Data Science Research Methodology

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Questions and Answers

What is the main difference between a question-driven and data-driven approach?

The way data is collected and analyzed

The order in which the hypothesis and experiment are designed

The role of the researcher in the experiment

The approach to formulating a research question (correct)

What is the correct order of the steps to set up and execute an experiment within data science?

Data collection, task definition, data exploration, pre-processing, model learning, evaluation

Task definition, data collection, data exploration, pre-processing, model learning, evaluation (correct)

Data exploration, task definition, data collection, pre-processing, model learning, evaluation

Task definition, data exploration, data collection, pre-processing, model learning, evaluation

What is an example of a challenge of working with data?

Lack of expertise

Lack of computational power

Insufficient funding

Noisy data (correct)

What does a clear definition of a task based on a given data set and general problem description consist of?

Research question, whether the data is supervised or unsupervised, and data type (A) Signup and view all the answers

What is the equation for simple linear regression?

Y = βX + b (D) Signup and view all the answers

What is the purpose of normalization/standardization in multiple linear regression?

To compare different ranges (B) Signup and view all the answers

What is the role of cross-entropy loss in logistic regression?

To measure the difference between predicted and actual probabilities (B) Signup and view all the answers

What is the purpose of the sigmoid function in logistic regression?

To transform the output to a probability distribution (A) Signup and view all the answers

What is the purpose of the sigmoid function in the given context?

To transform the output into a probability (A) Signup and view all the answers

What type of data would an image be classified as?

Unstructured data (D) Signup and view all the answers

What is the purpose of gradient descent in the given context?

To minimize the loss function (C) Signup and view all the answers

What is the advantage of reporting the median instead of the mean when the data is skewed?

It is more informative (C) Signup and view all the answers

What type of data would a database with rows and columns be classified as?

Structured data (B) Signup and view all the answers

What is data exploration often a precursor to?

Machine learning (D) Signup and view all the answers

Study Notes

Data Science Research Approaches

Question-driven approach: formulate hypothesis, design experiment, collect data, analyze data, accept or reject hypothesis
Data-driven approach: explore data, formulate research question, structure and annotate data, develop and apply learning techniques, evaluate on data, answer research question

Steps in Data Science Research

Formulate a clear research question and task definition
Collect data
Explore and preprocess data
Develop and apply learning techniques
Evaluate model performance
Answer research question

Challenges of Working with Data

Noisy data
Large data
Small data
Incomplete data
Different sampling rates
Different formats
Wrongly chosen or irrelevant variables
Large or unknown number of classes
Class imbalance
Heterogeneous data or features
New domain

Defining a Task in Data Science

Clearly define the research question
Specify whether the task is supervised or unsupervised
Determine the type of task: classification, regression, ranking, etc.
Identify the data and its characteristics
Identify the labels or targets and their characteristics

Simple and Multiple Linear Regression

Simple linear regression: Y = βX + b, where Y is the dependent variable, X is the independent variable, β is the slope, and b is the intercept
Multiple linear regression: y = β⋅X + b, requires normalization or standardization of values before training

Logistic Regression

A discriminative model that learns to distinguish between two classes
Learns from a training set to minimize the loss function L(y ̂,y)
Uses gradient descent to optimize the parameters w and b
Uses the sigmoid function to transform output to a probability: σ(z) = 1/(1+e^(-z) )

Data Types and Exploration

Structured data: a database with rows, columns, and a relational key
Semi-structured data: data with additional information, such as references to images
Unstructured data: unorganized data, such as images, text, or time series data
Use median instead of mean when data is skewed or has outliers
Boxplots are more useful for outlier detection than histograms

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Description

Learn about the question-driven and data-driven approaches in data science research, including the steps involved in each methodology. Understand how to set up and execute an experiment within data science.