Data Analysis Process Quiz

<p>Defining the project's business objectives. (B)</p> Signup and view all the answers

<p>To get familiar with the data and evaluate its quality. (A)</p> Signup and view all the answers

<p>Problem Understanding (D)</p> Signup and view all the answers

<p>Normalizing the data. (C)</p> Signup and view all the answers

<p>Problem Understanding -&gt; Data Understanding -&gt; Data Preparation -&gt; Modeling. (C)</p> Signup and view all the answers

<p>Longer model training times, decreased accuracy, increased error variance, and decreased normality. (A)</p> Signup and view all the answers

<p>Min-Max Normalization (D)</p> Signup and view all the answers

<p>To enhance the efficiency of the machine learning process. (A)</p> Signup and view all the answers

<p>Selecting and applying various modeling techniques and calibrating their parameters. (B)</p> Signup and view all the answers

<p>Mean of 0 and standard deviation of 1. (A)</p> Signup and view all the answers

<p>[0, 1] (C)</p> Signup and view all the answers

<p>Determining if the model meets assumptions and if all objectives were accounted for. (C)</p> Signup and view all the answers

<p>Organizing the knowledge obtained and presenting it for the customer. (C)</p> Signup and view all the answers

<p>Poor classifier (C)</p> Signup and view all the answers

<p>Algorithms that can find patterns or create predictions from not yet seen data. (A)</p> Signup and view all the answers

<p>It is known for its readability, simplicity, and rich libraries for machine learning. (C)</p> Signup and view all the answers

<p>They provide prebuilt functions which help to reduce the amount of code you have to write. (C)</p> Signup and view all the answers

<p>FP / (TN + FP) (C)</p> Signup and view all the answers

<p>Evaluation (A)</p> Signup and view all the answers

<p>The knowledge gained needs to be presented so the customer can use it. (C)</p> Signup and view all the answers

<p>Python has prebuilt mathematical and machine learning functions in different libraries. (A)</p> Signup and view all the answers

<p>Data Preparation (A)</p> Signup and view all the answers

<p>To improve the algorithm's learning by ensuring data quality and information content. (D)</p> Signup and view all the answers

<p>datum (A)</p> Signup and view all the answers

<p>A quantity resulting directly from the terms of a given problem. (B)</p> Signup and view all the answers

<p>The number of observations (records). (B)</p> Signup and view all the answers

<p>Target variables. (A)</p> Signup and view all the answers

<p>A set of attributes with a fixed tuple length. (C)</p> Signup and view all the answers

<p>They are also known as descriptive variables. (A)</p> Signup and view all the answers

<p>To understand the data, quantify results, and measure application performance. (A)</p> Signup and view all the answers

<p>The frequency of each unique value in a dataset. (D)</p> Signup and view all the answers

<p>Standard Deviation. (D)</p> Signup and view all the answers

<p>By summing all the values and dividing by the total count of values. (B)</p> Signup and view all the answers

<p>The value separating the lower and upper halves of the dataset. (B)</p> Signup and view all the answers

<p>To measure the variability or scattering of values across a dataset. (A)</p> Signup and view all the answers

<p>The mean. (B)</p> Signup and view all the answers

<p>By reporting mean, median and a measure of spread for a balanced view of the data. (C)</p> Signup and view all the answers

<p>Based on the likelihood of the data under the Gaussian distribution. (B)</p> Signup and view all the answers

<p>To identify directions of maximum variance in the data. (B)</p> Signup and view all the answers

<p>Gaussian kernel (A)</p> Signup and view all the answers

<p>It approaches a normal distribution. (D)</p> Signup and view all the answers

<p>It gets closer to the population mean. (C)</p> Signup and view all the answers

<p>To infer information about the population using a smaller subset. (D)</p> Signup and view all the answers

<p>Data Visualization. (A)</p> Signup and view all the answers

<p>It reduces. (D)</p> Signup and view all the answers

The first step in the data analysis process, where you clearly define the project goals and translate them into a data-driven problem statement.

This phase involves gaining a deep understanding of the data, answering crucial questions about its origin, collection methods, and meaning.

Involves preparing your raw data for analysis by cleaning, transforming, and combining datasets to build a cohesive and usable dataset.

This stage applies statistical and machine learning techniques to analyze the curated data and build models that meet the defined objectives.

This stage involves evaluating the performance of the developed models to assess their accuracy, reliability, and potential effectiveness in solving the problem.

This final step involves putting the best performing model into practice, integrating it into systems or processes to achieve the desired outcome.

In this stage, different modeling techniques are selected, configured, and optimized.

This phase focuses on evaluating if the created model meets the initial goals and objectives. It also checks for any missing aspects or business needs.

Even if the model's purpose is to learn from data, the knowledge gained needs to be organized and presented in a way that makes sense to the user.

Python is known for its clear and simple code, making it easy to learn for beginners and efficient for experts.

Python offers various libraries and frameworks (like Scikit-learn, TensorFlow, PyTorch, Keras, and Pandas) that offer ready-made tools for data analysis and machine learning tasks.

These libraries in Python offer built-in functions and utilities for math operations, data manipulation, and machine learning, reducing the need to write code from scratch.

The process of data analysis involves collecting, cleaning, transforming, and analyzing data to extract meaningful insights and support decision-making.

Data analysis aims to gain insights and knowledge from data to solve problems, improve processes, and make informed decisions.

The central tendency measure that represents the average of a dataset calculated by summing all values and dividing by the total count.

The central tendency measure that represents the middle value in a sorted dataset, separating the lower half from the upper half.

Measures that indicate the spread or variability of data points within a dataset.

A visual representation of how frequently values appear within a dataset. It shows the distribution of data points.

A field in statistics that helps us understand and quantify data, and is essential for evaluating the performance of machine learning models.

A type of statistical measure that helps determine the central location of data points within a distribution.

The practice of preparing data for analysis by addressing issues such as inconsistencies, missing values, and data format discrepancies.

In machine learning, data is often represented as a collection of records. Each record represents a single observation or sample, described by a set of attributes or variables.

In machine learning, each record, also known as an observation or sample, is described by a specific set of attributes or variables. Each record represents a unique instance within the dataset.

In machine learning, attributes are the characteristics or variables that describe each record in the dataset. They represent the features or properties of the data.

Input variables, also known as independent variables, are the features used to predict the target variable. They are the factors that influence the outcome.

Output variables, also known as dependent variables, are the target or label you are trying to predict. They are the outcomes you want to understand.

Transaction-based datasets often involve a series of events or actions, and each transaction is represented as a vector. The vectors usually describe properties of each transaction.

The CRISP-DM (Cross Industry Process for Data Mining) is a structured framework for conducting data mining projects. It outlines a six-phase process for effective data analysis.

The quality and information content of a dataset are crucial for machine learning algorithms to learn effectively. Preprocessing data before using it in machine learning is vital.

Anomalies are identified by examining how likely the data is to occur under a normal distribution.

A method used to reduce the number of dimensions in a dataset by transforming it into a smaller set of variables called principal components. This helps to find the directions of maximum variance in the data.

A technique used in machine learning algorithms where data is mapped into a higher-dimensional space using a kernel function. This is often used to improve the accuracy of models.

The central limit theorem states that the distribution of sample means will approach a normal distribution as the sample size increases. This is a fundamental principle for hypothesis testing.

It's a subset of data that represents the characteristics of the entire population. It's used for analysis when collecting data for the whole population is impossible.

The process of collecting data for research or analysis.

The process of creating visualizations to present data in a clear and understandable way.

Statistical tests and confidence intervals rely on the assumption that the population data follows a normal distribution.

Outliers are data points that are significantly different from the rest of the data. They can cause problems when it comes to building predictive models.

Outliers can affect the accuracy and efficiency of predictive models. This can lead to longer training times, inaccurate predictions, and higher error variance.

A box plot is a visual representation of data that shows the distribution of the data and identifies outliers. It uses quartiles to divide the data into groups, with the outliers displayed as individual points.

The Z-score is a statistical measure that indicates how many standard deviations a data point is from the mean. Outliers often have a high Z-score, indicating they are far from the mean.

Data transformation involves changing the structure or characteristics of your data to improve its suitability for analysis. This can include scaling data or using techniques like standardization.

Z-score standardization transforms data to have a mean of 0 and a standard deviation of 1. This makes features comparable and can improve model performance.

Min-Max normalization changes data values to a range between 0 and 1. This is another way to scale features and make them comparable.

AUC (Area Under the Curve) measures the overall performance of a classification model. An AUC of 0.5 means no discrimination; a score of 0.7 and above suggests good performance.