PCAD Python Data Analytics

PCAD Python Data Analytics

Duration: 5 Days

Description

This five-day course covers the complete data analytics workflow, from raw data to a communicated finding, and is built specifically toward the PCAD certification. It’s broader than a typical data science course in one important way: it includes SQL for querying and manipulating data alongside Python, reflecting how analytics actually works in most real organizations, where data lives in a database before it ever reaches a notebook. From there it moves through descriptive and inferential statistics, regression modeling, data cleaning and reshaping with Pandas, and a full visualization module using Matplotlib and Seaborn — closing with a dedicated focus on designing narratives and reports that make findings usable, not just accurate.

Audience

This course is for developers who want a complete, certified analytics skill set — not just the Python side of it, but the SQL, statistics, and communication skills that make analytics work valuable to a business. The direct benefit for a developer transitioning toward data-focused work is credibility: the PCAD credential, combined with the SQL-plus-Python skill combination taught here, directly matches what most data analyst and junior data scientist job descriptions ask for. It’s equally valuable for developers already handling ad hoc reporting duties who want to replace informal, script-by-script analysis with a structured, repeatable, and properly validated workflow.

Objectives

  • Collect, clean, and validate data from a variety of sources
  • Work with Python to process, structure, and transform data
  • Query and manipulate data using SQL and integrate it into Python workflows
  • Apply descriptive and inferential statistics to analyze datasets
  • Perform feature engineering and fit basic regression models
  • Visualize data effectively using Matplotlib and Seaborn
  • Communicate insights to technical and non-technical stakeholders using clear narratives and visuals
  • Apply ethical and legal considerations in data handling and reporting

Prerequisites

Participants should have a basic understanding of Python, including variables, data types, control flow (if/else, loops), and writing simple functions. Familiarity with lists, dictionaries, and basic file handling is recommended. No prior experience with data analysis, statistics, or databases is required, as these topics will be introduced from the ground up.

Related Python Courses

See the full Python training roadmap and course directory for how this fits into a broader learning path.

This course builds on the foundation in PCED Introduction to Python Data Analytics or Python for Data Science. Developers wanting deeper visualization skills specifically should also review Python Data Visualization. For developers who need strong intermediate Python first, start with PCAP Intermediate Python. This course pairs naturally with our AI track — see Python Foundations and Python OpenAI API for Developers.

Course Outline

Module 1: Data Acquisition and Pre-Processing

  • Data Types, Structures, and Collection Methods
  • Data Storage and Quality Considerations
  • Data Integration, Cleaning, and Transformation
  • Data Preparation, Validation, and Ethics

Module 2: Programming and Database Skills

  • Python Fundamentals for Data Processing
  • Writing Clean, Maintainable Python Code
  • Object-Oriented Programming in Python
  • SQL for Data Querying and Manipulation
  • Python and Database Integration

Module 3: Statistical Analysis

  • Descriptive Statistics and Data Distributions
  • Correlation and Exploratory Data Visualization
  • Inferential Statistics and Bootstrapping
  • Regression Analysis and Model Interpretation

Module 4: Data Analysis and Modeling

  • Data Cleaning and Reshaping with Pandas
  • Numerical Computing with NumPy
  • Feature Engineering and Exploratory Analysis
  • Introduction to Modeling and Evaluation

Module 5: Data Communication and Visualization

  • Creating Visualizations with Matplotlib and Seaborn
  • Enhancing Visual Clarity and Aesthetics
  • Communicating Insights to Diverse Audiences
  • Designing Effective Data Narratives and Reports