PCAD-31-02 Exam Reference, New PCAD-31-02 Dumps Ppt

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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Data Exploration and Statistical Analysis	25%	- Inferential statistics 1. Statistical significance and interpretation 2. Hypothesis testing and confidence intervals 3. Probability concepts and distributions - Exploratory data analysis 1. Feature selection and dimensionality reduction basics 2. Identifying patterns, trends and outliers - Descriptive statistics 1. Measures of central tendency and dispersion 2. Correlation and covariance analysis 3. Frequency distributions and percentiles
Data Acquisition and Preprocessing	30%	- Data preparation with Pandas and NumPy 1. Indexing, filtering, sorting and grouping 2. Data reshaping and aggregation 3. Data structures: Series, DataFrame, ndarray - 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. Quality assurance and validation techniques 2. Data standardization and transformation 3. Handling missing, duplicate and invalid values
Data Modeling and Machine Learning Basics	20%	- Supervised learning fundamentals 1. Regression: linear, multiple, polynomial 2. Model training, testing and evaluation 3. Classification: logistic regression, k-NN, decision trees - Model performance and optimization 1. Overfitting, underfitting and generalization 2. Hyperparameter tuning basics 3. Accuracy, precision, recall, F1-score
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. Color, layout and clarity 2. Choosing appropriate chart types 3. Audience-focused presentation - Visualization with Matplotlib and Seaborn 1. Customization and styling 2. Heatmaps, pair plots and correlation matrices 3. Line, bar, scatter, histogram, box plots
SQL and Database Integration	10%	- 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 - SQL querying 1. Subqueries and filtering 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q89-Q94):

NEW QUESTION # 89
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

Answer: A,D

NEW QUESTION # 90
Which practices are considered part of professional Python scripting standards?
(Choose two)

A. Including in-line comments and docstrings
B. Avoiding use of modules and libraries
C. Using consistent indentation and naming conventions
D. Avoiding error handling to keep code clean

Answer: A,C

NEW QUESTION # 91
Which of the following would best validate the uniqueness of a primary key in a Pandas DataFrame?

A. df['id'].nunique()
B. df['id'].duplicated().any()
C. df['id'].isnull().sum()
D. df['id'].fillna()

Answer: B

NEW QUESTION # 92
Which SQL commands can be used to modify the contents of a table's data directly?
(Choose all that apply)

A. SELECT
B. DELETE
C. INSERT
D. UPDATE

Answer: C,D

NEW QUESTION # 93
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. GROUP BY
B. ORDER BY
C. HAVING
D. WHERE

Answer: D

NEW QUESTION # 94
......

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