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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Data Analysis Fundamentals20%- Introduction to Data Analysis
  • 1. Data analysis concepts and terminology
  • 2. Types of data (structured, unstructured, semi-structured)
  • 3. Data analysis process lifecycle
- Data Collection and Preparation
  • 1. Data import/export operations
  • 2. Data sources and acquisition methods
  • 3. Data cleaning and preprocessing basics
Python Programming for Data Analysis30%- Python Data Types and Structures
  • 1. Lists, tuples, dictionaries, sets
  • 2. Numbers, strings, booleans
  • 3. Data type conversions
- Control Flow and Functions
  • 1. Conditional statements (if, elif, else)
  • 2. Loops (for, while)
  • 3. Function definitions and parameters
  • 4. Return values and scope
- File Operations
  • 1. Writing to files
  • 2. Reading from files (text, CSV)
  • 3. Context managers (with statement)
Working with Data Using Python Libraries30%- Pandas Library
  • 1. Data selection and filtering
  • 2. Handling missing data
  • 3. DataFrame operations (merge, join, concat)
  • 4. GroupBy operations
  • 5. Series and DataFrame structures
- NumPy Fundamentals
  • 1. Vectorized operations
  • 2. Array indexing and slicing
  • 3. Basic statistical functions
  • 4. NumPy arrays and operations
- Data Visualization
  • 1. Seaborn introduction
  • 2. Creating basic charts (line, bar, scatter, histogram)
  • 3. Customizing plots
  • 4. Matplotlib basics
Applied Data Analysis Projects20%- Data Analysis Workflow
  • 1. Analysis and modeling
  • 2. Results interpretation and presentation
  • 3. Problem definition
  • 4. Data exploration and cleaning
- Exploratory Data Analysis (EDA)
  • 1. Descriptive statistics computation
  • 2. Correlation analysis
  • 3. Pattern identification
  • 4. Data distribution analysis

Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions:

1. Which practices enhance both readability and professionalism in spreadsheet formatting?
(Choose two)

A) Using uniform date formats across columns
B) Applying consistent font and cell styling
C) Aligning column headers to the left
D) Centering all data regardless of type


2. What is the primary advantage of using web scraping as a data collection method in business analytics?

A) It guarantees real-time data accuracy
B) It allows automation of data collection from public sources
C) It eliminates the need for data cleaning
D) It ensures compliance with privacy laws


3. Which of the following would best validate the uniqueness of a primary key in a Pandas DataFrame?

A) df['id'].nunique()
B) df['id'].fillna()
C) df['id'].duplicated().any()
D) df['id'].isnull().sum()


4. Which Python library is most commonly used to establish a connection to a SQLite database and perform SQL operations?

A) psycopg2
B) pymysql
C) sqlalchemy
D) sqlite3


5. Which practices are considered part of professional Python scripting standards?
(Choose two)

A) Including in-line comments and docstrings
B) Avoiding error handling to keep code clean
C) Using consistent indentation and naming conventions
D) Avoiding use of modules and libraries


Solutions:

Question # 1
Answer: A,B
Question # 2
Answer: B
Question # 3
Answer: C
Question # 4
Answer: D
Question # 5
Answer: A,C

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