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| Section | Weight | Objectives |
|---|
| Working with Data Using Python Libraries | 30% | - NumPy Fundamentals
- 1. Vectorized operations
- 2. Basic statistical functions
- 3. NumPy arrays and operations
- 4. Array indexing and slicing
- Pandas Library
- 1. Handling missing data
- 2. Data selection and filtering
- 3. DataFrame operations (merge, join, concat)
- 4. GroupBy operations
- 5. Series and DataFrame structures
- Data Visualization
- 1. Matplotlib basics
- 2. Seaborn introduction
- 3. Creating basic charts (line, bar, scatter, histogram)
- 4. Customizing plots
|
| Data Analysis Fundamentals | 20% | - Introduction to Data Analysis
- 1. Types of data (structured, unstructured, semi-structured)
- 2. Data analysis concepts and terminology
- 3. Data analysis process lifecycle
- Data Collection and Preparation
- 1. Data cleaning and preprocessing basics
- 2. Data import/export operations
- 3. Data sources and acquisition methods
|
| Applied Data Analysis Projects | 20% | - Data Analysis Workflow
- 1. Results interpretation and presentation
- 2. Problem definition
- 3. Data exploration and cleaning
- 4. Analysis and modeling
- Exploratory Data Analysis (EDA)
- 1. Correlation analysis
- 2. Descriptive statistics computation
- 3. Data distribution analysis
- 4. Pattern identification
|
| Python Programming for Data Analysis | 30% | - File Operations
- 1. Writing to files
- 2. Context managers (with statement)
- 3. Reading from files (text, CSV)
- 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. Data type conversions
- 2. Lists, tuples, dictionaries, sets
- 3. Numbers, strings, booleans
|
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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q96-Q101):
NEW QUESTION # 96
What is a major challenge in aggregating data from multiple sources?
Response:
- A. Increased algorithmic complexity
- B. Duplicate records and format inconsistencies
- C. Excessive disk space usage
- D. Overfitting in predictive models
Answer: B
NEW QUESTION # 97
Which of the following improves the readability of a spreadsheet used for collaborative data analysis?
- A. Converting numeric values into images
- B. Removing gridlines from all sheets
- C. Adding freeze panes to lock header rows
- D. Using colored fonts to highlight every cell
Answer: C
NEW QUESTION # 98
When joining two tables in a relational database using SQL, which clause is used to specify the relationship between their columns?
- A. HAVING
- B. FROM
- C. GROUP BY
- D. JOIN ON
Answer: D
NEW QUESTION # 99
Which methods are typically used to assess relationships between variables in exploratory data analysis?
(Choose two)
- A. Histogram analysis
- B. Box plots
- C. Scatter plots
- D. Correlation matrix
Answer: C,D
NEW QUESTION # 100
What is the correct sequence to connect to a database and execute a query using the sqlite3 module in Python?
- A. Execute → Connect → Commit → Close
- B. Connect → Execute → Commit → Close
- C. Connect → Commit → Execute → Close
- D. Commit → Connect → Execute → Close
Answer: B
NEW QUESTION # 101
......
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