Certified Associate Data Analyst with Python (PCAD™)

Many organizations collect more data than their teams can clean, analyze, or explain. When analysis depends on a few spreadsheet experts or outside contractors, reporting slows down and decisions rest on numbers no one can fully trace.

Who it fits: IT Directors, Training Managers, and Team Leads building Python data-analysis capability for IT practitioners and junior analysts who already know some Python and need a structured path to the PCAD™ credential. Newer staff may start with Certified Entry-Level Data Analyst with Python (PCED™) first.

What it covers: Your team works in Jupyter Notebooks with NumPy, pandas, web scraping with BeautifulSoup and CSS selectors, descriptive and inferential statistics, SQL transactions, and introductory machine learning such as k-nearest neighbor and k-means. It also covers PEP style guidelines, which help keep shared code readable and maintainable.

Time investment: About 49 hours across 61 skills. At three to four hours a week, most learners finish in three to four months. Each skill runs under an hour, so it fits around project work.

Change-management note: Lessons cover handling missing values based on why they are missing and separating correlation from causation. These habits reduce misleading reports before they reach stakeholders.

Throughout the course, challenge-and-solution exercises use real-world datasets, such as real estate sales records. Learners practice applying each skill as they go, not just watching.

IT and data teams complete this training to turn raw, messy data into reliable reporting without leaning on outside contractors. The course is built around real-world datasets, not toy examples, so teams practice the same workflows they’ll use on the job.

• Cleaner data, fewer bad decisions: Practitioners learn to load CSV datasets in pandas, explore them with functions like info and describe, and handle missing values based on why the data is missing, not just by dropping or filling rows.
• Automated data collection: Teams build web-scraping skills with BeautifulSoup and CSS selectors. That means less manual copy-and-paste when pulling data from web sources.
• Reporting stakeholders can act on: Hands-on challenges using real-world data have learners create clear visual summaries, including pie charts, histograms, scatter plots, and Matplotlib figures that organize multiple views of the data.
• Safer database work and sounder analysis: Learners practice SQL aggregates, grouping, and transactions with rollbacks to protect data integrity. They also apply descriptive and inferential statistics, including hypothesis testing, bootstrapping, and checks that a correlation is not mistaken for causation.

For Team Leads, the course also covers PEP style guidelines, which helps keep shared analysis code readable and maintainable across the team.

Knowledge

• How NumPy arrays, broadcasting, fancy indexing, and data types support fast numerical work, and why pandas builds on NumPy
• How pandas Series and DataFrames load, explore, group, filter, transform, and sort real-world CSV datasets
• Why values go missing in a dataset, and how the type of missingness should drive the cleanup approach
• How BeautifulSoup and CSS selectors target specific elements on a web page
• Core descriptive and inferential statistics, including correlation versus causation, hypothesis testing, and bootstrapping
• SQL fundamentals for analysts, including aggregates, grouping, and transactions with rollbacks

Ability

• Build reproducible analyses in Jupyter Notebooks and use hotkeys to work efficiently
• Clean and reshape CSV datasets, including electric vehicle data, with pandas functions such as read_csv, info, and describe
• Scrape structured data from web pages with BeautifulSoup and precise CSS selectors
• Communicate findings with Matplotlib and Seaborn, including histograms, box plots, scatter plots, pie charts, and multi-panel figures
• Train and evaluate k-nearest neighbor and k-means models, and read a classification report for precision, recall, and F1 score
• Write maintainable, PEP-compliant Python and apply SQL transactions that roll back cleanly when an error occurs