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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

1. When analyzing a dataset of customer ages, you calculate the mean, median, and mode. What does it indicate if all three values are approximately equal?

A) The dataset has a normal distribution
B) The dataset is positively skewed
C) The dataset contains outliers
D) The dataset is uniformly distributed


2. When joining two tables in a relational database using SQL, which clause is used to specify the relationship between their columns?

A) GROUP BY
B) FROM
C) JOIN ON
D) HAVING


3. In supervised learning, what is the primary goal when training a model on labeled data?

A) To cluster data points into similar groups
B) To analyze unstructured data formats like text
C) To discover hidden patterns without outputs
D) To learn a function that maps inputs to known outputs


4. What is the primary reason for converting all categorical labels to lowercase during the data cleaning process?

A) To reduce memory usage in the dataset
B) To make string comparison operations slower
C) To improve data visualization aesthetics
D) To avoid treating the same category as different due to case differences


5. Which command should a data analyst use to install the pandas library in a Python environment using the Python Package Index?

A) python install pandas
B) install pip pandas
C) python get pandas
D) pip install pandas


Solutions:

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

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