computing three-month revenue. Normalize non-selling periods by setting seasonality to one.

Learn how to apply a min-max inventory policy using average daily sales, lead time, safety stock, and standard deviation, with abc segmentation and a 95% service level.

Calculate demand for inventory using time-based adjustments and the average lead time, with a 10-day example, to determine inventory requirements.

Learn how to set service levels using volume and margin drivers, compute safety stock from lead-time variability, and apply normal distribution and ppf concepts to inventory calculations.

Compute the reordering policy from current stock and total requested to set min and max levels, and place an order up to max when inventory calculations warrant it.

Integrate last sale and lost sales data and stock on hand to enrich inventory calculations, including current stock value and three-month revenue.

Send inventory calculations and stock reports to the database, verify max stock, total requested, and in transit items, and review the Mongo stocks report.

Schedule the inventory algorithm on Linux servers using cron and crontab for daily, weekly, or replenishment-based real-time execution. Compare Linux cron to Windows task scheduler for cloud deployments.

Explore scheduling Python scripts with cron on Mac and Linux, and Windows Task Scheduler, including setting cron expressions, handling spaces in directories, and testing a simple script.

Troubleshoot Python-based scheduling for inventory replenishment and verify logs and stock reports. Install missing modules and run scripts as the correct user to ensure automation.

Develop an inventory app that shows the requested order quantity, current stock, and 90-day sales, enables Excel-like filtering, and visualizes three-month revenue and category margins.

Connect to the stocks database and import the latest report by cleaning brand whitespace, selecting max report date, and building a one-page inventory app using core components and libraries.

Select and shape inventory data to power an application, choosing stock value, three months revenue and cogs, then compute inventory turns and build filters for subfamily, brand, and product mix.

Build a dashboard layout for inventory using four data frames (filter data, ranks linked, red alarm, orange alert) and three plots, with a subfamily dropdown set to all.

Configure interactive ranking controls with dropdowns to rank items by user-selected attributes and time ranges, such as revenue or stock value, from highest to lowest.

Discover the data table's extensive features for ranking data, including filtering options, column selection, paging, and CSV download, enabling flexible data exploration.

Identify red and orange alarms to guide replenishment decisions when stock dips or delays occur. Prioritize critical items to sustain cash flow, meet demand, and reduce missed revenue.

Define the callback function Updatable, assign names to its arguments, initialize data as the dataset, and set up an event-driven update that changes data based on subfamily and brand conditions.

Learn to compute inventory turns and margin, rank categories by performance, and integrate three-month revenue, sales, and current stock value for data-driven inventory analysis.

Define ranking data columns and identify orange and red alarm conditions using volume and margin drivers, current stock, and in-transit items.

Build a four-panel subplot pie chart to compare three months revenue and current stock value across ABC categories, with labeled data, a right legend, and cohesive colors for harmony.

Learn to create bar and ranking charts in python for inventory, selecting the top 50 items and placing rank on the y-axis, with three-month data on profit and stock value.

Finalize inventory app by implementing ranking and columns, managing current and ordered stock, converting data to dect records, filtering records, and configuring red and orange alerts for end-of-day verification.

Learn hands-on debugging of a Python inventory application, fixing errors in component naming, class definitions, and data filters. Practice adjusting layouts to ensure the UI updates correctly.

Identify and fix bugs and data filtering issues in the inventory app, normalize headings to uppercase, enable category filters, view revenues and stock value, and prepare for deployment.

Deploy the application to the cloud by provisioning a 24/7 virtual machine on any provider, including Azure. Azure offers a free account with 12 months of services and $200 credit.

Deploy a server by selecting a subscription, creating a resource group, choosing a virtual machine size, configuring login options, and setting up networking, with cost estimates and deployment time.

learn to deploy a flask application for inventory in the supply chain by configuring a server, installing python 3.8, enabling apache and wsgi, and logging in as a road user.

Learn to set up a Flask app for inventory management by configuring Apache, creating a WSGI entry, and testing the server on a virtual machine.

Resolve server issues by ensuring the inventory path uses lowercase, aligning the configuration, then run and reload the app after updating to Python 3.8 to confirm the inventory operates correctly.

Deploy a dash app as a placeholder for the inventory application and plan to connect to a remote database by setting up a database server on a second virtual machine.