Python Institute PCAD-31-02 Valid Practice Materials, PCAD-31-02 Sample Questions Answers

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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Acquisition and Preprocessing30%- Data cleaning and validation
  • 1. Quality assurance and validation techniques
  • 2. Handling missing, duplicate and invalid values
  • 3. Data standardization and transformation
- Data collection, integration and storage
  • 1. Data formats and storage systems
  • 2. Data integration and merging
  • 3. Data collection methods and sources
- Data preparation with Pandas and NumPy
  • 1. Indexing, filtering, sorting and grouping
  • 2. Data reshaping and aggregation
  • 3. Data structures: Series, DataFrame, ndarray
Topic 2: Data Exploration and Statistical Analysis25%- Inferential statistics
  • 1. Hypothesis testing and confidence intervals
  • 2. Probability concepts and distributions
  • 3. Statistical significance and interpretation
- Exploratory data analysis
  • 1. Feature selection and dimensionality reduction basics
  • 2. Identifying patterns, trends and outliers
- Descriptive statistics
  • 1. Measures of central tendency and dispersion
  • 2. Frequency distributions and percentiles
  • 3. Correlation and covariance analysis
Topic 3: Data Modeling and Machine Learning Basics20%- Model performance and optimization
  • 1. Hyperparameter tuning basics
  • 2. Overfitting, underfitting and generalization
  • 3. Accuracy, precision, recall, F1-score
- Supervised learning fundamentals
  • 1. Model training, testing and evaluation
  • 2. Regression: linear, multiple, polynomial
  • 3. Classification: logistic regression, k-NN, decision trees
Topic 4: SQL and Database Integration10%- SQL querying
  • 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
  • 2. Subqueries and filtering
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
- Python-database connectivity
  • 1. Connecting to SQLite, MySQL or PostgreSQL
  • 2. Executing queries and retrieving results
  • 3. Error handling and best practices
Topic 5: Data Visualization and Communication15%- Visualization principles and best practices
  • 1. Color, layout and clarity
  • 2. Audience-focused presentation
  • 3. Choosing appropriate chart types
- Data storytelling and reporting
  • 1. Written and verbal presentation techniques
  • 2. Structuring insights and conclusions
- Visualization with Matplotlib and Seaborn
  • 1. Heatmaps, pair plots and correlation matrices
  • 2. Customization and styling
  • 3. Line, bar, scatter, histogram, box plots

>> Python Institute PCAD-31-02 Valid Practice Materials <<

PCAD-31-02 Valid Practice Materials Exam Pass For Sure | Python Institute PCAD-31-02: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q20-Q25):

NEW QUESTION # 20
Which element is essential to justify a conclusion drawn from a dataset?

Answer: A


NEW QUESTION # 21
Which two benefits are most closely associated with using object-oriented programming (OOP) in data analysis pipelines?
(Choose two)

Answer: A,D


NEW QUESTION # 22
Which Python control structures are commonly used for data filtering and iterative operations in real-world analytics tasks?
(Choose two)

Answer: A,D


NEW QUESTION # 23
Which Python tool or library is best suited for retrieving data from HTML tables on web pages?

Answer: B


NEW QUESTION # 24
Which file formats are commonly used for hierarchical or nested data structures in data acquisition?
(Choose two)

Answer: A,C


NEW QUESTION # 25
......

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