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| Section | Weight | Objectives |
|---|
| Topic 1: Working with Data Using Python Libraries | 30% | - Data Visualization
- 1. Seaborn introduction
- 2. Customizing plots
- 3. Creating basic charts (line, bar, scatter, histogram)
- 4. Matplotlib basics
- NumPy Fundamentals
- 1. Basic statistical functions
- 2. Vectorized operations
- 3. Array indexing and slicing
- 4. NumPy arrays and operations
- Pandas Library
- 1. DataFrame operations (merge, join, concat)
- 2. Data selection and filtering
- 3. Series and DataFrame structures
- 4. GroupBy operations
- 5. Handling missing data
|
| Topic 2: Applied Data Analysis Projects | 20% | - Data Analysis Workflow
- 1. Data exploration and cleaning
- 2. Analysis and modeling
- 3. Results interpretation and presentation
- 4. Problem definition
- Exploratory Data Analysis (EDA)
- 1. Data distribution analysis
- 2. Descriptive statistics computation
- 3. Correlation analysis
- 4. Pattern identification
|
| Topic 3: 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
|
| Topic 4: 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. Return values and scope
- 2. Loops (for, while)
- 3. Conditional statements (if, elif, else)
- 4. Function definitions and parameters
- Python Data Types and Structures
- 1. Lists, tuples, dictionaries, sets
- 2. Numbers, strings, booleans
- 3. Data type conversions
|
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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q85-Q90):
NEW QUESTION # 85
Which actions are valid techniques for handling erroneous categorical values in a dataset?
(Choose two)
- A. Normalizing using min-max scaling
- B. Removing rows with invalid labels
- C. Converting all values to integers
- D. Replacing inconsistent labels with a standardized value
Answer: B,D
NEW QUESTION # 86
What is a best practice when organizing data in a spreadsheet for analysis?
- A. Merge cells to reduce redundancy
- B. Apply conditional formatting to entire rows only
- C. Place charts in the middle of data tables
- D. Use consistent column headers across all sheets
Answer: D
NEW QUESTION # 87
When analyzing a sales dataset using Pandas, which of the following techniques help extract key insights by grouping and summarizing the data?
(Choose two)
- A. Using fillna() to replace missing product prices
- B. Applying agg() to calculate multiple metrics like mean and max
- C. Using groupby() to compute total sales per region
- D. Using rename() to change column headers
Answer: B,C
NEW QUESTION # 88
What is the main reason for using try-except blocks in a data analysis script that reads multiple files?
- A. To ensure functions always return None
- B. To automatically restart the script after execution
- C. To enforce strict type annotations
- D. To gracefully handle runtime errors without crashing the program
Answer: D
NEW QUESTION # 89
Which command should a data analyst use to install the pandas library in a Python environment using the Python Package Index?
- A. pip install pandas
- B. install pip pandas
- C. python install pandas
- D. python get pandas
Answer: A
NEW QUESTION # 90
......
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