Certified Entry-Level Data Analyst with Python (PCED™)

For IT leaders: Many organizations have staff who need to work with data, such as sales exports, CSV files, and API feeds, but have no Python or analytics background. That gap often means slow, error-prone spreadsheet work or reliance on outside help. This PCED course builds entry-level data analysis skills from the ground up and supports preparation for the Certified Entry-Level Data Analyst with Python (PCED™) certification.

Role fit: Best for IT practitioners, operations staff, and employees moving toward analyst work. No prior Python is assumed. The course starts with variables and lists in Jupyter notebooks, then moves to cleaning dirty datasets, descriptive statistics, exploratory data analysis, and visualization. Developers who already know Python may move quickly through the early skills.

Time investment: 32 skills, about 25.6 hours per learner. At two to three hours a week, most learners can finish in roughly two to three months. Each skill runs under an hour, so it fits around regular duties.

Change-management notes: Learners install Anaconda and JupyterLab locally, so confirm workstation policies allow it. The course teaches technical skills first and theory second, including the data lifecycle and a data quality framework. It ends with data storytelling, which trains analysts to tailor findings to decision-makers. That helps reports reach the people who can act on them.

How the course supports the outcome: Hands-on challenges and downloadable practice datasets, including sales, customer segmentation, and EDA walkthroughs, let the team practice on realistic data before applying it to organizational datasets.

Most IT teams already sit on useful data: ticket exports, asset inventories, sales or usage reports in CSV and JSON. What they often lack is a repeatable way to turn that raw data into decisions. This course closes that gap with walkthroughs built on realistic datasets, so your team practices the same work it will do on the job.

• Turn messy exports into trustworthy data. Learners work through an intentionally dirty dataset with missing values and impossible entries like an age of -5 or 999. They apply a data quality framework to clean it, which means fewer bad numbers reaching reports.
• Prepare files for analysis in Python. Learners work with Python data types, CSV files, and JSON files, including how data is structured and written back out for reuse. That gives teams a clearer path from raw exports to usable analysis inputs.
• Move from raw numbers to business answers. In the descriptive analysis walkthrough, learners summarize a year of monthly product sales, including units, price, and cost. They also practice core exploratory data analysis tasks like sorting, filtering, and grouping data.
• Report findings stakeholders can act on. Learners critique misleading real-world charts, build visualizations in Jupyter, and practice data storytelling. That includes tailoring reports to what the audience already knows and making sure findings reach the people who can act on them.

The result for IT Directors and Team Leads is a team that can pull data from CSV and JSON sources, analyze it in Python, and present clear conclusions without relying on outside analysts for routine reporting.

Knowledge

• How Python variables, data types, lists, dictionaries, sets, and strings store and organize data for analysis tasks
• The stages of the data lifecycle (collection, storage, and processing) and the difference between data, information, and knowledge
• Core descriptive statistics, including means, medians, standard deviations, and frequency distributions, and what they reveal about large datasets
• Common data quality problems such as missing values and impossible entries (for example, an age of -5 or 999), and a basic framework for judging data quality
• How CSV files, JSON files, APIs, and other sources feed analysis workflows in Python and Jupyter
• Why some visualizations mislead, such as charts with missing axes or totals that are not adjusted for population

Ability

• Write and run Python code interactively in Jupyter notebooks, using functions and control flow to make data tasks reusable
• Load data from CSV, JSON, and API sources and clean messy datasets into a consistent, usable format
• Use exception handling to keep scripts running when they hit bad or unexpected values
• Run descriptive analysis and basic exploratory data analysis tasks, including sorting, filtering, and grouping data
• Build basic visualizations in Jupyter and spot misleading charts before they reach stakeholders
• Shape findings into clear reports by tailoring terminology and context to the audience and the people who make decisions