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Python Institute PCED-30-02 Exam Syllabus Topics:

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

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

1. Given the following string:
text = "Report2024Final"
Which of the following statements are entirely correct? (Choose two.)

A) text.capitalize()changes Report2024Finalto REPORT2024FINAL, and text.find("2024")returns 6
B) text[-6].isdigit() returns True, text.startswith("r") returns True
C) text.capitalize()changes Finalto lowercase, and text.find ("L")returns True
D) text[6:8].isdigit()returns True, and text.endswith("Final")returns True
E) text.isalpha()returns True, and text.find ("2024")returns True
F) text[0:6].isalpha() returns True, and text.find("2024") returns 6


2. You are reading a data.csvfile line by line. To prepare each line for formatting with f-strings, you need to remove extra whitespace and split the values by commas.
Which line should you insert to correctly clean and parse the input?

A) fields = line.strip().split(', ')
B) fields = line.split().strip(', ')
C) fields = line.replace(', ', '|').split('|')
D) fields = line.split(', ').trim()


3. 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)


4. Which task is traditionally or typically performed by a data analyst, and not by a data scientist or analytics specialist? Select the best answer.

A) Designing a data pipeline to ingest and process real-time sensor data.
B) Developing machine learning algorithms to predict user behavior.
C) Recommending pricing strategies using predictive modeling and A/B testing.
D) Creating summary tables and charts to describe patterns in historical data.


5. You are reading a data.csvfile line by line. To prepare each line for formatting with f-strings, you need to remove extra whitespace and split the values by commas.
Which line should you insert to correctly clean and parse the input?

A) fields = line.strip().split(', ')
B) fields = line.split().strip(', ')
C) fields = line.replace(', ', '|').split('|')
D) fields = line.split(', ').trim()


Solutions:

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

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