Introduction to Python for Data Professionals

In this course, you'll learn:

Python Fundamentals for Data Work

• Configuring Python projects
• Data types: strings, integers, floats, lists, tuples, dictionaries
• Control flow: if-else logic, loops, conditional statements
• Functions: defining and reusing logic, parameters, return values
• Working with files: open, read/write, working with .csv, .json, .txt
• Installing packages

Data Wrangling with Pandas

• Creating Series and DataFrames from lists, dictionaries, or files
• Reading from CSV, Excel, JSON, and SQL sources
• Indexing, selecting, and filtering rows/columns
• Data cleaning techniques
• Renaming columns and reformatting data
• Grouping and aggregation
• Merging/joining datasets
• Transforming data

Exploratory Data Analysis & Visualization

• Summary statistics: mean, median, mode, std dev, percentiles
• Value counts and distributions
• Correlation matrices and scatterplot analysis
• Creating plots with matplotlib
• Enhancing visuals with labels, legends, color, layout
• Saving figures for use in reports or presentations

Automation & Reporting

• Creating reusable functions for data pipelines
• Automating daily/weekly data pulls and transformations
• Logging results or exceptions to a file
• Organizing projects using a folder-based structure
• Using environment variables or config files for credentials and parameters

Overview of Predictive Modeling

• Predictive modeling use cases in business and analytics
• The modeling workflow
• Selecting features, training a model, making predictions
• Using scikit-learn to create a basic linear or logistic regression model
• Evaluating model results with simple metrics
• Interpreting outputs