Data Science Process

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Questions and Answers

What is a common rule of thumb regarding mortgage interest rates and credit scores?

Mortgage interest rates are fixed regardless of credit scores.

Mortgage interest rates reduce with an increase in credit scores. (correct)

There is no relationship between mortgage interest rates and credit scores.

Mortgage interest rates increase with higher credit scores.

What is the primary purpose of splitting a dataset into training and test datasets?

To manipulate the data for better relevance.

To ensure all data is used for training only.

To validate the model's performance on unseen data. (correct)

To simplify data analysis without a testing phase.

Which of the following is NOT a step involved in the model deployment stage?

Product readiness.

Data cleaning. (correct)

Technical integration.

Model response time.

Which technique is classified as descriptive data science rather than predictive?

Clustering. Signup and view all the answers

In the context of data science modeling, what does the training dataset provide?

Data with known attributes and target variables. Signup and view all the answers

What do advanced data science algorithms help extract from massive data assets?

Nontrivial information. Signup and view all the answers

Which of the following statements best describes a model in data science?

A model is an abstract representation of data and relationships. Signup and view all the answers

What is the typical proportion of a known dataset that should be used as the training dataset according to a standard rule of thumb?

Two-thirds. Signup and view all the answers

What is the primary goal of data exploration?

To understand the dataset's structure and assess data quality Signup and view all the answers

Which statistic is typically used to quantify the central tendency of a dataset?

Mean Signup and view all the answers

In data preparation, why is it important to identify and remove correlated attributes?

To improve the performance of certain data science algorithms Signup and view all the answers

What does the term 'outliers' refer to in the context of data preparation?

Data points that differ significantly from other observations Signup and view all the answers

Which method can help visualize attribute distributions and assess predictions in data science?

Histograms Signup and view all the answers

Which of the following measures provides insight into the variability of a dataset?

Standard deviation Signup and view all the answers

What is the purpose of determining a 'typical value' for each attribute during data understanding?

To analyze anomalies and patterns Signup and view all the answers

What type of statistical measure is the mode?

The most frequently occurring value in a dataset Signup and view all the answers

What is the purpose of using the median in a dataset?

To provide a better representation in skewed distributions or with outliers Signup and view all the answers

How can the mode be particularly useful in statistical analysis?

It provides insights into the frequency of categorical data. Signup and view all the answers

In what scenario is the mean most appropriately used?

When values are evenly distributed Signup and view all the answers

What happens to the mean when there are outliers in a data set?

It becomes less reliable as a central measure. Signup and view all the answers

Which of the following describes the correct method to calculate the median in an even-numbered dataset?

Average the two middle values from the sorted data. Signup and view all the answers

When organizing a dataset in a tabular format, what does each row typically represent?

Each individual observation or subject Signup and view all the answers

Which measure of central tendency is most likely to differ in datasets with multiple natural distributions?

Mode Signup and view all the answers

What is the mean for the Mathematics scores given the dataset?

82 Signup and view all the answers

Study Notes

Fundamentals of Data Science

Course: DS302
Instructor: Dr. Nermeen Ghazy
Reference Books:
- Data Science: Concepts and Practice, Vijay Kotu and Bala Deshpande, 2019
- DATA SCIENCE: FOUNDATION & FUNDAMENTALS, B. S. V. Vatika, L. C. Dabra, Gwalior, 2023

Lecture 3

Chapter 2 - Data Science Process

Modeling

Model: Abstract representation of data and relationships within a dataset.
Simple Rule of Thumb: Example: mortgage interest rate reduces with increasing credit score. This is directional, not fully quantitative.
Modeling steps in predictive data science:
- Training data
- Build model
- Test data
- Evaluation
- Final model
Association analysis and clustering: Descriptive data science techniques.
No target variable to predict, hence no test dataset.
Both predictive and descriptive models have an evaluation step.
Splitting training and test data sets: Modeling step creates a representative model. The dataset used to create the model is called the training dataset.
Validity check: The created model must be validated with another known dataset (test dataset or validation dataset).
Standard rule: Two-thirds of the overall dataset can be used for training, and the remaining one-third for the test dataset.

Application

Deployment: Stage where models become production ready.
Model Deployment Stage Deals With:
- Product readiness
- Technical integration
- Model response time
- Remodeling
- Assimilation

Knowledge

Framework for extracting non-trivial information from data.
Advanced approaches, like data science algorithms, are needed to extract knowledge.
Data science process begins with prior knowledge and ends with posterior knowledge (incremental insights).
Spurious irrelevant patterns can arise during the data science process.

Chapter 3 - Data Exploration

Objectives: Understanding dataset structure, identifying patterns, assessing data quality before in-depth analysis.
Process involves:
- Data understanding
- Data preparation
- Data science tasks
- Interpreting the results

1- Data Understanding

Broad overview of each attribute/variable in the dataset.
Interactions between attributes, typical values, data point variations, extreme values.

2- Data Preparation

Anomalies addressed before applying data science algorithms (outliers, missing values, highly correlated attributes.)
Identifying and removing correlated attributes as certain algorithms perform poorly with them.

3- Data Science Tasks

Basic exploration can replace the entire data science process.
Scatterplots reveal clusters in low-dimensional data.
Simple, visually-based rules can assist predictive models.

4- Interpreting the Results

Data exploration aids in understanding prediction, classification, and clustering outcomes.
Histograms are tools for data visualization, assessing numeric predictions, error rate estimations, and more.

Datasets

Iris dataset: Popular in learning data science.
150 observations from three species of Iris.
Iris Setosa, Virginica, Versicolor. (50 observations each).
Four attributes (sepal length, sepal width, petal length, petal width).
Iris dataset readily available in standard data science tools and from the UCI Machine Learning dataset repository.
All four aspects of the Iris dataset are continuous numeric values measured in centimeters.
Iris setosa classification is simple (petal length < 2.5 cm)
Dataset is widely used in data science education for its simplicity, clarity, and representing a common problem that common data science algorithms approach
Used to represent and illustrate straightforward classification

Types of Data

Data formats and types vary.
Data properties determine which operations can be applied (numeric values, ordered labels, threshold-based data).

Descriptive Statistics

Overall dataset summaries for understanding its characteristics (e.g., average age, median rental price).
Analyzing key characteristics: central tendency (mean/median), spread (range/variance), overall distributions.
Two main types:
- 1-Univariate Explorations: Focuses on a single attribute to summarize its characteristics.
- 2-Multivariate Exploration: Exploring relationship and interactions between multiple attributes.

Descriptive Statistics - Univariate

Describes characteristics of single attributes.

Measures of Central Tendency

Mean: Arithmetic average.
Median: Middle value in a sorted dataset, less sensitive to outliers.
Mode: Most frequent value.

Measures of Spread

Range: Difference between max and min values (simple but sensitive to outliers).
Deviation, Variance and Standard Deviation: Measures spread encompassing all values, less sensitive to outliers and considering distributions.

Example A (Data Exploration Exercise)

Example dataset with student exam scores (Mathematics, Science, English, History).
Steps for analysis:
- Organizing data in tabular format
- Calculating measures of central tendency (mean for each subject).
- Calculating measures of spread (range, standard deviation).

Multivariate Exploration

Examining relationships and interactions among multiple attributes within the dataset.
Data points as coordinate points in multi-dimensional space.
Correlation: Statistically measures relationships between attributes.
Correlation Coefficient (r): Measures the strength of linear dependence (-1 < r< 1)

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Description

Explore the fundamental concepts of data science modeling in Lecture 3 of DS302. This quiz covers the steps in predictive data science, including training, evaluation, and the techniques of association analysis and clustering. Test your knowledge on how these methodologies contribute to building effective models.

Data Science Process - Lecture 3