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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Data Modeling and Machine Learning Basics	20%	- Model performance and optimization 1. Accuracy, precision, recall, F1-score 2. Hyperparameter tuning basics 3. Overfitting, underfitting and generalization - Supervised learning fundamentals 1. Regression: linear, multiple, polynomial 2. Classification: logistic regression, k-NN, decision trees 3. Model training, testing and evaluation
Data Exploration and Statistical Analysis	25%	- Exploratory data analysis 1. Identifying patterns, trends and outliers 2. Feature selection and dimensionality reduction basics - Inferential statistics 1. Statistical significance and interpretation 2. Probability concepts and distributions 3. Hypothesis testing and confidence intervals - Descriptive statistics 1. Measures of central tendency and dispersion 2. Frequency distributions and percentiles 3. Correlation and covariance analysis
Data Visualization and Communication	15%	- Data storytelling and reporting 1. Written and verbal presentation techniques 2. Structuring insights and conclusions - Visualization with Matplotlib and Seaborn 1. Line, bar, scatter, histogram, box plots 2. Customization and styling 3. Heatmaps, pair plots and correlation matrices - Visualization principles and best practices 1. Audience-focused presentation 2. Color, layout and clarity 3. Choosing appropriate chart types
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 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
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. Executing queries and retrieving results 2. Connecting to SQLite, MySQL or PostgreSQL 3. Error handling and best practices

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Python Institute - PCAD-31-02 - The Best Reliable Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Practice Materials

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q35-Q40):

NEW QUESTION # 35
Why is it important to use a test dataset separate from the training dataset when evaluating a machine learning model?

A. To evaluate the model's performance on unseen data
B. To optimize the model's training accuracy
C. To ensure the loss function is minimized
D. To reduce the number of iterations during training

Answer: A

NEW QUESTION # 36
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. HAVING
B. ORDER BY
C. GROUP BY
D. WHERE

Answer: D

NEW QUESTION # 37
What is the correct SQL statement to permanently remove a table named customers from a database?

A. REMOVE TABLE customers;
B. TRUNCATE TABLE customers;
C. DROP TABLE customers;
D. DELETE TABLE customers;

Answer: C

NEW QUESTION # 38
When creating a line chart with Matplotlib, which function is most appropriate to add a text label at a specific data point?

A. plt.title()
B. plt.xlabel()
C. plt.annotate()
D. plt.legend()

Answer: C

NEW QUESTION # 39
What will the following function return?
def average(*args):
return sum(args) / len(args)
average(4, 5, 6)

A. Error
B. 0
C. 5.0
D. (4, 5, 6)

Answer: C

NEW QUESTION # 40
......

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