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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
SQL and Database Integration	10%	- 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. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering
Data Visualization and Communication	15%	- Visualization principles and best practices 1. Color, layout and clarity 2. Choosing appropriate chart types 3. Audience-focused presentation - 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. Structuring insights and conclusions 2. Written and verbal presentation techniques
Data Modeling and Machine Learning Basics	20%	- 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. Overfitting, underfitting and generalization 2. Accuracy, precision, recall, F1-score 3. Hyperparameter tuning basics
Data Acquisition and Preprocessing	30%	- Data collection, integration and storage 1. Data collection methods and sources 2. Data integration and merging 3. Data formats and storage systems - 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. Indexing, filtering, sorting and grouping 3. Data reshaping and aggregation
Data Exploration and Statistical Analysis	25%	- Descriptive statistics 1. Measures of central tendency and dispersion 2. Frequency distributions and percentiles 3. Correlation and covariance analysis - 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. In supervised learning, what is the primary goal when training a model on labeled data?

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

2. Why is min-max scaling applied before using algorithms like K-Nearest Neighbors or SVM?

A) To make all features interpretable as binary values
B) To compress all values into integers
C) To prevent features with larger scales from dominating distance calculations
D) To convert strings into numeric values

3. What is the key difference between the .iloc[] and .loc[] accessors when working with Series or DataFrames?

A) .iloc[] allows label-based selection
B) .iloc[] is position-based, while .loc[] is label-based
C) .loc[] can only be used on Series
D) .loc[] does not allow slicing

4. When analyzing a sales dataset using Pandas, which of the following techniques help extract key insights by grouping and summarizing the data?
(Choose two)

A) Applying agg() to calculate multiple metrics like mean and max
B) Using groupby() to compute total sales per region
C) Using fillna() to replace missing product prices
D) Using rename() to change column headers

5. Which conditions typically necessitate data normalization or scaling before analysis?
(choose two)

A) Distance-based modeling algorithms
B) Boolean feature encoding
C) Presence of duplicate data
D) Features with different units and scales

Solutions:

Question # 1
Answer: B

Question # 2
Answer: C

Question # 3
Answer: B

Question # 4
Answer: A,B

Question # 5
Answer: A,D