Data Analysis Project with Python: Electric Vehicles Population

 Data Analysis of the Electric Vehicles Population with Python

                                               USA 2014-2023

Summary

Introduction

Project Task Statement

The Data

Data Exploration And Preprocessing

Data Analysis:

Electric Vehicles: 2014-2023

Battery Electric Vehicle (BEV)

Plug-in Hybrid Electric Vehicle (PHEV)

Analysis Report: Electric Vehicles Population( USA:2014-2023)

Introduction

Electric Vehicles: A Sustainable Transportation Solution

Electric vehicles (EVs) are becoming increasingly popular as a sustainable alternative to traditional gasoline-powered cars. They use electricity stored in rechargeable batteries to power an electric motor, which in turn drives the wheels. Unlike conventional vehicles, EVs produce zero tailpipe emissions, reducing air pollution and contributing to a cleaner environment. They also offer a quieter and smoother driving experience.

This project will deliver a comprehensive analysis of the electric vehicle market, from understanding its size and growth to delving into regional sales data, electric range distributions, and regulatory aspects. The refined questions aim to provide clear and actionable insights, enabling stakeholders to make informed decisions based on market trends, regional data, vehicle performance, and eligibility for incentives. This holistic approach will help in grasping the current landscape and future trajectory of the electric vehicle market.

Project Task Statement

1. Assess the Electric cars market size and it's growth , overall and by car type.What are the Top 5 Electric Vehicles maker?

2. what is the market size in each state, county and city, list the top five city by total electric vehicles number.

3. how the electric range is distributed by electric vehicle type ?

4.What's the average electric range by electric vehicle type and how it was evolved in the last ten years?, list the top ten models make.

5.Explore the Clean Alternative Fuel Vehicle (CAFV) Eligibility by electric vehicle type and by vehicle make.

click the link to see the full project:

https://colab.research.google.com/drive/1hgLMtzMeEaB3zyuF8IvHBV0308L94LT6?usp=sharing

Data Analysis Project With Python: The Olympic Games

 Data Analysis of the Olympic Games with Python

                                                   Games 1896-2016

Summary

Introduction

Project Task

Data Source

Data Exploration And Preprocessing

Data Analysis:

All Modern Olympic Seasons

First Modern Olympic Season(1896)

Modern Olympic Seasons hosted in France

Analysis Report: Modern Olympic Games(1896-2016)

Introduction

The Olympic Games, a prestigious international sporting event, bring together athletes from around the world to compete in various sports. Originating in ancient Greece, the Olympics were revived in the late 19th century, marking the beginning of the modern Olympic era. Held every four years, the modern Olympic Games alternate between Summer and Winter editions, showcasing the pinnacle of athletic achievement. This project aims to explore key aspects of the modern Olympics, including historical timelines, participation trends, athlete demographics, and country performances, providing a comprehensive analysis of their evolution and impact on the global sporting

Full project:

https://colab.research.google.com/drive/114aXrVf7ZK5rYqTkObDMgsaxQAlDti8E?usp=sharing

Data An&lysis Tools

 Data analysis tools are software applications used to collect, organize, analyze, and interpret data. These tools can be used to identify patterns, trends, and insights that can help businesses make better decisions.   

Here are some of the most popular data analysis tools:

Microsoft Excel: A spreadsheet software that can be used for basic data analysis tasks.   

Tableau: A data visualization tool that can be used to create interactive dashboards and reports.   

Power BI: A business analytics tool from Microsoft that can be used to create interactive dashboards and reports.   

Python: A programming language that can be used for data analysis and machine learning.   

R: A programming language that is specifically designed for statistical computing and data analysis.   

SQL: A query language that can be used to extract data from databases.   

Google Analytics: A web analytics tool that can be used to track website traffic and user behavior.   

The best data analysis tool for you will depend on your specific needs and goals. If you are just getting started with data analysis, you may want to start with a tool like Microsoft Excel or Google Analytics. As you become more experienced, you can explore more advanced tools like Tableau or Power BI.   

https://www.coursera.org/articles/data-analysis-tools

******

Excel:

 https://play.google.com/store/apps/details?id=com.microsoft.office.excel&pli=1

Tableau:

https://www.tableau.com/#:~:text=Tableau%20helps%20people%20see%2C%20understand,use%20data%20to%20solve%20problems.

Power BI:

https://www.microsoft.com/en-us/power-platform/products/power-bi#:~:text=Connect%20to%20and%20visualize%20any,apps%20you%20use%20every%20day.

Python:

https://www.ucd.ie/professionalacademy/resources/why-do-data-analysts-use-python/#:~:text=While%20some%20data%20analysts%20use,choice%20due%20to%20its%20flexibility%2C

R :

https://www.r-project.org

Skills you'll gain from the Google Data Analytics Professional Certificates

 Google data analytics course :

Creating case studies

Data Visualization

Data Cleansing

Developing a portfolio

 Data Collection

Spreadsheet

Metadata

SQL

Data Ethics

Data Aggregation

Data Calculations

R Markdown

 R Programming

Rstudio

Tableau Software

Presentation

Data Integrity

Sample Size Determination

Decision-Making

Problem Solving

Questioning

advanced Google data analytics course;

Data Science

Data Analysis

Python Programming

Jupyter Notebook

Machine Learning

Statistical Analysis

Tableau Software

Data Visualization

Predictive Modelling

Kaggle

Exploratory Data Analysis (EDA)

Regression Models

Can I Learn Data Analytics?

 YES, you can Learn   Data Analytics .

How?

Enroll in  the Popular Data analytics Course

 Take Google Data Analytics Professional Course and the Google Advanced Data Analytics Professional Course and earn Google Data Analytics Certificate .

And Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

Google data analytics course :

   8 Course Series(8Certificates):

-Foundations: Data, Data, Everywhere

- Ask Questions to Make Data-Driven Decisions

-Analyze Data to Answer Questions

-Prepare Data for Exploration

-Process Data from Dirty to Clean

-Share Data Through the Art of Visualization

-Data Analysis with R Programming

-Google Data Analytics Capstone: Complete a Case Study

https://www.coursera.org/professional-certificates/google-data-analytics

advanced Google data analytics course;

  7 Course Series(7Certificates):

-Foundations of Data Science

-Get Started with Python

-Go Beyond the Numbers: Translate Data into Insights

-The Power of Statistics

-Regression Analysis: Simplify Complex Data Relationships

-The Nuts and Bolts of Machine Learning

-Google Advanced Data Analytics Capstone

https://www.coursera.org/professional-certificates/google-advanced-data-analytics

Data analysis Definition

 Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

                                                                       From Wikipedia, the free encyclopedia

Exploring the World of Data Analysis Tools

Welcome to "Data Analysis Tools," your comprehensive resource for all things related to data analysis in the modern age! Whether you're a data professional seeking to enhance your skills or someone interested in delving into the fascinating realm of data analysis, this blog is here to guide you through the ever-evolving landscape of tools and techniques.

In today's data-driven world, organizations across industries rely on the power of data analysis to uncover valuable insights, make informed decisions, and drive business success. However, with the multitude of tools available, it can be overwhelming to navigate the vast array of options. That's where this blog comes in – we're here to simplify the process and help you make the most of these tools.