Python for Data Science Automation (Course 1)

Automate Business Processes with Python

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Advance your career with Python
By learning how to automate business processes

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:

  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.

Gain the Confidence to Build Python Business Automations


Crafted for Business Analysts

that need to combine data science and programming

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.
 

Learn Python Business Automation from Scratch

Learn by integrating tools inside a real forecasting project

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.

Learn how to tackle Business Process Automation

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

We teach you everything you need to know

No Prerequisites
We teach you everything you need to know.
For (Serious) Beginners
You should be open to learning programming for data science.
Project-Based
Learn python by building a forecasting and reporting system

You'll love learning this way.

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What Transformation Can You Expect?

Here's where you're headed when you take this course.


Undergo a Complete Transformation

Learn the essential tools that build your data science foundations


In Python for Data Science Automation, you will learn:

  • Pandas for Data Wrangling
  • Data Visualization
  • SQL Databases
  • Modules and Python Programming
  • VSCode for Python
  • Jupyter Notebook Automation with Papermill
  • Forecasting with Sktime

Example of what you create

You'll make automated Jupyter Notebook reports that forecast:

  • Customers
  • Products
  • By Time Period (Month, Quarter, Year)
  • And any combination of these parameters you choose!

Example of what you learn

Learn how to bring insights to light at scale:

  • Use plotnine to make fully customizable visualizations
  • Automate the visualization and reporting process with papermill

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How it works

Your path to learning Python for Data Science Automation

Three Streamlined Steps

Your path to learning Python for Data Science Automation

is simplified into 3 streamlined steps.

1

Data Analysis Foundations

2

Time Series Forecasting

3

Reporting Automation

Part 1: Data Analysis Foundations

In part 1, you learn the foundations of data analysis.

  • Learn how to use pandas in-depth with over 5-hours of training just focusing on data wrangling.
  • Learn how to work with SQL databases by creating your own SQLite database.
  • Learn how to use VSCode as a python development environment.
  • You’ll also begin building a python package to store your python workflow automation functions.


Part 2: Time Series Forecasting

In part 2, you:

  • Focus on Pandas time series and forecasting with Sktime, a state-of-the-art forecasting library in python.
  • Learn how to work with time series in pandas and how to make python functions that simplify repetitive tasks.
  • Build much of the core software for your company’s forecasting automation.

Part 3: Reporting Automation

In part 3, you focus on Python Reporting. You:

  • Make report-quality visualizations with plotnine.
  • Set up templatized Jupyter Notebook Reports.
  • Create a Jupyter file automation with Papermill.


Challenges & Cheat Sheets

Your learning is reinforced with:

  • Workflow Diagram: Used to break down the business process into automation steps
  • Cheat Sheets: Developed to make it easy to navigate the Python ecosystem
  • Challenges: Designed to test your abilities & solidify your knowledge


Certificate upon completion

Earn your certificate upon full completion of Python for Data Science Automation.

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Bonuses

But wait, there's more!

Unlock Two Valuable Bonuses!

Automate Python Scripts with Windows Task Scheduler and Mac Automator

As an automation bonus, you learn how to automate your Python Scripts and Business Reports using two valuable productivity-enhancing tools: Windows Task Scheduler and Mac Automator.

Summary of what you get

A methodical training plan that goes from concept to automation ($3,000 value)

  • Part 1: Foundations of Data Analysis
  • Part 2: Time Series & Forecasting
  • Part 3: Reporting & Automation
  • Challenges, Diagrams, & Cheat Sheets
  • Certificate (Upon Completion)
  • 2 Valuable Bonuses: Automate Python Scripts with Windows Task Scheduler & Mac Automator

$3999 Value

Purchase today for 👇.

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Upgrade your skills to an enterprise-level

Gain skills that get results.

Learn Enterprise-Grade Tools

This course is designed to teach you using VSCode, a powerful development environment that will help you develop python software.

Build Enterprise-Grade Software

You'll learn by building a Python Package that extends pandas for your reporting automation.

Get started now!



 

For Employers

Common questions.

Why should you or your employee(s) take this course?

  • Improve organizational results. Organizational results improve through making data-driven decisions. This course teaches students how to complete a end-to-end data science project with executive project deliverables. Students learn how to perform data analysis, become proficient at programming, build python software, and most importantly deliver results in the form of reports and data products that decision-makers can use to improve results.
  • Gain a competitive advantage. Data is increasing faster than the number of people that can analyze it. Learning how to leverage python is the solution. Students learn data analysis in-depth helping your organization to stay ahead of the competition.
  • Automate to reduce errors and expedite repetitive tasks. Not only will students learn the tools to perform data analysis, but they will also learn how to automate repetitive business processes that produce fewer errors and deliver stakeholder results on demand.
  • Empower stakeholders to make better decisions. Whether the end-user is a customer or your organization's executive leadership, students learn how to make data products that help end-users make better decisions at the speed of python.
 

More Information

Instructor, Curriculum, and FAQs

Your Instructor


Matt Dancho
Matt Dancho

Founder of Business Science and general business & finance guru, He has worked with many clients from Fortune 500 to high-octane startups! Matt loves educating data scientists on how to apply powerful tools within their organization to yield ROI. Matt doesn't rest until he gets results (literally, he doesn't sleep so don't be suprised if he responds to your email at 4AM)!


Course Curriculum


  Welcome to Python for Data Science Automation
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  Python Packages Used In This Course
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  Getting Help (Important!)
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  Part 1 - Foundations of Data Analysis with Python
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  Module 0: Business Project & Course Setup
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  0.1 Course Project Download
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  0.2 Key Resources
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  0.3 Software Downloads
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  0.4A CONDA ENVIRONMENT SETUP
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  0.4B VIRTUAL ENVIRONMENT SETUP (Alternative to Conda Environment Setup)
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  04C: EASY INSTALL (Less Reproducible, but Will Get You Moving Forward)
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  0.5 VSCode Setup (for Data Science)
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  0.6 Recap & Code Checkpoint
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  Module 1: Jumpstart - Sales Analysis (Time Series)
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  1.1 Importing from Python Packages
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  1.2 Data Import
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  1.3 Examining the Data
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  1.4 Joining the Excel Tables
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  1.5 Data Wrangling (Transformation)
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  1.6 Time Series Plotting
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  1.6.1 Sales Analysis, Part 1 - Sales By Month
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  1.6.2 Sales Analysis, Part 2 - Sales by Week & Category 2
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  1.7 Save & Recap
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  1.8 Checkpoint: Module 1 - Jumpstart
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  Module 2: SQL Databases & Python Packages
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  2.1 Importing Files
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  2.2 SQL Alchemy Databases
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  2.2.1 Creating a Database
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  2.2.2 Reconnecting to the Database
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  2.3 Creating a Function: collect_data()
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  2.4 Intro to Python Packages & Modules
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  2.5 Checkpoint: Module 2 - SQL Databases
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  3.0 Pandas Data Analysis Deep Dive
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  3.1 Data Structures for Analysis
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  3.1.1 Python Objects (How Python Works)
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  3.1.2 Data Structures for Analysis
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  3.1.3 Python's Built-in Data Structures & Data Types
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  3.1.4 Casting (Data Type Conversion)
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  3.2 Pandas Core Functions: Deep-Dive
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  3.2.1 Column-Wise Data Frame Selection
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  3.2.2 Arranging Rows
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  3.2.3 Rowwise Filtering (Slicing)
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  3.2.4 Calculated Columns (Mutating / Assigning)
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  3.2.5 Grouping & Aggregate / Apply
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  3.2.6 Renaming Columns
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  3.2.7 Reshaping (Pivoting)
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  3.2.8 Joins
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  3.2.9 Splitting & Uniting Text Columns
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  3.2.10 The Apply Function (In-Depth)
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  3.2.11 [Advanced] The Pipe: Method Chaining Helper
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  3.3 Code Checkpoint: Module 3 - Pandas Core
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  ⛰️ 3.4 Challenge #1: Test Your Data Wrangling Skills
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  💡 3.4.1 Introduction to JupyterLab Notebooks
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  3.4.2 Challenge #1 Assignment
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  Part 2: Time Series Forecasting Automation
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  4.0 Data Exploration & Time Series Fundamentals
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  4.1 Pandas Profiling
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  4.1.1 Profile Report Components
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  4.1.2 Profiling Workflow
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  4.2 Time Series Fundamentals
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  4.2.1 Pandas Datetime Basics
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  4.2.2 Periods & Time-Based Groupings (Resampling)
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  4.2.3 Measuring Change
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  4.2.4 Cumulative Calculations (Expanding Windows)
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  4.2.5 Rolling Window Calculations
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  4.4 Code Checkpoint: Module 4 - Time Series Fundamentals
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  5.0 Functional Programming
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  5.1 Functional Programming with Pandas [Outlier Detection Function]
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  5.1.1 Examining Pandas Functions
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  5.1.2 Detect Outliers Function
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  5.1.2.1 Building an Outlier Detection Function
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  5.1.2.2 Adding Checks: Raising Informative Exceptions
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  5.1.2.3 Finalizing the Function
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  5.2 Summarize By Time Function
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  5.2.1 Beginning the Summarize by Time Function
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  5.2.2 Debugging the Value Column 🐜
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  5.2.3 Handling the Output Formatting (Wide Data)
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  5.2.4 Finalizing the Function & Adding to Our Extensions Package
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  5.3 Code Checkpoint: Module 5 - Functional Programming
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  6.0 Forecasting with Sktime
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  6.1 Introduction to Sktime Forecasting
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  6.1.1 Data Summarizations (Always Needed Prior to Forecasting)
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  6.1.2 Single Time Series Forecasting: AutoARIMA()
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  6.1.3 Multiple Time Series Forecasting (Modeling within For-Loops)
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  6.2 The ARIMA Forecast Automation
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  6.2.1 Building the ARIMA Forecasting Automation
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  6.2.2 The forecasting.py Module
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  6.3 Checkpoint
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  Challenge 2: Make an AutoETS() Forecast Automation
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  Part 3: Visualization & Report Automation
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  7.0 Visualization with Plotnine
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  7.1 Plotnine Basics
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  7.1.1 Data and Geometry Basics
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  7.1.2 Formatting Basics
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  7.2 Plotnine In-Depth
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  7.2.1 Scatter Plot and Line Plot | geom_point() and geom_line()
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  7.2.2 Column Plot (Bar Plot) | geom_col()
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  7.2.3 Histograms and Density Plots | geom_histogram() and geom_density()
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  7.2.4 Box Plots and Violin Plots | geom_boxplot() and geom_violin()
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  7.2.5 Text and Labels | geom_text() and geom_label()
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  7.2.6 Plot Customizations | Facets, Scales, & Themes
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  Mini-Challenge: Convert our Cyberpunk 2077 Plot to a Business Science Theme
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  7.3 Building the Plot Forecast Function
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  7.3.1 Forecast Plot
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  7.3.2 Forecast Plot Automation
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  7.4 Code Checkpoint - Module 7
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  8.0 Debugging: Building & Debugging the Database Read/Write Automation
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  8.1 Database Update: Developing Read/Write Functionality
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  8.1.1 Preparation & Validation Functionality
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  8.1.2 Write Forecast to Database Function
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  8.1.3 Read Forecast from Database Function
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  8.1.4 Add Functions to Database Module
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  8.2 Debugging the Automation Workflow
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  8.2.1 Testing the Summarizing & Forecasting Automation Workflow
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  8.2.2 Testing the Write Database Workflow
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  8.3 Code Checkpoint - Module 8
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  Module 9 | Jupyter Reporting Automation (Part 1): Automating Jupyter Notebooks
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  9.0 Automatic Forecasting... The Update Database Script
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  9.1 Jupyter Sales Analysis
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  9.2 Papermill
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  9.2.1 Making Parameters
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  9.2.2 Setting Up the Directory (Programmatically)
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  9.2.3 Creating the Jupyter Template
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  9.2.4 Automating Jupyter Notebooks with Papermill
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  9.3 Run Reports Function: Version 1
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  9.3.1 Handling Paths
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  9.3.2 Building the Reporting Function
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  9.3.3 Adding Run Reports to the Python Module
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  9.4 Checkpoint: Module 9 - Jupyter Automation, Part 1
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  Module 10: Jupyter Automation (Part 2): Automating HTML & PDF Reports
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  10.0 NB Convert: HTML & PDF Conversion Setup
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  10.1 NB Convert Automation
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  10.2 Run Reports Function, Version 2: run_reports()
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  10.3 Upgrading our My Pandas Extensions & Making run_forecast_reports.py
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  10.4 Checkpoint: Module 10 - Jupyter Automation, Part 2
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  ✨✨✨ BONUSES: Scheduling Python Scripts
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  BONUS #1: Windows Task Scheduler
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  BONUS #2: Mac Automator & Calendar App Automation
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  CONGRATULATIONS!!! You Did It! 🥳🥳🥳
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  SPECIAL BONUS CONTENT 😲
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Frequently Asked Questions


When does the course start and finish?
The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.
How long do I have access to the course?
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.
What if I am unhappy with the course?
We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund.

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