Certified Entry-Level Data Analyst with Python (PCED-30-02)

Organizations that rely on spreadsheets or ad hoc reporting often struggle to turn raw data into repeatable analysis without adding specialist headcount. This PCED™ training gives IT Directors and Training Managers a structured path for data analysts and IT Practitioners who already have basic programming exposure and need Python-based data manipulation, cleaning, scraping, and visualization skills.

Plan for 32+ hours per learner across the listed modules, with additional lessons extending the path. For change management, Team Leads can assign the course in phases: Jupyter and NumPy foundations first, then Pandas dataset work, web scraping, and visualization. That makes it easier to align learning with reporting backlogs or analytics modernization efforts.

CBT Nuggets Playlists can sequence the rollout by role or milestone, while Team Reporting helps managers track progress and completion across the team.

IT teams complete this training to make Python a practical part of everyday data workflows, not just a certification topic. The course maps to common analyst tasks: working in Jupyter Notebooks, manipulating arrays with NumPy, cleaning and grouping datasets with Pandas, extracting web data with BeautifulSoup/CSS selectors/XPath, and communicating results with Matplotlib and Seaborn.

• Cleaner reporting pipelines: Practitioners practice filtering, transforming, sorting, grouping, and cleaning Pandas datasets.
• Faster data preparation: Teams build comfort with NumPy arrays, data types, broadcasting, indexing, and array operations.
• More reliable external data collection: Analysts learn web scraping patterns using BeautifulSoup, CSS selectors, combinators, pseudo-classes, and XPath predicates.
• Clearer stakeholder communication: Teams create scatterplots, bar charts, pie charts, statistical visuals, and interactive Matplotlib figures.

Knowledge

• How Jupyter Notebooks support code execution, documentation, and iterative analysis.
• Core NumPy concepts, including arrays, multidimensional structures, data types, fancy indexing, broadcasting, and random number generation.
• Pandas Series and DataFrame fundamentals for structured dataset work.
• Dataset cleaning, grouping, filtering, transforming, sorting, and function application patterns in Pandas.
• Web scraping concepts using BeautifulSoup, CSS selectors, CSS combinators, pseudo-classes, XPath, and XPath predicates.
• Visualization concepts in Matplotlib and Seaborn for relational, categorical, and statistical plots.

Ability

• Build repeatable Python analysis workflows in notebooks.
• Manipulate numeric and multidimensional data with NumPy.
• Prepare datasets in Pandas for analysis and reporting.
• Extract targeted elements from web pages using scraping techniques covered in the course.
• Create charts and visualizations that help stakeholders interpret data.
• Prepare for the Certified Entry-Level Data Analyst with Python (PCED™) certification path.