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| Section | Weight | Objectives |
|---|
| Topic 1: Python Programming for Data Analysis | 30% | - File Operations
- 1. Context managers (with statement)
- 2. Writing to files
- 3. Reading from files (text, CSV)
- Control Flow and Functions
- 1. Function definitions and parameters
- 2. Loops (for, while)
- 3. Conditional statements (if, elif, else)
- 4. Return values and scope
- Python Data Types and Structures
- 1. Numbers, strings, booleans
- 2. Data type conversions
- 3. Lists, tuples, dictionaries, sets
|
| Topic 2: Applied Data Analysis Projects | 20% | - Data Analysis Workflow
- 1. Problem definition
- 2. Analysis and modeling
- 3. Results interpretation and presentation
- 4. Data exploration and cleaning
- Exploratory Data Analysis (EDA)
- 1. Pattern identification
- 2. Data distribution analysis
- 3. Correlation analysis
- 4. Descriptive statistics computation
|
| Topic 3: Data Analysis Fundamentals | 20% | - Introduction to Data Analysis
- 1. Data analysis process lifecycle
- 2. Data analysis concepts and terminology
- 3. Types of data (structured, unstructured, semi-structured)
- Data Collection and Preparation
- 1. Data sources and acquisition methods
- 2. Data import/export operations
- 3. Data cleaning and preprocessing basics
|
| Topic 4: Working with Data Using Python Libraries | 30% | - Pandas Library
- 1. Data selection and filtering
- 2. Series and DataFrame structures
- 3. GroupBy operations
- 4. DataFrame operations (merge, join, concat)
- 5. Handling missing data
- Data Visualization
- 1. Seaborn introduction
- 2. Creating basic charts (line, bar, scatter, histogram)
- 3. Customizing plots
- 4. Matplotlib basics
- NumPy Fundamentals
- 1. Vectorized operations
- 2. Basic statistical functions
- 3. NumPy arrays and operations
- 4. Array indexing and slicing
|
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Python Institute PCAD-31-02 Exam Questions - Choice Of Certified Professionals [2026]
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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q37-Q42):
NEW QUESTION # 37
What is the purpose of using df.loc[2:5, 'column1'] in a Pandas DataFrame?
- A. Returns the entire DataFrame
- B. Returns rows 2 to 4 in 'column1'
- C. Returns a new DataFrame excluding 'column1'
- D. Returns rows 2 through 5 in 'column1'
Answer: D
NEW QUESTION # 38
Which Python constructs are useful for comparing and managing object identity in a data pipeline?
(Choose all that apply)
- A. id() function to retrieve unique object identifiers
- B. str() function to convert data formats
- C. is operator for memory-level comparison
- D. == operator for checking value equivalence
Answer: A,C
NEW QUESTION # 39
When merging two Pandas DataFrames using a common key, which parameters are essential to ensure accurate and conflict-free results?
(Choose two)
- A. ignore_index
- B. suffixes
- C. dropna
- D. on
Answer: B,D
NEW QUESTION # 40
Which of the following correctly demonstrates the use of parameterized queries using Python's sqlite3 module?
- A. cursor.execute("SELECT * FROM users WHERE id = ?", (user_id,))
- B. cursor.execute("SELECT * FROM users WHERE id = ?", user_id)
- C. cursor.execute("SELECT * FROM users WHERE id = ':id'", {'id': user_id})
- D. cursor.execute("SELECT * FROM users WHERE id = " + user_id)
Answer: A
NEW QUESTION # 41
Which metric is most appropriate for evaluating the accuracy of a classification model?
- A. Confusion matrix
- B. R-squared
- C. Mean squared error
- D. Precision
Answer: D
NEW QUESTION # 42
......
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