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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q100-Q105):

NEW QUESTION # 100
Which storage system is best suited for storing and retrieving large volumes of unstructured data, such as images or logs?

Answer: B


NEW QUESTION # 101
Which of the following are true characteristics of bootstrapping in statistics?
(Choose two)

Answer: B,C


NEW QUESTION # 102
Which actions are valid techniques for handling erroneous categorical values in a dataset?
(Choose two)

Answer: A,C


NEW QUESTION # 103
Which characteristics are typically associated with unstructured data formats?
(Choose two)

Answer: B,D


NEW QUESTION # 104
What is the main advantage of using Seaborn over Matplotlib when creating complex statistical visualizations in Python?

Answer: C


NEW QUESTION # 105
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