Data Science Core: 16 flashcards
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1. What is the primary difference between data engineering and data analytics?
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Data engineering focuses on the infrastructure and pipelines to collect, store, and process data, while data analytics focuses on analyzing data to derive insights and support decision-making.
2. Define 'reporting' and contrast it with 'analytics'.
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Reporting is the process of presenting data in a structured format (e.g., dashboards, tables) to monitor metrics. Analytics goes further to interpret data, find patterns, and predict future outcomes.
3. List the four types of analytical techniques mentioned in the syllabus.
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Descriptive, Diagnostic, Predictive, and Perspective (Prescriptive).
4. What are the 'traits' of Big Data commonly referred to as?
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The 5 V's: Volume, Velocity, Variety, Veracity, and Value.
5. What is web scraping and how does it relate to social media analytics?
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Web scraping is the automated extraction of data from websites. Social media analytics uses such data (e.g., posts, comments, likes) to analyze trends, sentiment, and user behavior.
6. Name the Python data structures covered in the syllabus.
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Lists, Dictionaries, and Sets.
7. What is the purpose of NumPy in data science?
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NumPy provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays, enabling vectorized operations.
8. What are the two primary data structures in Pandas?
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Series (1-dimensional labeled array) and DataFrame (2-dimensional labeled data structure with columns of potentially different types).
9. Which Python libraries are used for visualization according to the syllabus?
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Matplotlib and Seaborn.
10. What are the four measurement scales (data types) mentioned?
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Nominal, Ordinal, Interval, and Ratio.
11. Define 'population' and 'sample' in statistics.
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A population is the entire group of individuals or items of interest. A sample is a subset of the population that is selected for analysis.
12. What are percentiles, deciles, and quartiles used for?
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They are measures of position that divide a data set into equal parts: percentiles (100 parts), deciles (10 parts), and quartiles (4 parts), helping to understand the distribution of data.
13. What do skewness and kurtosis measure?
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Skewness measures the asymmetry of a distribution, while kurtosis measures the 'tailedness' or peakedness of a distribution.
14. What is the Central Limit Theorem (CLT)?
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The CLT states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the population's distribution, provided the sample size is sufficiently large.
15. What is the difference between a population parameter and a sample statistic?
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A population parameter is a numerical characteristic of the entire population (e.g., population mean μ). A sample statistic is a numerical characteristic of a sample (e.g., sample mean x̄), used to estimate the parameter.
16. What is the Maximum Likelihood Estimation (MLE) method used for?
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MLE is a method of estimating the parameters of a statistical model by maximizing the likelihood function, which measures how well the model explains the observed data.
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