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ibm data visualization with python final assignment 2022

Data Visualization with Python

Learn various tools of data visualisation and tell a compelling story with Coursera’s Data Visualization with Python training by IBM.

The highlights

Who it is for, eligibility criteria, what you will learn.

  • Admission Details

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Filling the form.

  • Scholarship

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Intermediate

Quick facts, course overview.

Data visualisation plays a vital role in explaining the insights you obtain after analysing large or small data sets. It is one of the essential skills a  data scientist must possess to explain the data in a stimulating and approachable way. 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. 

Data Visualization with Python online course will explore various developed techniques to present the data in a simple manner. You will also use multiple data visualisation libraries in Python like Folium, Seaborn and Matpotlib. This course is offered by IBM and will be taught by two experienced data scientists from IBM, namely Alex Aklson and Saishruti Swaminathan. 

You can audit the Data Visualization with Python certification for free. However, there is a certification and an IBM digital badge up for grabs as well. The course is 100% online and offers flexible lesson timings.  

  • Videos in English with subtitles in multiple languages
  • IBM digital badge
  • 100% digital course
  • 19 hours of learning
  • Certification available
  • Graded assignments
  • Practice quizzes
  • Free 7-day trial
  • Financial aid available
  • Reading materials
  • Flexible deadlines
  • Offered by IBM    
  • Free to audit

Program offerings

  • Offered by ibm
  • Ibm digital badge

Course and certificate fees

  • You can audit the Data Visualization with Python certification course for free. But you must pay the programme fee to access the graded exercises and the certificate.  

Data Visualization with Python course fee structure

certificate availability

Certificate providing authority.

The Data Visualization with Python course is ideal for  data scientists who want to improve their skills.  

Certificate Qualifying Details

You can achieve the Data Visualization with Python certificate by completing the entire curriculum and graded assignments successfully. 

Once you complete the Data Visualization with Python syllabus, you will be well versed in:  

  • Visualising data insights in a way that makes sense to people
  • Applying various techniques for a visual representation of data
  • Using Python visualisation libraries such as Folium, Matpotlib, and Seaborn
  • Python programming

Admission details

  • Begin your enrollment for the Data Visualization with Python course https://www.coursera.org/learn/python-for-data-visualization.
  • Sign in or sign up on Coursera.
  • Select ‘Enroll for free. 
  • Confirm the course name and select a suitable enrolment option. You can either opt to audit the course to start a seven-day free trial or pay the certification fee. 
  • Pay the required fee and begin the course.

You are only required to fill out a sign-up form for the Data Visualization with Python training course. If you already have a Coursera account, sign in to enrol. Else, you can create a new one by entering your email address, full name, and password of your choice. You may also sign in with your Facebook or Google account. 

Module 1: Introduction to Data Visualisation Tools

  • Introduction to Data Visualization
  • Introduction to Matplotlib
  • Basic Plotting with Matplotlib
  • Dataset on Immigration to Canada
  • [Optional] Download Jupyter Notebook
  • Introduction to Data Visualization Tools
  • Hands-on Lab: Exploring and Pre-processing a Dataset using Pandas
  • Hands-on Lab: Introduction to Matplotlib and Line Plots
  • Cheat Sheet: Data Preprocessing Tasks in Pandas & Plot Libraries

Module 2: Basic and Specialized Visualization Tools

  • Scatter Plots
  • Plotting Directly with Matplotlib
  • Summary: Basic and Specialized Visualization Tools
  • Practice Quiz: Basic Visualization Tools
  • Practice Quiz: Specialized Visualization Tools
  • Graded Quiz: Basic and Specialized Visualization Tools
  • Hands-on Lab: Area Plots, Histograms, and Bar Charts
  • Hands-on Lab: Pie Charts, Box Plots, Scatter Plots, and Bubble Plots
  • Hands-on Lab: Plotting Directly with Matplotlib
  • Cheat Sheet: Plotting with Matplotlib using Pandas

Module 3: Advanced Visualizations and Geospatial Data

  • Waffle Charts
  • Word Clouds
  • Seaborn and Regression Plots
  • Introduction to Folium
  • Maps with Markers
  • Choropleth Maps
  • Summary: Advanced Visualizations and Geospatial Data
  • Practice Quiz: Advanced Visualization Tools
  • Practice Quiz: Visualizing Geospatial Data
  • Graded Quiz: Advanced Visualizations and Geospatial Data
  • Hands-on Lab: Waffle Charts, Word Clouds, and Regression Plots
  • Hands-on Lab: Creating Maps and Visualizing Geospatial Data
  • Cheat Sheet: Maps, Waffles, WordCloud and Seaborn

Module 4: Creating Dashboards with Plotly and Dash

  • Dashboarding Overview
  • Introduction to Plotly
  • Introduction to Dash
  • Make Dashboards Interactive
  • Understanding the Lab Environment
  • Additional Resources for Dashboards
  • Additional Resources for Plotly
  • Additional Resources for Dash
  • Additional Resources for Interactive Dashboards
  • Summary: Creating Dashboards with Plotly and Dash
  • Practice Quiz: Creating Dashboards with Plotly
  • Practice Quiz: Working with Dash
  • Graded Quiz: Creating Dashboards with Plotly and Dash
  • Plotly Basics: Scatter, Line, Bar, Bubble, Histogram, Pie, Sunburst
  • Dash Basics: HTML and Core Components
  • Add Interactivity: User Input and Callbacks
  • Flight Delay Time Statistics Dashboard
  • Overview of Cloud IDE Lab Environment
  • Cheat Sheet: Plotly and Dash

Module 5: Final Project and Exam

Scholarship details.

If you cannot afford to pay the certification fee for the Data Visualization with Python course, you can take financial aid from Coursera. 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. After submission, the Coursera representatives will take about two weeks to reach out.  

Joining the Data Visualization with Python training by Coursera will help you develop critical skills like Python programming, Matplotlib, Data visualisation, and Data visualisation (DataViz). The certificate and IBM digital badge you achieve will develop your professional profile and enhance your employability.

Instructors

Ms Saishruthi Swaminathan

Ms Saishruthi Swaminathan Data Scientist IBM

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ibm data visualization with python final assignment 2022

ibm data visualization with python final assignment 2022

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Learning Outcomes:

  • Demonstrate a working knowledge of Data Science Tools such as Jupyter Notebooks, R Studio, GitHub, Watson Studio
  • Understand the popular tools and statistical techniques used by data scientists including Descriptive Statistics, Data Visualization, Probability Distribution, Hypothesis Testing and Regression, including how to effectively choose the right chart type for the audience and data type
  • Demonstrate programming skills for working with data including data structures, logic, working with files, invoking APIs, and libraries such as Pandas and Numpy
  • Describe concepts related to accessing Databases using Python
  • Describe SQL and Databases and demonstrate understanding of Relational Database fundamentals including SQL query language, Select statements, sorting & filtering, database functions, accessing multiple tables

General Topics:

  • Data Scientist's Toolkit
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  • IBM Tools for Data Science
  • Final Assignment: Create and Share Your Jupyter Notebook
  • Python Basics
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  • Crowdsourcing Short squeeze Dashboard
  • Course Introduction and Python Basics
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  • Introduction to Probability Distributions
  • Hypothesis testing
  • Regression Analysis
  • Project Case: Boston Housing Data
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  • Bonus Module: Advanced SQL for Data Engineering (Honors)

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  • five peer review graded projects with rubrics

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Analyzing the Impact of Recession on Automobile Sales

diogommiranda/Data-Visualization-with-Python-IBM

Folders and files, repository files navigation, data-visualization-with-python-ibm, final project overview.

Analyzing the Impact of Recession on Automobile Sales You have been hired by XYZAutomotives as a data scientist. Your first task is to analyze the historical data and give the company directors insights on how the sales were affected during times of recession. You will provide a number of charts/plots to visualize the data and make it easy for the directors to understand your analysis.

Instructions for submission

You will be required to upload images showing your plots or dashboards, for your peers to review and award points. For each task you will be directed to save your images locally with a specific name. We rcommend that you create a local folder and save all your images there for easy reference.

At the end of each task, you are provided with the name to save your plot/chart image.

For example:

Save this plot as "Line_Plot_1.png" Hint: You can right lick on the plot and then click on "Save image as" option to save it on your local machine

About the dataset

In this assignment, you will be presented with various questions for analysing data to understand the historical trends in automobile sales during recession periods.

recession period 1 - year 1980

recession period 2 - year 1981 to 1982

recession period 3 - year 1991

recession period 4 - year 2000 to 2001

recession period 5 - year end 2007 to mid 2009

recession period 6 - year 2020 -Feb to April (Covid-19 Impact)

The data used in this lab has been artificially created for this assignment only. No real data has been used.

Data Description

The dataset includes the following variables:

Date: The date of the observation. Recession: A binary variable indicating a recession period; 1 means it was a recession, and 0 means it was normal. Automobile_Sales: The number of vehicles sold during the period. GDP: The per capita GDP value in USD. Unemployment_Rate: The monthly unemployment rate. Consumer_Confidence: A synthetic index representing consumer confidence, which can impact consumer spending and automobile purchases. Seasonality_Weight: The weight representing the seasonality effect on automobile sales during the period. Price: The average vehicle price during the period. Advertising_Expenditure: The advertising expenditure of the company. Vehicle_Type: The type of vehicles sold; Supperminicar, Smallfamiliycar, Mediumfamilycar, Executivecar, Sports. Competition: The measure of competition in the market, such as the number of competitors or market share of major manufacturers. Month: Month of the observation extracted from Date. Year: Year of the observation extracted from Date. By examining various factors mentioned above from the dataset, you aim to gain insights into how recessions impact automobile sales for your company.

This project will be completed in 3 Parts:

Part 1: Create Visualizations using Matplotlib, Seaborn & Folium Part 2: Create a Dashboard using Plotly and Dash Part 3: Submit your project and evaluate your peers Part 1: Create visualizations using Matplotib, Seaborn & Folium Objective: The objective of this part of the Final Assignment is to analyze the historical trends in automobile sales during recession periods. The goal is to provide insights into how the sales of XYZAutomotives, a company specializing in automotive sales, were affected during times of recession.

In this lab, you will create visualizations using Matplotlib, Seaborn, and Pandas.

Tasks to be performed

TASK 1.1: Develop a Line chart using the functionality of pandas to show how automobile sales fluctuate from year to year.

TASK 1.2: Plot different lines for categories of vehicle type and analyze the trend to answer the question “Is there a noticeable difference in sales trends between different vehicle types during recession periods?”

TASK 1.3: Use the functionality of Seaborn Library to create a visualization to compare the sales trend per vehicle type for a recession period with a non-recession period.

TASK 1.4: Use subplotting to compare the variations in GDP during the recession and non-recession periods by developing line plots for each period.

TASK 1.5: Develop a Bubble plot to display the impact of seasonality on Automobile Sales.

TASK 1.6: Use the functionality of Matplotlib to develop a scatter plot to identify the correlation between average vehicle price and the sales volume during recessions.

TASK 1.7: Create a pie chart to display the portion of advertising expenditure of XYZAutomotives during the recession and non-recession periods.

TASK 1.8: Develop a pie chart to display the total Advertisement expenditure for each vehicle type during the recession period.

TASK 1.9: Develop a lineplot to analyze the effect of the unemployment rate on vehicle type and sales during the Recession Period.

Part 2: Create a Dashboard using Plotly and Dash Objective: The objective of this part of the Final assignment is to create dashboards to contain your plots and charts and to provide the directors with the ability to select a particular report or a period of time so they can discuss the data in detail.

In this lab, you will create dashboards using Dash and Plotly and then add user-interactions to your dashboards.

Creating dashboards and adding customizations to the dashboards The directors of XYZAutomobiles have requested a dashboard to be developed so they can drill into the data in more detail for specific years or by different categories. Your second task is to create a suitable dashboard and add in user interactions so that the directors can select the data they want to review without the need to request new plots.

TASK 2.1: Create a Dash application and give it a meaningful title.

TASK 2.2: Add drop-down menus to your dashboard with appropriate titles and options.

Task 2.3: Add a division for output display with appropriate id and classname property

TASK 2.4: Creating Callbacks; Define the callback function to update the input container based on the selected statistics and the output container.

TASK 2.5: Create and display graphs for Recession Report Statistics.

TASK 2.6: Create and display graphs for Yearly Report Statistics.

Part 3: Submit your project and evaluate your peers Once you have completed the labs and saved all your files locally you will submit your assignment which will be graded by your peers who are also completing this course during the same session.

Points will be awarded as follows for each task completed:

Part 1: Total 10 points

TASK 1.1: Develop a Line chart using the functionality of pandas to show how automobile sales fluctuate from year to year. (1 point)

TASK 1.2: Plot different lines for categories of vehicle type and analyze the trend to answer the question “Is there a noticeable difference in sales trends between different vehicle types during recession periods?” (1 point)

TASK 1.3: Use the functionality of Seaborn Library to create a visualization to compare the sales trend per vehicle type for a recession period with a non-recession period. (1 point)

TASK 1.4: Use sub plotting to compare the variations in GDP during the recession and non-recession periods by developing line plots for each period. (2 points)

TASK 1.5: Develop a Bubble plot to display the impact of seasonality on Automobile Sales. (1 point)

TASK 1.6: Use the functionality of Matplotlib to develop a scatter plot to identify the correlation between average vehicle price and sales volume during recessions. (1 point)

TASK 1.7: Create a pie chart to display the portion of advertising expenditure of XYZAutomotives during the recession and non-recession periods. (1 point)

TASK 1.8: Develop a pie chart to display the total Advertisement expenditure for each vehicle type during the recession period. (1 point)

TASK 1.9: Develop a lineplot to analyze the effect of the unemployment rate on vehicle type and sales during the Recession Period. (1 point)

Part 2: Total 14 points

TASK 2.1: Create a Dash application and give it a meaningful title. (2 points)

TASK 2.2: Add drop-downs to your dashboard with appropriate titles and options. (1 point)

TASK 2.3: Add a division for output display with appropriate id and classname property. (1 point)

TASK 2.4: Creating Callbacks; Define the callback function to update the input container based on the selected statistics and the output container. (5 points)

TASK 2.5: Create and display graphs for Recession Report Statistics (3 points)

TASK 2.6: Create and display graphs for Yearly Report Statistics. (2 points)

  • Jupyter Notebook 99.4%
  • Python 0.6%

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  1. Data visualization with python peer graded assignment 2022

    ibm data visualization with python final assignment 2022

  2. Final exam of Data Analysis with python by IBM

    ibm data visualization with python final assignment 2022

  3. Coursera Course Data Visualization With Python

    ibm data visualization with python final assignment 2022

  4. IBM Data Visualization with Python__Final Exam

    ibm data visualization with python final assignment 2022

  5. Data-Visualization-with-Python/Week 5

    ibm data visualization with python final assignment 2022

  6. IBM Certified : Data Visualization with Python-IBM Course

    ibm data visualization with python final assignment 2022

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  1. |python for Data science IBM| |python||IBM||python for Data science IBM final assessment| #shorts

  2. Coursera: IBM

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  4. Python Class16 For Data Scientist || Data Analyst || Numpy

  5. Coursera: IBM

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COMMENTS

  1. GitHub

    IBM-Data-Visualization-Final-Assignment- Assignment Story Components of the report items Expected layout Requirements to create the dashboard What is new in this exercise compared to other labs? Review Hints to complete TODOs Application Story:

  2. Data Visualization with Python

    This is the Final Assignment for the course Data Visualization with Python, as part of the IBM Data Science Professional Certification on Coursera, taught by Alex Aklson (Ph.D., Data Scientist / IBM Developer Skills Network). Even though the report has not been requested, I took the chance to practice with it.

  3. GitHub

    main README Final-Assignment Data Visualization with Python - Final Assignment Import required libraries import pandas as pd import dash import dash_html_components as html import dash_core_components as dcc from dash.dependencies import Input, Output, State import plotly.graph_objects as go import plotly.express as px from dash import no_update

  4. IBM Data Visualization Python_Final Assignment

    IBM Data Visualization with Python ¶ Hello everyone! This is my first published notebook :) In this notebook I will be answering the questions for my final assignment for IBM Data Visualization with Python course on Coursera In [2]: import matplotlib as mpl import matplotlib.pyplot as plt import matplotlib.patches as patches

  5. Final Assignment

    Python · No attached data sources. Final Assignment - Data Visualization with Python . Notebook. Input. Output. Logs. Comments (0) Run. 180.5s. history Version 1 of 1. menu_open. License. This Notebook has been released under the Apache 2.0 open source license. Continue exploring. Input.

  6. Data Visualization with Python

    Implement data visualization techniques and plots using Python libraries, such as Matplotlib, Seaborn, and Folium to tell a stimulating story. Create different types of charts and plots such as line, area, histograms, bar, pie, box, scatter, and bubble. Create advanced visualizations such as waffle charts, word clouds, regression plots, maps ...

  7. Free Course: Data Visualization with Python from IBM

    You will use several data visualization libraries in Python, including Matplotlib, Seaborn, Folium, Plotly & Dash. Syllabus Introduction to Data Visualization Tools Data visualization is a way of presenting complex data in a form that is graphical and easy to understand.

  8. Data visualization with Python

    Published May 25, 2021 Summary Learn how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually, but in this course you use several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium. Legend

  9. Here's Your Guide to IBM's "Data Visualization with Python" Final

    This article will guide you to accomplish the final assignment of Data Visualization with Python, a course created by IBM and offered by Coursera. Nevertheless, this tutorial is for anyone— enrolled in the course or not — who wants to learn how to code an interactive dashboard in Python using Plotly's Dash library.

  10. Learner Reviews & Feedback for Data Visualization with Python by IBM

    Learner Reviews & Feedback for Data Visualization with Python by IBM Enroll for Free Starts Feb 19 4.5 11,421 ratings About the Course One of the most important skills of successful data scientists and data analysts is the ability to tell a compelling story by visualizing data and findings in an approachable and stimulating way.

  11. Final Dashboard Assignment with IBM

    Final Dashboard Assignment with IBM | Kaggle Sonya Lawrence-Thompson · Linked to GitHub · 2y ago · 7,279 views arrow_drop_up Copy & Edit more_vert Final Dashboard Assignment with IBM Python · airline_data Notebook Input Output Logs Comments (0) Run 4.2 s history Version 3 of 3

  12. Marvin Rubia on LinkedIn: #datavisualization #dashboard #datascience #

    Here's the expected final assignment from IBM 's Data Visualization with Python, a course offered by Coursera. The task is to code an interactive dashboard using Plotly's Dash and...

  13. theju0528/Final-Asiignment-Data-Visualization-python-IBM

    The final assignment solution for IBM Data Visualization with python - theju0528/Final-Asiignment-Data-Visualization-python-IBM

  14. Data Visualization with Python

    Starts Jan 29 Sponsored by SVEC + MBU 253,162 already enrolled About Outcomes Modules Testimonials Reviews What you'll learn Implement data visualization techniques and plots using Python libraries, such as Matplotlib, Seaborn, and Folium to tell a stimulating story

  15. Data Visualization with Python by IBM via Coursera: Fee ...

    Data visualisation plays a vital role in explaining the insights you obtain after analysing large or small data sets. It is one of the essential skills a data scientist must possess to explain the data in a stimulating and approachable way. 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 ...

  16. IBM Data Science Fundamentals with Python and SQL Specialization

    Objective: This course is offered through Coursera, which is an ACE Authorized Instructional Platform. The specialization consists of 5 self-paced online modules that provide learners with the foundational skills required for Data Science, including open source tools and libraries, Python, Statistical Analysis, SQL, and relational databases.

  17. Databases and SQL for Data Science with Python

    Accessing Databases using Python. Module 4 • 4 hours to complete. In this module you will learn the basic concepts of using Python to connect to databases. In a Jupyter Notebook, you will create tables, load data, query data using SQL magic and SQLite python library. You will also learn how to analyze data using Python.

  18. GitHub: Let's build from here · GitHub

    {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"Final_Assignment","path":"Final_Assignment","contentType":"file"}],"totalCount":1 ...

  19. Badge: Data Visualization with Python

    This badge earner has a good understanding of what data visualization is, uses of data visualization, and best practices when creating plots and visuals. The individual has the skills to use different Python Libraries, mainly Matplotlib and Seaborn to generate different types of visualization tools such as line plots, scatter plots, bubble plots, area plots, histograms, and bar charts.

  20. Data analysis using Python

    This learning path is designed to give you an overview of working with data using Python. It includes details on working with Python, GeoPandas, vector data, and raster data. Skill level. Beginner. Estimated time to complete. Approximately 2 hours. Learning objectives. This learning path covers the following topics: Python overview

  21. ibm-data-science · GitHub Topics · GitHub

    python data-science clustering regression assignment classification recommendation-system ibm coursera-data-science coursera-specialization ibm-data-science ibm-data-science-professional analyzing-us-economy python-for-data-science-and-ai data-science-assignment python-for-data-science-quiz data-science-quiz analyzing-us-economic-data

  22. IBM Data Science Professional Certificate Projects

    Data Visualization with Python Machine Learning with Python Applied Data Science Capstone Project/assignment notebooks for courses 2, 4, 5, 6, 7, 8 and 9 are included in this repository. Courses 1 and 3 only have quizzes as part of their assignments. Hence, there are no notebooks for them.

  23. diogommiranda/Data-Visualization-with-Python-IBM

    Data-Visualization-with-Python-IBM Final Project Overview. Analyzing the Impact of Recession on Automobile Sales You have been hired by XYZAutomotives as a data scientist. Your first task is to analyze the historical data and give the company directors insights on how the sales were affected during times of recession.