Now is the time to get certified as Data Analyst with Python!

Python Institute PCED-30-02: Certified Entry-Level Data Analyst with Python

There are six Practice Tests with preparation questions from all knowledge areas

to prepare for the PCED-30-02 exams at the Python Institute.

The first Practice Test should be worked through in Practice Mode.

The other five Practice Tests should be taken in Exam Mode.

They have the same number of questions as the official exam.

You will have 40 questions and 60 minutes.

Every question has an explanation and/or a Try-It-Yourself-Code

which you can run to better understand the topic.

You can download the Try-It-Yourself-Code for all questions.

(The download link will be in your welcome message.)

Why learn Data Analysis with Python?

Data analysis has become one of the most valuable skills in today's job market, and Python is the leading programming language for data work. Here's why learning data analysis with Python is a smart investment in your career:

1. Python Is the Industry Standard

Python dominates the data analysis field. Major companies like Google, Facebook, Amazon, and Netflix use Python for data-driven decisions. Learning Python means learning the language that professionals actually use every day.

2. Powerful, Easy-to-Learn Libraries

Python offers specialized libraries that make data analysis fast and intuitive:

- Pandas for data manipulation and cleaning

- NumPy for numerical computations

- Matplotlib and Seaborn for data visualization

- Jupyter Notebooks for interactive analysis

You don't need to rebuild everything from scratch—these tools let you focus on insights, not infrastructure.

3. High Demand, Strong Salaries

Data analysts are in demand across every industry: finance, healthcare, e-commerce, marketing, and more. According to job market reports, data analyst positions consistently rank among the fastest-growing roles, with competitive salaries worldwide.

4. Entry-Level Friendly

Unlike many programming paths, data analysis with Python is accessible to beginners. You don't need a computer science degree. With the right training, you can go from zero to job-ready in months, not years.

5. Real-World Impact

Data analysis turns raw numbers into actionable insights. You'll learn to:

- Identify trends and patterns

- Create compelling visualizations

- Support business decisions with evidence

- Tell stories with data

6. Future-Proof Skill

As AI and automation grow, the ability to work with data becomes even more critical. Python is also the primary language for machine learning and AI, so your skills will scale as you advance.

By mastering data analysis with Python, you're not just learning a tool—you're building a career foundation that opens doors across industries, with room to grow into data science, machine learning, and beyond.

Exam Syllabus:

Introduction to Data and Data Analysis Concepts:

Define and Classify Data

Describe Data Sources, Collection Methods, and Storage

Explain the Data Lifecycle and Its Management

Understand the Scope of Data Science, Analytics, and Analysis

Identify key questions each type answers and their business relevance.

Python Basics for Data Analysis:

Work with Variables and Data Types

Use Python Data Collections and Sequences

Use Functions and Handle Exceptions

Control Program Flow with Conditionals and Loops

Use Modules and Packages

Working with Data and Performing Simple Analyses:

Read and Write Data Using Files

Clean and Prepare Data for Analysis

Perform Basic Analytical Computations

Conduct Basic Exploratory Data Analysis (EDA)

Communicating Insights and Reporting:

Understand Basic Principles of Data Visualization

Apply Fundamentals of Data Storytelling

Create Clear and Concise Analytical Reports

Communicate Insights Effectively in Presentations