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| Section | Weight | Objectives |
|---|
| Topic 1: Data Exploration and Statistical Analysis | 25% | - Inferential statistics
- 1. Probability concepts and distributions
- 2. Hypothesis testing and confidence intervals
- 3. Statistical significance and interpretation
- Exploratory data analysis
- 1. Identifying patterns, trends and outliers
- 2. Feature selection and dimensionality reduction basics
- Descriptive statistics
- 1. Measures of central tendency and dispersion
- 2. Correlation and covariance analysis
- 3. Frequency distributions and percentiles
|
| Topic 2: SQL and Database Integration | 10% | - Relational database concepts
- 1. Tables, keys, relationships and normalization
- Python-database connectivity
- 1. Error handling and best practices
- 2. Executing queries and retrieving results
- 3. Connecting to SQLite, MySQL or PostgreSQL
- SQL querying
- 1. Subqueries and filtering
- 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
|
| Topic 3: Data Acquisition and Preprocessing | 30% | - Data preparation with Pandas and NumPy
- 1. Data structures: Series, DataFrame, ndarray
- 2. Data reshaping and aggregation
- 3. Indexing, filtering, sorting and grouping
- Data cleaning and validation
- 1. Data standardization and transformation
- 2. Quality assurance and validation techniques
- 3. Handling missing, duplicate and invalid values
- Data collection, integration and storage
- 1. Data collection methods and sources
- 2. Data integration and merging
- 3. Data formats and storage systems
|
| Topic 4: Data Visualization and Communication | 15% | - Data storytelling and reporting
- 1. Structuring insights and conclusions
- 2. Written and verbal presentation techniques
- Visualization with Matplotlib and Seaborn
- 1. Line, bar, scatter, histogram, box plots
- 2. Customization and styling
- 3. Heatmaps, pair plots and correlation matrices
- Visualization principles and best practices
- 1. Choosing appropriate chart types
- 2. Audience-focused presentation
- 3. Color, layout and clarity
|
| Topic 5: Data Modeling and Machine Learning Basics | 20% | - Model performance and optimization
- 1. Overfitting, underfitting and generalization
- 2. Accuracy, precision, recall, F1-score
- 3. Hyperparameter tuning basics
- Supervised learning fundamentals
- 1. Model training, testing and evaluation
- 2. Classification: logistic regression, k-NN, decision trees
- 3. Regression: linear, multiple, polynomial
|
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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q105-Q110):
NEW QUESTION # 105
Which packages are most commonly used together in a data science workflow for data manipulation and visualization?
(Choose two)
- A. Pygame
- B. NumPy
- C. flask
- D. pandas
Answer: B,D
NEW QUESTION # 106
What is a major challenge in aggregating data from multiple sources?
Response:
- A. Excessive disk space usage
- B. Duplicate records and format inconsistencies
- C. Increased algorithmic complexity
- D. Overfitting in predictive models
Answer: B
NEW QUESTION # 107
What does the describe() method in Pandas provide when applied to a numerical DataFrame column?
- A. A normalized version of the data column
- B. A histogram of the data distribution
- C. Basic descriptive statistics such as count, mean, and quartiles
- D. Only the standard deviation of the data
Answer: C
NEW QUESTION # 108
Which file formats are commonly used for hierarchical or nested data structures in data acquisition?
(Choose two)
- A. CSV
- B. TSV
- C. JSON
- D. XML
Answer: C,D
NEW QUESTION # 109
Which practices contribute to better exception handling in data pipelines involving file I/O and numerical computations?
(Choose two)
- A. Using specific exception types like FileNotFoundError or ZeroDivisionError
- B. Logging error messages for later inspection during failures
- C. Catching all exceptions using a generic except: clause without specifying the error
- D. Suppressing all error output using pass
Answer: B,D
NEW QUESTION # 110
......
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