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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
SQL and Database Integration10%- 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
Data Modeling and Machine Learning Basics20%- Model performance and optimization
  • 1. Overfitting, underfitting and generalization
  • 2. Hyperparameter tuning basics
  • 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
Data Exploration and Statistical Analysis25%- Inferential statistics
  • 1. Statistical significance and interpretation
  • 2. Probability concepts and distributions
  • 3. Hypothesis testing and confidence intervals
- 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
Data Visualization and Communication15%- Visualization principles and best practices
  • 1. Audience-focused presentation
  • 2. Color, layout and clarity
  • 3. Choosing appropriate chart types
- Visualization with Matplotlib and Seaborn
  • 1. Line, bar, scatter, histogram, box plots
  • 2. Heatmaps, pair plots and correlation matrices
  • 3. Customization and styling
- Data storytelling and reporting
  • 1. Structuring insights and conclusions
  • 2. Written and verbal presentation techniques
Data Acquisition and Preprocessing30%- Data cleaning and validation
  • 1. Handling missing, duplicate and invalid values
  • 2. Data standardization and transformation
  • 3. Quality assurance and validation techniques
- 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. Data reshaping and aggregation
  • 2. Indexing, filtering, sorting and grouping
  • 3. Data structures: Series, DataFrame, ndarray

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q25-Q30):

NEW QUESTION # 25
Why is it important to manage object identity correctly in complex data analysis pipelines?

Answer: C


NEW QUESTION # 26
Which of the following would most likely be classified as unstructured data?

Answer: A


NEW QUESTION # 27
What is the correct sequence to connect to a database and execute a query using the sqlite3 module in Python?

Answer: C


NEW QUESTION # 28
Which packages are most commonly used together in a data science workflow for data manipulation and visualization?
(Choose two)

Answer: B,C


NEW QUESTION # 29
What will the following function return?
def average(*args):
return sum(args) / len(args)
average(4, 5, 6)

Answer: C


NEW QUESTION # 30
......

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