**Assignment Context for a Data Scientist:**A data scientist for an online retailer is given the assignment of predicting what a customer will be interested in based on past purchases. The data scientist is given access to six months of purchasing history from 1000 customers.**Question:**What task does the data scientist do next?1. Give a presentation of the results to the targeted advertising team.2. Run classification models to predict which product characteristics are likely to appeal to each customer.3. Create scatter plots of each pair of variables to determine which are likely to be correlated.4. Reformat the data into a CSV file and remove rows with missing data.**Additional Question:**The task is an example of which step in the data science lifecycle?**Response Options:**- A dropdown menu allows the selection of a lifecycle step. A data scientist for an online retailer is given the assignment of predicting what a customer will be interested in based on past purchases. The data scientist is given access to six months of purchasing history from 1000 customers.What task does the data scientist do next?- [ ] Give a presentation of the results to the targeted advertising team.- [ ] Run classification models to predict which product characteristics are likely to appeal to each customer.- [ ] Calculate statistics for each pair of variables to determine which are likely to be correlated.- [ ] Open up a csv file and remove rows with missing data.This task represents which step in the data science lifecycle?- Step 1: Gathering the data- Step 2: Cleaning the data- Step 3: Exploring the data- Step 4: Modeling the data- Step 5: Interpreting data

The data science lifecycle is a systematic process that data scientists follow to extract valuable insights from raw data. It involves stages such as problem definition, data collection, cleaning, exploratory analysis, feature engineering, modeling, evaluation, deployment, monitoring, and communication of results. The lifecycle is iterative, allowing for revisiting stages based on new insights or changes. Flexibility and adaptability are crucial, as data scientists navigate through the lifecycle to drive informed decision-making.The correct option is:Run classification models to predict which product characteristics are likely to appeal to each customer.Explanation:Run Classification Models:This is the most suitable option. Given past purchases, the data scientist would likely want to build predictive models (such as classification models) to understand patterns and predict which product characteristics are likely to appeal to each customer. Classification models can be trained on historical purchase data to identify patterns and preferences.Incorrect options:1. Presentation of Results:Not the next step. The data scientist usually doesn't jump directly into presenting results without analyzing and deriving insights from the data first.2. Create Scatter Plots:While scatter plots are useful for visualizing relationships between variables, they don't necessarily address the specific task mentioned. The task is about predicting product characteristics that are likely to appeal to customers, which requires more advanced analysis than scatter plots.3. Reformat Data into a CSV File:Data formatting and cleaning are important, but this step usually comes after the initial analysis. Moreover, the task mentioned is more about deriving insights and predictions from the data, not just preparing the data for storage or analysis.The task "Run classification models to predict which product characteristics are likely to appeal to each customer" is an example of the Modeling the data step in the data science lifecycle.Conclusion:The correct option involves running classification models to predict customer preferences based on past purchases. This aligns with the goal of leveraging data science techniques to gain insights and make predictions, which is a key step in the data science lifecycle. The other options may be relevant at different stages of the process but are not the immediate next step based on the provided scenario.The task of running classification models to predict customer preferences aligns with the Modeling step in the data science lifecycle, where predictive models are developed to extract insights and make predictions based on the available data.

Engineering

Computer Science

A data scientist for an online retailer is given the assignment of predicting what a customer will be interested in based on past purchases. The data scientist is given access to six months of purchasing history from 1000 customers. What task does the data scientist do next? Give a presentation of the results to the targeted advertising team. Run classification models to predict which product characteristics are likely to appeal to each customer. Create scatter plots of each pair of variables to determine which are likely to be correlated. Reformat the data into a csv file and remove rows with missing data. The task is an example of which step in the data science lifecycle? Pick î

A data scientist for an online retailer is given the assignment of predicting what a customer will be interested in based on past purchases. The data scientist is given access to six months of purchasing history from 1000 customers. What task does the data scientist do next? Give a presentation of the results to the targeted advertising team. Run classification models to predict which product characteristics are likely to appeal to each customer. Create scatter plots of each pair of variables to determine which are likely to be correlated. Reformat the data into a csv file and remove rows with missing data. The task is an example of which step in the data science lifecycle? Pick î

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

See similar textbooks

Related questions

Q: Create a comprehensive guide on how to construct a model using Plaxis, taking into account the…

A: Powerful geotechnical engineering software called Plaxis is utilized in a variety of geotechnical…

Q: A large car fleet company asked you to help them forecast vehicle resale values. They purchase new…

A: The above question is solved in step 2 :-

Q: Predictive modeling: Task: Ambulance Demand Data Generating Process: Dates of Coverage {Please…

A: Predictive modeling is a statistical technique used to forecast future events based on historical…

Q: What is the predicted modeling method and why was it chosen for any particular model?

A: What is the expected modelling method and why should it be used for every model?

Q: You can only assess a model based on its performance on test data. describe in detail, elaborate on?…

A: Introduction: Not only do testers gather and manage data from existing sources, but they also…

Q: The variable's "type" is not enough to describe it. Each variable has unique data and…

A: What is variable has unique data and characteristics: A variable having unique data and…

Q: Assume you are giving a dataset that is based on user surveys. You notice that many surveys have…

A: Data cleaning sometimes also known as "Data Scrubbing" or "Data cleansing" is important part for…

Q: PK userid firstName lastName UQ username phone UQ emailld User password emailVerified createdOn PK…

A: Normalization A database design method called normalization minimizes data duplication and gets rid…

Q: python to solve this. Task: Handling missing data (Replacing missing data with the mean value or…

A: It is defined as an interpreted, object-oriented, high-level programming language with dynamic…

Q: Which metric provides an overall measure of how well a classification model is performing,…

A: A classification model is a type of machine learning model designed to categorize input data into…

Q: Suppose, as manager of a chain of stores, you would like to use sales transactional data to analyze…

A: Today discovery of the new items comes with huge profit deals when it comes from the high utility…

Q: The hyper-parameters of a model must NOT be tuned on the test data ( i.e, the data used to evaluate…

A: hyper-parameters A hyperparameter is machine learning is a parameter whose quantity is utilised to…

Q: mple size and training/testi

A: Solution - In the given question, we have to explain sample size and training/testing a model.

Q: Why is it useful to leave certain variables out of the model altogether?

A: This query explains the rationale for leaving out a data point from a data model. A data model (or…

Q: What is the best way to decide how many epochs of training to perform? It is always obvious looking…

A: Epoch meaning:- An epoch is a term used in machine learning and indicates the number of passes of…

Q: explain me step by step for this data sceince question please Given the dataset below and a…

A: Given the dataset below and a support threshold of 3 and confidence of 100%, +generate all the…

Q: Why did you choose each prediction model and a brief description of how it works.

A: what is predictive model? So it is basically a statistical technique which uses machine learning…

Q: please solve this data science questions step by step please. 2. Given the dataset below and a…

A: The code for the association rule mining is given below: import pandas as pdimport numpy as npfrom…

Q: Discuss the benefits of using a data model in the context of the standard method for making strength…

A: A data model is a representation of the structure and relationships between different types of data.…

Q: To begin with, why is it necessary to divide the data into training and validation sets? What is the…

A: In general the Data set refers to the collections of the data which are related, which are discrete…

Concept explainers

Enhanced ER Model

The enhanced ER model is an abbreviation for Enhanced Entity-Relationship (EER) model. As you know, the entity-relationship (ER) model is a pictorial illustration of the relationship between various entities of a domain. In the case of a large amount of data with multiple interrelated entities, the ER diagrams become complex and tough to interpret. For such cases, the need for enhancement in the ER model was observed, and thus, the EER model was designed.

Question

**Assignment Context for a Data Scientist:**

A data scientist for an online retailer is given the assignment of predicting what a customer will be interested in based on past purchases. The data scientist is given access to six months of purchasing history from 1000 customers.

**Question:**
What task does the data scientist do next?

1. Give a presentation of the results to the targeted advertising team.
2. Run classification models to predict which product characteristics are likely to appeal to each customer.
3. Create scatter plots of each pair of variables to determine which are likely to be correlated.
4. Reformat the data into a CSV file and remove rows with missing data.

**Additional Question:**
The task is an example of which step in the data science lifecycle?

**Response Options:**
- A dropdown menu allows the selection of a lifecycle step.

A data scientist for an online retailer is given the assignment of predicting what a customer will be interested in based on past purchases. The data scientist is given access to six months of purchasing history from 1000 customers.

What task does the data scientist do next?

- [ ] Give a presentation of the results to the targeted advertising team.
- [ ] Run classification models to predict which product characteristics are likely to appeal to each customer.
- [ ] Calculate statistics for each pair of variables to determine which are likely to be correlated.
- [ ] Open up a csv file and remove rows with missing data.

This task represents which step in the data science lifecycle?

- Step 1: Gathering the data
- Step 2: Cleaning the data
- Step 3: Exploring the data
- Step 4: Modeling the data
- Step 5: Interpreting data

Expert Solution

This question has been solved!

Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.

This is a popular solution!

SEE SOLUTION Check out a sample Q&A here

Step 1: Define data science lifecycle

VIEW

Step 2: Explain the correct and incorrect options

VIEW

Solution

VIEW

Trending now

This is a popular solution!

Step by step

Solved in 3 steps

SEE SOLUTION Check out a sample Q&A here

Knowledge Booster

Learn more about

Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, computer-science and related others by exploring similar questions and additional content below.

Similar questions

What components go into constructing a descriptive model?
Which criteria were used for evaluating the various categorization and prediction methods?
The only way to tell if a model is right or wrong is to see how well it works on test data. tell me more; give me more information? Explain?
Assume you are giving a dataset that is based on user surveys. You notice that many surveys have missing fields. You also notice that some surveys have fields on a scale of 1 to 5 while others are on a scale of 1 to 10. The survey has mixed information using metric measurements and the imperial system. There are some different fields depending on the country, but they all relate to the location of the participant. You also notice that some values just don't make sense. What analysis would you do on the data set to come up with a data cleaning plan? What are some normalization and cleaning tasks you could do based on the information provided in this description? Edit View Insert Format Tools Table 12pt v Paragraph BIU Αν 2 T² : PAYTO THE MARCELLA M. SERRANO PEDRO GOMEZ 3610 COUNTRY TRAILS PH. 619 S BONITA, CA 91902 91902 Ale 2512 12759 Eraser ser made in ape g GRIP 12 31 Super Polymer Lead Pentel
Only by seeing how well a model performs on test data can its accuracy be determined. elaborate on; provide additional information about Explain?
BDAN 250 Select the answer that best describes an ordinal variable: A categorical variable with categories that cannot be ranked A categorical variable where there is a logical rank-order relationship between the variable responses A continuous variable with categories that cannot be ranked A continuous variable where there is a logical rank-order relationship between the variable responses Numeric values at set/even intervals.
Why do we use z-scores to evaluate a classifier? Why do we use feature weighting?
ER modeling is one approach in creating a data model. There are several approaches in creating a data model (bottom up, top down, inside out, and mix). 1. explain each of these approaches!2. Then explain when we use top down, bottom up, inside out and mix? what is the function of each of the approaches?
Only by seeing how well a model performs on test data can its accuracy be determined. elaborate on; provide additional information about Explain?
5 of 5 You are evaluating a classification model and notice that the error on the test set is significantly greater than on the training set. You determine that the model is overfitting the training data. What could you do to prevent overfitting? Reduce the number of features to model Increase the train/test split ratio Decrease the train/test split ratio Use a different error function NEXT
Do an analysis of a real data set and also mention where did you find the data set. Please do analysis completely in R studio. A basic requirement for the data set is that it includes one response variable and at least two predictor variables.The main objectives of this question are• to identify a suitable data set,• to come up with meaningful research questions based on the data,• to experience some of the problems encountered when analyzing real data,Also mention:• Where I find the data set?• Why the problem is of interest?• Which method or model is appropriate to this problem?• How do I apply the method to analysis the data set?• What is my conclusion?
A variable cannot be sufficiently described by the word "type." Or to put it another way, every variable has some kind of attributes and data types. What is the best approach to describe any variable's characteristics using the notion that makes this possible?