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| Section | Weight | Objectives |
|---|
| Topic 1: Working with Data and Performing Simple Analysis | 25% | - 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. Reading text, CSV, and structured files
- 2. Importing data from external sources
- Exploratory data analysis
- 1. Calculating mean, median, mode, range, variance, standard deviation
- 2. Identifying patterns, trends, and outliers
- Data aggregation and grouping
- 1. Summarizing and grouping datasets
|
| Topic 2: Data Visualization and Communication | 20% | - Creating basic visualizations
- 1. Line charts, bar charts, histograms, pie charts
- 2. Using text and simple plotting tools
- Principles of effective data visualization
- 1. Choosing appropriate chart types
- 2. Clarity, simplicity, and accuracy
- Interpreting and presenting results
- 1. Deriving conclusions and insights
- 2. Reporting findings clearly and concisely
|
| Topic 3: Introduction to Data and Data Analysis Concepts | 22.5% | - Definition and classification of data
- 1. Difference between data, information, and knowledge
- 2. Process of turning raw data into insights
- 3. Role of data in decision-making and business
- 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. Population, sample, variable, observation
- 2. Descriptive vs inferential statistics
|
| Topic 4: Python Basics for Data Analysis | 32.5% | - 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. Basic exception handling
- 2. Conditional statements, loops, iteration
- 3. Defining and calling functions, parameters, return values
- Core Python syntax and data types
- 1. Variables, numbers, strings, booleans
- 2. Lists, tuples, sets, dictionaries
|
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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q27-Q32):
NEW QUESTION # 27
What will be the output of the following code?

- A. 5 10
- B. 10 5
- C. 10 10
- D. 5 5
Answer: D
Explanation:
The function declares the variable as global and assigns it the value 5. When the function is called, it updates the global variable and returns 5, so both the returned value and the global variable printed afterward are 5.
NEW QUESTION # 28
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.split().strip(', ')
- B. fields = line.strip().split(', ')
- C. fields = line.replace(', ', '|').split('|')
- D. fields = line.split(', ').trim()
Answer: B
Explanation:
Stripping removes leading and trailing whitespace from the line, and then splitting on the comma- space delimiter correctly separates the values into a list for further formatting.
NEW QUESTION # 29
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 retrieves raw inputs from dashboards, while visualization uses that input to clean the data.
- C. Analysis produces charts and graphs, while visualization interprets the data to detect outliers.
- D. Analysis summarizes archived data, and visualization transforms it into structured tables for storage.
Answer: A
Explanation:
Analysis involves examining processed data to uncover patterns, trends, and insights, and visualization presents those findings in graphical form so stakeholders can clearly understand and act on the results.
NEW QUESTION # 30
A loop is designed using range(1, 6, 2) to iterate through numbers. The developer wants to know exactly which values will be generated during execution. Which sequence correctly represents the values produced?
- A. 1, 3
- B. 2, 4, 6
- C. 1, 3, 5
- D. 1, 2, 3, 4, 5
Answer: C
Explanation:
The range(start, stop, step) function generates numbers starting from 1, increasing by 2, and stopping before 6. Therefore, the resulting sequence is 1, 3, and 5.
NEW QUESTION # 31
A loop includes a break statement when a condition is met. The programmer wants to understand how break affects loop execution. What happens when break is executed inside the loop?
- A. Loop ends immediately
- B. Loop restarts
- C. Error
- D. Skips one iteration
Answer: A
Explanation:
The break statement immediately terminates the loop, regardless of remaining iterations. Control is transferred to the first statement following the loop block.
NEW QUESTION # 32
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