Certified Entry-Level Data Analyst with Python (PCED)

The Certified Entry-Level Data Analyst with Python (PCED) training course from the Python Institute equips aspiring data analysts with foundational skills to collect, integrate, clean, validate, 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 PCED certification exam (exam code: PCED-30-01). It covers data collection methods, cleaning techniques, statistical analysis, and visualization, leveraging Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn, while addressing ethical and legal considerations. Ideal for beginners in South Africa and beyond, this training provides hands-on experience to tackle real-world data challenges and lays a foundation for advanced data science careers.

Target Audience

This course is ideal for:

• Aspiring data analysts starting their career in data analytics.
• Professionals transitioning into data roles from other domains.
• Students and researchers seeking Python-based data analysis skills.
• Candidates preparing for the PCED certification exam in South Africa.

Prerequisites

To succeed in this course, participants should have:

• Basic knowledge of Python programming (variables, loops, functions).
• Understanding of fundamental statistics and mathematics (averages, probability).
• Familiarity with data concepts (recommended but not mandatory).

What You Will Learn

In this course, you will gain expertise in:

• Collecting and integrating data ethically from multiple sources using Python.
• Cleaning, standardizing, and validating data with Pandas and NumPy.
• Applying statistical methods to ensure data integrity and insights.
• Creating effective visualizations with Matplotlib and Seaborn.

Benefits of the Course

By completing this course, you will:

• Master foundational data analytics skills with Python.
• Be prepared to pass the PCED certification exam (PCED-30-01).
• Boost your career in entry-level data analyst roles.
• Gain a stepping stone for advanced data science certifications.

Course Outline

• Data Collection, Integration, and Storage
  • Exploring Data Collection Methods (Surveys, Interviews, Web Scraping)
  • Understanding Sampling, Ethical, and Legal Considerations
  • Aggregating Data from Databases, APIs, and File Storage
  • Comparing Data Storage Solutions (Data Warehouses, Data Lakes, Cloud Storage)
• Data Cleaning and Standardization
  • Identifying and Rectifying Erroneous Data (Missing Values, Duplicates)
  • Applying Normalization and Scaling Techniques (Min-Max, Z-Score)
  • Encoding Categorical Variables (One-Hot, Label Encoding)
  • Handling Outliers and Standardizing Data Formats
• Data Validation and Integrity
  • Performing Basic Data Validation (Type, Range, Cross-Reference Checks)
  • Establishing Validation Rules for Data Integrity
• Data Preparation Techniques
  • Working with File Formats (CSV, JSON, XML, TXT)
  • Accessing and Managing Datasets from Various Sources
  • Enhancing Spreadsheet Readability and Formatting
  • Pre-Processing Data (Sorting, Filtering, Splitting for Machine Learning)
• Python Proficiency and SQL Integration
  • Applying Python Syntax and Control Structures
  • Creating Functions and Managing Modules with PIP
  • Executing SQL Queries and Connecting to Databases with Python
  • Implementing Exception Handling and Secure Parameterized Queries
• Descriptive and Inferential Statistics
  • Applying Measures of Central Tendency and Spread
  • Analyzing Data Relationships with Plots and Pearson’s R
  • Using Bootstrapping for Sampling Distributions
  • Implementing Linear and Logistic Regression with Python
• Data Analysis with Pandas and NumPy
  • Managing Data with Pandas (Filtering, Merging)
  • Utilizing DataFrame and Series Relationships
  • Performing Array Operations with NumPy
  • Computing Descriptive Statistics and Grouping Data
• Data Visualization Techniques
  • Creating Visualizations with Matplotlib and Seaborn (Boxplots, Histograms)
  • Assessing Pros and Cons of Data Representations
  • Labeling and Annotating Visualizations
  • Optimizing Clarity with Colors and Legends
• Effective Communication of Data Insights
  • Tailoring Communication for Diverse Audiences
  • Combining Visualizations and Text for Clear Reports
  • Summarizing Findings with Evidence-Based Reasoning