data visualization with python peer graded assignment coursera

If you only want to read and view the course content, you can audit the course for free. & For Mobile User, You Just Need To Click On Three dots In Your Browser & You Will Get AFindOption There. GitHub Instantly share code, notes, and snippets. You will be able to effectively utilize tools and packages that are widely available to amplify your effectiveness and write useful programs. More questions? Here, you will find Data Analysis With Python Exam Answers in Bold Color which are given below. You can observe and tell the story of your data in a more impactful way through visualization. Question 11: What do the letters in the box plot above represent? These answers are updated recently and are 100% correct answers of all week, assessment and final exam answers of Data Analysis With Python from Coursera Free Certification Course. We will learn how to install external packages for use within Python, acquire data from sources on the Web, and then we will clean, process, analyze, and visualize that data. Summary information on; Question: Assignment #3: Data Analysis and Visualization In this assignment, you will design a visualization for a dataset. Note: The pre-requisite for this course is basic R programming skills. By signing up, you unlock access to our entire library of courses. If you do not have these skills, it is highly recommended that you first take the Introduction to R Programming for Data Science as well as the Data Analysis with R courses from IBM prior to starting this course. You will also use multiple data visualisation libraries in Python like Folium, Seaborn and Matpotlib. Developed by Rice University's world-class Computer Science & Data Science faculty, this specialization is intended for beginners who would like to master essential programming skills. Completing this course will count towards your learning in any of the following programs: Table of Contents. Question 14: A bubble plot is a variation of the scatter plot that displays three dimensions of data. Question 1: Area plots are stacked by default. Use These Option to Get Any Random Questions Answer. This course will combine the skills learned throughout the specialization to enable you to write interesting, practical, and useful programs. You can either opt to audit the course to start a seven-day free trial or pay the certification fee. We have detected that you are using extensions to block ads. via When will I have access to the lectures and assignments? Salesforce Sales Development Representative, Preparing for Google Cloud Certification: Cloud Architect, Preparing for Google Cloud Certification: Cloud Data Engineer. Question 1: Data visualizations are used to (check all that apply). Data visualization with python peer graded assignment us domestic airline flights performance. Question 4: Using the notebook backend, you can modify a figure after it is rendered. Question 12: What is the correct combination of function and parameter to create a box plot in Matplotlib? Thank you! Learnt many new things about programming in python, It has been a while since I programmed. Question 1: What are the layers that make up the Matplotlib architecture? The final project of the specialization will enable you to demonstrate mastery of the concepts you have learned up to this point. Coursera offers many courses in many fields. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. I appreciate you for sharing! Access to lectures and assignments depends on your type of enrollment. everything you need to complete. Question 2: In Python, creating a waffle chart is straightforward since we can easily create one using the scripting layer of Matplotlib. GitHub - NakulLakhotia/Data-Visualization-with-Python: This repository contains all the jupyter notebooks and the final peer graded assignment for the course - "Data Visualization with Python" offered by Coursera NakulLakhotia / Data-Visualization-with-Python Public Notifications Fork 5 Star 1 Code Issues Pull requests Actions Projects Insights Data without a way to convey the story behind it to yourself or others is just numbers on a page. Enrol for a Program in Data Science, 25+ Tips for Improving Your Graphic Design Skills, Top Universities in India Offering Cyber Security Courses, How to Make a Career in the Field of Artificial Intelligence, Top 10 Benefits Of Holding A Certification In Business Intelligence, Which are the best certification courses for Photography in India, Want to Pursue a Career in Blockchain Technology? Supply Chain Logistics Coursera Peer Graded Assignment Solutions. chevron_left list_alt. Cell link copied. This knowledge will provide a solid foundation towards a career in data science, software engineering, or other disciplines involving programming. Data Science Survey Data - Barplot. Data. License. No. Clone with Git or checkout with SVN using the repositorys web address. NOTE: This course requires knowledge of working with R and data. Second Language Listening, Speaking, and Pronunciation Coursera Quiz Answers 2022 [% Correct Answer], Teach English Now! Please support us by disabling these ads blocker. Question 19: You cluster markers superimposed onto a map in Folium using a marker cluster object. perform data analytics and build predictive models. 1 input and 1 output. These email lessons are designed to be short, but are packed with expert tips, insights, and simple exercises to reinforce your learning. 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\n". Data Analysis with Python | Week- 5 & 6 All Quiz Answers With Peer - Graded Assignments | Coursera 4,375 views Jul 30, 2021 Coursera - Data Analysis with Python | Week- 5 & 6 All Quiz. Figure Layer, Artist Layer, and Scripting Layer, count, bin_edges = np.histogram(series_data), series_data.plot(kind=hist, xticks = count, bin_edges), series_data.plot(kind=hist, xticks = count), series_data.plot(type=hist, xticks = bin_edges), question.plot(kind=bar, orientation=horizontal), question.plot(kind=bar, type=horizontal), A = Mean, B = Upper Mean Quartile, C = Lower Mean Quartile, D = Inter Quartile Range, E = Minimum, and F = Outliers, A = Mean, B = Third Quartile, C = First Quartile, D = Inter Quartile Range, E = Minimum, and F = Outliers, A = Median, B = Third Quartile, C = Mean, D = Inter Quartile Range, E = Lower Quartile, and F = Outliers, A = Mean, B = Third Quartile, C = First Quartile, D = Inter Quartile Range, E = Minimum, and F = Maximum, Function = box, and Parameter = type, with value = plot, Function = boxplot, and Parameter = type, with value = plot, Function = plot, and Parameter = type, with value = box, Function = plot, and Parameter = kind, with value = boxplot, ax = sns.regplot(x=year, y=total, data=data_dataframe, color=green), data_dataframe.plot(kind=regression, color=green, marker=+), data_dataframe.plot(kind=regplot, color=green, marker=+), ax = sns.regplot(x=total, y=year, data=data_dataframe, color=green). IBM Data Visualization Python_Final Assignment. He always kept talking about this. Finally, you will be introduced to creating interactive dashboards using the R Shiny package. How Entrepreneurs Can Use Machine Learning to Make their Business Successful? The course may offer 'Full Course, No Certificate' instead. On lines 6 to 9, you read the data and preprocess it for use in the dashboard. Cell link copied. Question 16: Which of the choices below will create the following regression line plot, given a pandas dataframe, data_dataframe? Backend Layer, FigureCanvas Layer, Renderer Layer, Artist Layer, and Scripting Layer. The Scope of Artificial Intelligence in India. Data. This option lets you see all course materials, submit required assessments, and get a final grade. Final Assignment - Data Visualization with Python . Udacity, Google Cloud Data-Visualization-with-Python/Week 5 - Final Assignment.py Go to file Cannot retrieve contributors at this time 218 lines (180 sloc) 11.8 KB Raw Blame # Import required libraries import pandas as pd import dash import dash_html_components as html import dash_core_components as dcc from dash. You can apply for it by selecting the financial aid available option on the course page and fill in the required details in the online application form. Data Set: Flight Delays The U.S.Department of Transportation tracks the performance of domestic flights operated by air carriers. Thank you for sharing your knowledge. You have entered an incorrect email address! TheData Visualization with Python courseis ideal fordata scientists who want to improve their skills. 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The Data Visualization with Python course aims to teach you how to present data results from a pile of huge data sets and present them in a way that makes it simple to understand. Learn various tools of data visualisation and tell a compelling story with Courseras Data Visualization with Python training by IBM. You will also be able to understand and compare different approaches to reconciling two data sets. jaredbest / data-visualization-with-python Public Notifications Star master 1 branch 0 tags Code 8 commits Failed to load latest commit information. Choose all that apply. However, you'll have to pay for the same. This module will teach you about packages and modules in Python, including how to install packages and how to create your own modules. I learned a lot in this course, especially regarding ggplot and shiny. SHOW ALL This course is part of multiple programs This course can be applied to multiple Specializations or Professional Certificates programs. We have a rapidly growing library of fun, informative, expert-backed courses. Else, you can create a new one by entering your email address, full name, and password of your choice. When will I have access to the lectures and assignments? To review, open the file in an editor that reveals hidden Unicode characters. You will also learn how to deploy your dashboards and provide a way to generate informative reports with R Markdown. MarioStrange / Data-Visualization-with-Python-Week-5-Final-Peer-Graded-Assignment.ipynb Created 10 months ago Star 0 Fork 1 Code Revisions 1 Forks 1 Embed Download ZIP Raw train and test a machine learning algorithm. The certificate and IBM digital badge you achieve will develop your professional profile and enhance your employability. The course does offer an IBM digital badge. 150+ UGC Approved Online Degree, Diploma & Cert A Beginner's Guide to Pursue Python Programming, 15 Best Online Courses to Learn Data Visualization, Information Security Design and Development. For more information about IBM visit: www.ibm.com, See how employees at top companies are mastering in-demand skills. If you don't see the audit option: The course may not offer an audit option. Thank you for publishing this awesome article. . See our full. Coursera, Indias biggest learning platform which launched millions of free courses for students daily. The course is 100% online and offers flexible lesson timings. 2022 Coursera Inc. All rights reserved. A challenging and rewarding specialization - thank you Joe and Scott! Question 9: Given a pandas dataframe, question, which of the following will create a horizontal barchart of the data in question? Could your company benefit from training employees on in-demand skills? To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. Question 15: Seaborn is a Python visualization library that is built on top of Matplotlib. TypeError: list indices must be integers or slices, not str Question 3: What are the layers that make up the Matplotlib architecture? Instantly share code, notes, and snippets. Design customized charts and plots using annotations, axis titles, text labels, themes, and faceting. Watch the videos, work through the labs, and watch your data science skill grow. This is final assignment for " Data Visualization with Python " course by IBM in Coursera Stars. You will learn to create various types of basic and advanced graphs and charts like: Waffle Charts, Area Plots, Histograms, Bar Charts, Pie Charts, Scatter Plots, Word Clouds, Choropleth Maps, and many more! Data Visualization with Python. how to resolve this. Yes. Rice University is consistently ranked among the top 20 universities in the U.S. and the top 100 in the world. This course will teach you to work with many Data Visualization tools and techniques. 187.9s. dependencies import Input, Output, State however, throughly enjoyed the depth of the course. Question 2: Matplotlib was created by John Hunter, an American neurobiologist, and was originally developed as an EEG/ECoG visualization tool. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world. Futurelearn, CloudSwyft Global Systems, Inc The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. Question 1: Which of the choices below will create the following regression line plot, given a pandas dataframe, data_dataframe? This course is part of the Introduction to Scripting in Python Specialization. SF Crime - Choropleth map. via Goal is to analyze the performance of the reporting airline to improve fight reliability thereby improving customer relaibility. .devcontainer Assignment Labs Certificate_of_Completion.pdf history Version 2 of 2. You can achieve the Data Visualization with Python certificate by completing the entire curriculum and graded assignments successfully. data analysis with python | all quiz & assignment answers | ibm data science | coursera data analysis with python ll part of ibm data science professional certificate offered by: ibm. You will learn how to create and customize Shiny apps, alter the appearance of the apps by adding HTML and image components, and deploy your interactive data apps on the web. Build employee skills, drive business results. Question 1: What tile style of Folium maps is usefule for data mashups and exploring river meanders and coastal zones? Yes, the Data Visualization with Python training will be taught by Saishruthi Swaminathan and Alex Aklson from IBM. can be easily created using Matplotlib using the scripting layer. 180.5s. 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\n". 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Certification: Cloud Architect, Preparing for Google Cloud Certification: Cloud Architect, Preparing for Google Certification!

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