PCED Introduction to Python Data Analytics
Duration: 4 Days
Description
This four-day course is the on-ramp into data analytics for developers who know Python basics but have never formally worked with data. It starts conceptually — data types, sources, storage formats, and the ethical and legal considerations that now shape how data is handled professionally — before moving into applied work: cleaning and normalizing data, running basic statistical operations, and using NumPy for numeric computation. The course closes with a module most technical training skips: how to actually communicate findings, through visualizations, written summaries, and presentations built for both technical and non-technical audiences. It’s built to align with the PCED certification, giving participants a credential alongside the skill.
Audience
This course is designed for developers taking their first formal step into data analytics — whether that’s a personal career pivot, a team need for someone to own basic reporting, or preparation for more advanced analytics work down the line. The direct benefit for a developer is turning “I can write a script to process a CSV” into “I can run a structured, ethical, and well-communicated analysis a business stakeholder will actually trust.” It’s a strong fit for developers who’ve picked up scattered data-handling skills on the job and want the conceptual grounding — data ethics, proper statistical basics, audience-aware reporting — that self-directed learning usually skips.
Objectives
- Distinguish between data types, structures, and formats
- Collect, store, and describe data using ethical and legal best practices
- Write and execute Python code to manipulate and process data
- Clean, normalize, and perform simple statistical operations on datasets
- Apply NumPy and built-in tools for numeric computation
- Conduct basic exploratory data analysis (EDA)
- Interpret common data visualizations and generate reports
- Present findings effectively to technical and non-technical audiences
Prerequisites
Participants should have basic familiarity with Python, including variables, data types, and simple loops or conditionals. No prior experience with data analysis or statistics is required. Comfort with general computer use and working with files is expected.
Related Python Courses
See the full Python training roadmap and course directory for how this fits into a broader learning path.
This course assumes the fundamentals from Introduction to Python or PCEP Python Introduction. The natural next step is the full PCAD Python Data Analytics course, and developers who want deeper visualization skills specifically should also see Python Data Visualization. For the broader machine learning picture, see Python for Data Science. Curious how this connects to AI work? See Python Foundations.
Course Outline
Module 1: Introduction to Data and Data Analysis Concepts
- Types and Classifications of Data
- Data Collection, Storage, and Transformation
- Data Sources and Basic Collection Methods
- Data Storage Formats and Systems
- The Data Lifecycle and Its Impact
- Roles in the Data Domain
- Types of Analytics and Practical Use Cases
- Ethics, Privacy, and Legal Compliance in Data
Module 2: Python Basics for Data Analysis
- Variables, Arithmetic Operations, and Strings
- Working with Core Data Structures
- Functions and Code Reusability
- Control Flow with Conditionals and Loops
- Error Handling and Exception Management
- Using Python Modules and Packages
Module 3: Working with Data and Performing Simple Analyses
- Reading and Writing Data Files
- Data Cleaning and Preprocessing
- Data Normalization and Formatting
- Aggregations and Descriptive Statistics in Python
- Numerical Operations with NumPy and Built-In Tools
- Exploratory Data Analysis (EDA) Basics
Module 4: Communicating Insights and Reporting
- Understanding and Interpreting Basic Visualizations
- Summarizing Findings with Clarity
- Create Reports with Visual Support
- Presenting Data to Diverse Audiences