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World Happiness Report 2021 Data Analysis using Python will help you learn how to analyze real world data using Python libraries. In this Data Analysis project, you will perform exploratory data analysis and answer specific questions with Happiness data 2021 from the United Nations. You will visualize data and see how happiness of a country is related to factors such as GDP, Social Support, Corruption, Life Expectancy, etc.
Dataset Link: World Happiness Data 2021
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What is World Happiness Report?
The United Nation’s Sustainable Development Solutions Network published the world happiness report on 19th March 2021 that ranks the world's 149 countries on how happy their citizens perceive themselves to be based on various indicators. The happiness study ranks the countries on the basis of questions from the Gallup World Poll. The results are then equated with other factors such as GDP, life expectancy, generosity, etc. In the year 2021, it focused on the effects of the Covid-19 pandemic and how people all over the world have managed to survive and prosper.
➡️ About Post Graduate Program In Data Analytics
This Data Analytics Program is ideal for all working professionals and prior programming knowledge is not required. It covers topics like data analysis, data visualization, regression techniques, and supervised learning in-depth via our applied learning model with live sessions by leading practitioners and industry projects.
✅ Key Features
Post Graduate Program certificate and Alumni Association membership
Exclusive hackathons and Ask me Anything sessions by IBM
8X higher live interaction in live online classes by industry experts
Capstone from 3 domains and 14+ Data Analytics Projects with Industry datasets from Google PlayStore, Lyft, World Bank etc.
Master Classes delivered by Purdue faculty and IBM experts
Simplilearn's JobAssist helps you get noticed by top hiring companies
Resume preparation and LinkedIn profile building
1:1 mock interview
Career accelerator webinars
✅ Skills Covered
Data Analytics
Statistical Analysis using Excel
Data Analysis Python and R
Data Visualization Tableau and Power BI
Linear and logistic regression modules
Clustering using kmeans
Supervised Learning
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