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| Section | Weight | Objectives |
|---|
| Data Analysis Fundamentals | 20% | - Data Collection and Preparation
- 1. Data import/export operations
- 2. Data sources and acquisition methods
- 3. Data cleaning and preprocessing basics
- Introduction to Data Analysis
- 1. Types of data (structured, unstructured, semi-structured)
- 2. Data analysis process lifecycle
- 3. Data analysis concepts and terminology
|
| Working with Data Using Python Libraries | 30% | - Data Visualization
- 1. Matplotlib basics
- 2. Customizing plots
- 3. Creating basic charts (line, bar, scatter, histogram)
- 4. Seaborn introduction
- Pandas Library
- 1. Handling missing data
- 2. Series and DataFrame structures
- 3. GroupBy operations
- 4. DataFrame operations (merge, join, concat)
- 5. Data selection and filtering
- NumPy Fundamentals
- 1. Basic statistical functions
- 2. NumPy arrays and operations
- 3. Array indexing and slicing
- 4. Vectorized operations
|
| Python Programming for Data Analysis | 30% | - File Operations
- 1. Context managers (with statement)
- 2. Reading from files (text, CSV)
- 3. Writing to files
- Control Flow and Functions
- 1. Loops (for, while)
- 2. Conditional statements (if, elif, else)
- 3. Function definitions and parameters
- 4. Return values and scope
- Python Data Types and Structures
- 1. Lists, tuples, dictionaries, sets
- 2. Data type conversions
- 3. Numbers, strings, booleans
|
| Applied Data Analysis Projects | 20% | - Exploratory Data Analysis (EDA)
- 1. Data distribution analysis
- 2. Pattern identification
- 3. Correlation analysis
- 4. Descriptive statistics computation
- Data Analysis Workflow
- 1. Problem definition
- 2. Analysis and modeling
- 3. Results interpretation and presentation
- 4. Data exploration and cleaning
|
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The Python Institute PCAD-31-02 Exam Dumps In PDF File Format
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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q116-Q121):
NEW QUESTION # 116
What is the correct sequence to connect to a database and execute a query using the sqlite3 module in Python?
- A. Connect → Execute → Commit → Close
- B. Connect → Commit → Execute → Close
- C. Commit → Connect → Execute → Close
- D. Execute → Connect → Commit → Close
Answer: A
NEW QUESTION # 117
What is the primary purpose of using annotations in a data visualization?
- A. To highlight important data points and provide contextual information
- B. To replace the need for axes
- C. To ensure faster rendering in large datasets
- D. To enhance interactivity in static plots
Answer: A
NEW QUESTION # 118
Which of the following are true characteristics of bootstrapping in statistics?
(Choose two)
- A. It requires a large population sample
- B. It uses random sampling with replacement
- C. It assumes a normal distribution
- D. It enables confidence interval estimation
Answer: B,D
NEW QUESTION # 119
Which of the following are key differences between Pandas and NumPy arrays?
(Choose two)
- A. Pandas integrates better with tabular data and missing values
- B. Pandas Series are more memory efficient than NumPy arrays
- C. NumPy supports string indexing; Pandas does not
- D. Pandas handles labeled data; NumPy does not
Answer: A,D
NEW QUESTION # 120
What is the best approach when comparing SQL NULL values with Python data during conditional checks?
- A. Use Python's None and SQL's IS NULL clause
- B. Use == None directly in SQL queries
- C. Convert all NULL to string before comparison
- D. Replace NULL with zero before importing
Answer: A
NEW QUESTION # 121
......
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