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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: SQL and Database Integration	10%	- SQL querying 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering - Relational database concepts 1. Tables, keys, relationships and normalization - Python-database connectivity 1. Connecting to SQLite, MySQL or PostgreSQL 2. Executing queries and retrieving results 3. Error handling and best practices
Topic 2: Data Visualization and Communication	15%	- 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. Color, layout and clarity 3. Choosing appropriate chart types - Visualization with Matplotlib and Seaborn 1. Heatmaps, pair plots and correlation matrices 2. Customization and styling 3. Line, bar, scatter, histogram, box plots
Topic 3: Data Modeling and Machine Learning Basics	20%	- Model performance and optimization 1. Hyperparameter tuning basics 2. Overfitting, underfitting and generalization 3. Accuracy, precision, recall, F1-score - Supervised learning fundamentals 1. Regression: linear, multiple, polynomial 2. Model training, testing and evaluation 3. Classification: logistic regression, k-NN, decision trees
Topic 4: 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. Probability concepts and distributions 3. Hypothesis testing and confidence intervals
Topic 5: Data Acquisition and Preprocessing	30%	- Data collection, integration and storage 1. Data integration and merging 2. Data collection methods and sources 3. Data formats and storage systems - Data preparation with Pandas and NumPy 1. Data structures: Series, DataFrame, ndarray 2. Indexing, filtering, sorting and grouping 3. Data reshaping and aggregation - Data cleaning and validation 1. Data standardization and transformation 2. Handling missing, duplicate and invalid values 3. Quality assurance and validation techniques

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

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

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

2. What is a best practice when organizing data in a spreadsheet for analysis?

A) Apply conditional formatting to entire rows only
B) Merge cells to reduce redundancy
C) Place charts in the middle of data tables
D) Use consistent column headers across all sheets

3. Which SQL command should a data analyst use to modify existing records in a database table based on specific conditions?

A) SELECT
B) CREATE
C) UPDATE
D) INSERT

4. Which of the following practices improves clarity when combining visualizations and textual explanations in a report?

A) Avoid summarizing the data verbally
B) Include concise textual summaries that highlight the chart's message
C) Use visualizations alone without titles or captions
D) Repeat all numerical data in both the chart and text

5. Which of the following best describes the use of relational databases in analytics?

A) Used only for image processing
B) Cannot enforce schema
C) Suitable for managing tabular structured data
D) Best for real-time log ingestion

Solutions:

Question # 1
Answer: C

Question # 2
Answer: D

Question # 3
Answer: C

Question # 4
Answer: B

Question # 5
Answer: C