Python Programming for Digital Marketing Overview
This in-person or online Python for Marketers training course teaches marketing professionals how to gather, manipulate, and analyze data using the Python programming language. The first two days ramp participants up on Python. Then participants learn how to use their new Python skills to gather marketing data, clean it, and create compelling data visualizations. In addition, participants learn how to run A/B tests on groups of data, segment customer data, and much more.
If your team already knows Python, we have a 3-day Python for Marketers class without the introduction to Python Programming.
Location and Pricing
Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.
In addition, some courses are available as live, instructor-led training from one of our partners.
Objectives
- Get started with the Python programming language
- Gather data by scraping websites and querying web APIs
- Effectively clean, aggregate, and manipulate data
- Create compelling data visualizations
- Apply statistical techniques for running A/B tests on groups of data
- Use popular techniques to segment customer data
- Perform regression analysis to identify factors that have an impact on topics of interest
- Acquire skills for performing basic analysis on text data
Prerequisites
Some programming experience is helpful but not required. Students should be comfortable working with files and folders and understand basic statistics.
Outline
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Introduction
Getting Acquainted with the Command Line
- Paths, directories, and filenames
- Navigating through filesystem
- Create, copy, and move files and directories
Introduction to Python
- Starting Python
- Using the interpreter
- Running a Python script
- Using an IDE
Variables, data types, and operators
- Variables
- Basic data types (Strings, Integers, Floating Point, Boolean)
- Writing to the screen
- Converting between data types
- Operators
Flow Control
- Conditional statements (if, elif, else)
- Boolean expressions
- While loop
- Break and continue
Sequences
- Lists and tuples
- Indexing and slicing
- Iterating through sequences
- For loop
- List comprehensions
- Generator expressions
- Nested expressions
Using Files
- Opening a text file
- Reading a text file
- Writing to a text file
Dictionaries and Sets
- Creating dictionaries
- Creating sets
- Iterating through dictionaries and sets
Functions
- Defining functions
- Parameters
- Variable scope
- Returning values
- Lambda functions
Handling exceptions
- Exceptions
- Try/catch/finally
Modules and Packages
- Importing modules
- Namespaces
- Creating packages
Classes
- Defining classes
- Constructors
- Instance methods and data
- Attributes
- Inheritance
Scraping data from web sites
- Connecting to websites using requests package
- Parsing static HTML/CSS pages using BeautifulSoup package
- Scraping dynamic website content using Selenium
- Advanced: Building a web spider using scrapy
Using Web APIs
- Collecting data from a publicly available web API
Numerical Python with NumPy
- ND arrays
- NumPy operations
- Broadcasting
- Structured arrays
- Vectorization
Data Manipulation using Pandas
- Series vs Dataframe
- Datatypes in Pandas
- Importing data: CSV/Excel/JSON/HTML
- Dataframe indexing
- Selecting subsets of dataframe
- Creating and deleting variables
- Identifying duplicate data
Advanced Pandas Methods
- Uni and multivariate statistical summaries
- Handling missing data
- Aggregating data
- Pivot tables
- Merging dataframes
- Pandas string methods
Data Visualization using Matplotlib and Seaborn
- Creating histograms
- Creating bar plots
- Creating box plots
- Creating scatter plots
- Group-by plotting
- Plot formatting
A/B Testing for group differences
- p-values
- T-test
- Chi-squared test
Regression Analysis
- Linear Regression
- Logistic Regression
Customer segmentation
- K-means clustering algorithm
- Hierarchical clustering algorithm
- RFM Analysis
Text Analysis
- Tokenizing text
- Stopwords
- Cleaning and processing text
- Creating word clouds
- Named Entity Recognition
- Sentiment analysis
Conclusion
Training Materials
All Python for Marketers training students receive comprehensive courseware.
Software Requirements
- Any Windows, Linux, or macOS operating system
- Anaconda Python 3.6 or later
- Additional Python libraries, including seaborn, selenium, and BeautifulSoup
- Spyder IDE and Jupyter notebook (Comes with Anaconda)
