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| Section | Weight | Objectives |
|---|
| Topic 1: Python Programming for Data Analysis | 30% | - Control Flow and Functions
- 1. Conditional statements (if, elif, else)
- 2. Loops (for, while)
- 3. Return values and scope
- 4. Function definitions and parameters
- Python Data Types and Structures
- 1. Lists, tuples, dictionaries, sets
- 2. Numbers, strings, booleans
- 3. Data type conversions
- File Operations
- 1. Context managers (with statement)
- 2. Reading from files (text, CSV)
- 3. Writing to files
|
| Topic 2: Applied Data Analysis Projects | 20% | - Data Analysis Workflow
- 1. Results interpretation and presentation
- 2. Analysis and modeling
- 3. Problem definition
- 4. Data exploration and cleaning
- Exploratory Data Analysis (EDA)
- 1. Correlation analysis
- 2. Data distribution analysis
- 3. Pattern identification
- 4. Descriptive statistics computation
|
| Topic 3: Data Analysis Fundamentals | 20% | - Data Collection and Preparation
- 1. Data sources and acquisition methods
- 2. Data import/export operations
- 3. Data cleaning and preprocessing basics
- Introduction to Data Analysis
- 1. Data analysis concepts and terminology
- 2. Types of data (structured, unstructured, semi-structured)
- 3. Data analysis process lifecycle
|
| Topic 4: Working with Data Using Python Libraries | 30% | - NumPy Fundamentals
- 1. Vectorized operations
- 2. Basic statistical functions
- 3. Array indexing and slicing
- 4. NumPy arrays and operations
- Data Visualization
- 1. Seaborn introduction
- 2. Matplotlib basics
- 3. Customizing plots
- 4. Creating basic charts (line, bar, scatter, histogram)
- Pandas Library
- 1. Series and DataFrame structures
- 2. DataFrame operations (merge, join, concat)
- 3. GroupBy operations
- 4. Data selection and filtering
- 5. Handling missing data
|
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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q40-Q45):
NEW QUESTION # 40
Which of the following are limitations of manual data collection methods?
(Choose two)
- A. Seamless integration with big data tools
- B. Time-consuming process
- C. Increased chance of human error
- D. High scalability
Answer: B,C
NEW QUESTION # 41
You have a list of product prices, and you need to remove duplicate values while maintaining the fastest performance. Which core data structure should you use?
- A. list
- B. dictionary
- C. tuple
- D. set
Answer: D
NEW QUESTION # 42
Which method allows you to detect and remove rows with duplicate values across all columns in a Pandas DataFrame?
- A. DataFrame.drop_duplicates()
- B. DataFrame.unique()
- C. DataFrame.remove_duplicates()
- D. DataFrame.nunique()
Answer: C
NEW QUESTION # 43
What is the primary reason for converting all categorical labels to lowercase during the data cleaning process?
- A. To reduce memory usage in the dataset
- B. To avoid treating the same category as different due to case differences
- C. To improve data visualization aesthetics
- D. To make string comparison operations slower
Answer: B
NEW QUESTION # 44
Which techniques can be used to select a subset of rows and columns from a DataFrame using labels?
(Choose two)
- A. df.loc[:, 'col1']
- B. df['col1', 'col2']
- C. df.iloc[0:5, 'col2']
- D. df.loc[2:7, ['col1', 'col2']]
Answer: A,D
NEW QUESTION # 45
......
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