Python For Marketing
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 Programming courses are available as live, online classes for individuals.
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
- Acquire skills for performing basic analysis on text data
- 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
Prerequisites
Outline
- Paths, directories, and filenames
- Navigating through filesystem
- Create, copy, and move files and directories
- Starting Python
- Using the interpreter
- Running a Python script
- Using an IDE
- Variables
- Basic data types (Strings, Integers, Floating Point, Boolean)
- Writing to the screen
- Converting between data types
- Operators
- Conditional statements (if, elif, else)
- Boolean expressions
- While loop
- Break and continue
- Lists and tuples
- Indexing and slicing
- Iterating through sequences
- For loop
- List comprehensions
- Generator expressions
- Nested expressions
- Opening a text file
- Reading a text file
- Writing to a text file
- Creating dictionaries
- Creating sets
- Iterating through dictionaries and sets
- Defining functions
- Parameters
- Variable scope
- Returning values
- Lambda functions
- Exceptions
- Try/catch/finally
- Importing modules
- Namespaces
- Creating packages
- Defining classes
- Constructors
- Instance methods and data
- Attributes
- Inheritance
- 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
- Collecting data from a publicly available web API
- ND arrays
- NumPy operations
- Broadcasting
- Structured arrays
- Vectorization
- 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
- Uni and multivariate statistical summaries
- Handling missing data
- Aggregating data
- Pivot tables
- Merging dataframes
- Pandas string methods
- Creating histograms
- Creating bar plots
- Creating box plots
- Creating scatter plots
- Group-by plotting
- Plot formatting
- p-values
- T-test
- Chi-squared test
- Linear Regression
- Logistic Regression
- K-means clustering algorithm
- Hierarchical clustering algorithm
- RFM Analysis
- Tokenizing text
- Stopwords
- Cleaning and processing text
- Creating word clouds
- Named Entity Recognition
- Sentiment analysis
Training Materials
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)