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Python Institute PCED-30-02 Exam Syllabus Topics:

SectionWeightObjectives
Python Basics for Data Analysis32.5%- Control flow and functions
  • 1. Basic exception handling
  • 2. Conditional statements, loops, iteration
  • 3. Defining and calling functions, parameters, return values
- Built-in modules for data work
  • 1. math, statistics, datetime, collections, csv
- Introduction to NumPy
  • 1. Arrays, basic operations, indexing, slicing
- Core Python syntax and data types
  • 1. Variables, numbers, strings, booleans
  • 2. Lists, tuples, sets, dictionaries
Introduction to Data and Data Analysis Concepts22.5%- Basic statistical concepts
  • 1. Population, sample, variable, observation
  • 2. Descriptive vs inferential statistics
- Definition and classification of data
  • 1. Difference between data, information, and knowledge
  • 2. Role of data in decision-making and business
  • 3. Process of turning raw data into insights
- Data lifecycle and ethical considerations
  • 1. Privacy, security, bias, and fairness in data
  • 2. Data collection, storage, processing, usage, and sharing
- Data types and measurement scales
  • 1. Qualitative vs quantitative data
  • 2. Nominal, ordinal, interval, ratio scales
Working with Data and Performing Simple Analysis25%- Data cleaning and preparation
  • 1. Handling missing values, duplicates, and errors
  • 2. Formatting and standardizing values
  • 3. Filtering, sorting, transforming data
- Data acquisition and loading
  • 1. Reading text, CSV, and structured files
  • 2. Importing data from external sources
- Data aggregation and grouping
  • 1. Summarizing and grouping datasets
- Exploratory data analysis
  • 1. Identifying patterns, trends, and outliers
  • 2. Calculating mean, median, mode, range, variance, standard deviation
Data Visualization and Communication20%- Creating basic visualizations
  • 1. Using text and simple plotting tools
  • 2. Line charts, bar charts, histograms, pie charts
- Interpreting and presenting results
  • 1. Reporting findings clearly and concisely
  • 2. Deriving conclusions and insights
- Principles of effective data visualization
  • 1. Choosing appropriate chart types
  • 2. Clarity, simplicity, and accuracy

Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions:

1. A financial technology company is reviewing its data practices to ensure legal and ethical compliance.
Which of the following actions would support responsible data handling and align with regulations like GDPR, HIPAA, and CCPA? (Choose two.)

A) Retaining user data permanently unless users explicitly request deletion.
B) Providing users with a clear explanation of how their data is used and obtaining explicit consent.
C) Implementing encryption for both stored and transmitted customer data.
D) Automatically opting users into data sharing unless they manually change settings.
E) Limiting data access only to marketing staff to streamline personalization.


2. You are given a short Python script that uses both arithmetic and assignment operators. The variable x is initialized as 4, then updated using x *= 2 + 3. What will be printed when the final value of x is displayed?

A) 20
B) 14
C) 24
D) 10


3. A government agency collects citizen data for various public services.
What is the primary reason for emphasizing careful data management throughout the entire data lifecycle in this context? Select the best answer.

A) To enable seamless sharing of anonymized citizen information with other governmental bodies for collaborative initiatives.
B) To uphold data quality for effective service delivery, safeguard citizen privacy and prevent misuse, and comply with data protection laws.
C) To guarantee the long-term availability and integrity of crucial citizen datasets for future policy formulation and evaluation.
D) To streamline the initial capture of citizen details and minimize the administrative burden on both staff and the public.


4. You have a list of test scores, where each entry includes a student name and a score. Some students appear more than once. You want to compute the average score for each student and store the results in a dictionary. Here's the partial code block:

Which code correctly replaces the # MISSING CODE comment to calculate the average score for each student? Select the best answer.

A)

B)

C)

D)


5. How do the analysis and visualization stages of the data lifecycle typically work together?

A) Analysis identifies patterns and insights in processed data, which are then communicated through visualizations for informed decision-making.
B) Analysis summarizes archived data, and visualization transforms it into structured tables for storage.
C) Analysis produces charts and graphs, while visualization interprets the data to detect outliers.
D) Analysis retrieves raw inputs from dashboards, while visualization uses that input to clean the data.


Solutions:

Question # 1
Answer: B,C
Question # 2
Answer: A
Question # 3
Answer: B
Question # 4
Answer: B
Question # 5
Answer: A

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