Certified Associate Data Analyst with Python (PCAD)

The Python Certified Associate Data Analyst with Python (PCAD) training course from the Python Institute empowers professionals with intermediate skills to acquire, clean, manipulate, analyze, and visualize data using Python. Offered through flexible formats including Instructor-Led Training (ILT), Virtual Instructor-Led Training (VILT), and in-house sessions, this course prepares you for the PCAD certification exam (exam code: PCAD-31-0x). It covers data extraction, statistical analysis, predictive modeling, and visualization, leveraging Python libraries like NumPy, Pandas, Matplotlib, Seaborn, and SciKit-Learn. Ideal for data analysts in South Africa and beyond, this training builds expertise for decision-making and advances career opportunities in data science and analytics.

Target Audience

This course is ideal for:

• Junior data analysts seeking to enhance their Python skills.
• Software developers transitioning to data-focused roles.
• Professionals aiming for careers in data science or analytics.
• Candidates preparing for the PCAD certification exam in South Africa.

Prerequisites

To succeed in this course, participants should have:

• Basic knowledge of Python programming (PCAP certification recommended).
• Understanding of fundamental data analytics concepts.
• Familiarity with statistics and basic machine learning principles (recommended).

What You Will Learn

In this course, you will gain expertise in:

• Extracting and cleaning data from various sources using Python.
• Manipulating and analyzing data with NumPy and Pandas.
• Building predictive models using SciKit-Learn.
• Creating visualizations with Matplotlib and Seaborn.

Benefits of the Course

By completing this course, you will:

• Master intermediate data analytics skills with Python.
• Be prepared to pass the PCAD certification exam (PCAD-31-0x).
• Stand out in the job market with a recognized credential.
• Advance toward professional-level data science roles.

Course Outline

• Data Acquisition and Cleaning
  • Extracting Data from CSV, HTML, and JSON Files
  • Cleaning and Preprocessing Data with Python
  • Handling Missing Values and Data Validation
• Data Manipulation and Analysis
  • Data Manipulation with Pandas and NumPy
  • Performing Statistical Analysis on Datasets
  • Summarizing Data for Insights
• Predictive Modeling and Machine Learning
  • Introduction to Machine Learning with SciKit-Learn
  • Building Simple Predictive Models
  • Evaluating Model Performance
• Data Visualization and Reporting
  • Creating Visualizations with Matplotlib and Seaborn
  • Interpreting Data Insights for Decision-Making
  • Best Practices for Data Presentation