PCAD-31-02 Valid Exam Bootcamp - PCAD-31-02 Study Materials

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

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PCAD-31-02 Valid Exam Bootcamp | Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) 100% Free Study Materials

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q142-Q147):

NEW QUESTION # 142
Which of the following practices improves clarity when combining visualizations and textual explanations in a report?

Answer: A


NEW QUESTION # 143
Which practices are typically part of the data integration process?
(Choose two)

Answer: B,D


NEW QUESTION # 144
What is the primary difference between the == operator and the is operator in Python when used in a data pipeline?

Answer: B


NEW QUESTION # 145
Which of the following is the most suitable data structure in Python for representing a tabular row with column names?

Answer: A


NEW QUESTION # 146
Which of the following techniques are commonly used in the standardization of textual or categorical data during preprocessing?
(Choose two)

Answer: C,D


NEW QUESTION # 147
......

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