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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

1. Which operation would most efficiently apply element-wise multiplication to two NumPy arrays of equal size?

A) numpy.outer()
B) array1 * array2
C) array1.dot(array2)
D) numpy.matrix()


2. Which Python library is most commonly used to establish a connection to a SQLite database and perform SQL operations?

A) sqlite3
B) psycopg2
C) sqlalchemy
D) pymysql


3. Which SQL clause is most appropriate when you need to filter records that meet a specific condition during data retrieval in an analytics pipeline?

A) ORDER BY
B) GROUP BY
C) HAVING
D) WHERE


4. Which of the following libraries is specifically optimized for high-performance numerical computation with multi-dimensional arrays in Python?

A) seaborn
B) matplotlib
C) pandas
D) NumPy


5. Which actions are considered part of basic data validation tasks in Python using Pandas?
(Choose two)

A) Sorting the data frame by index
B) Checking for duplicate rows
C) Confirming numeric columns contain only integers
D) Replacing NULL values with placeholders


Solutions:

Question # 1
Answer: B
Question # 2
Answer: A
Question # 3
Answer: D
Question # 4
Answer: D
Question # 5
Answer: B,C

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