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IBM Data Analyst Capstone Project
IBM via Coursera Help
- Data Collection
- Data Collection is the first step in solving any analysis problem and can be collected in many formats and from many sources. In the first module of the Capstone, we will collect data by scraping the internet and using web APIs.
- Data Wrangling
- In this module, you will be focusing on the cleaning of your dataset with various techniques. With these techniques you will be identifying duplicate rows, finding missing values, and normalizing the data.
- Exploratory Data Analysis
- In this module, begin working with the cleaned dataset from the previous module. You will now begin to analyze the dataset to find the distribution of data, presence of outliers and the correlation between different columns.
- Data Visualization
- In module 4 of the Capstone, you will be required to create visualizations using the developer survey data. The visualizations you create should highlight the distribution of data, relationships between data, the composition of data, and comparison of data.
- Building A Dashboard
- In this module, you will create a dashboard using IBM Cognos Analytics or Google Looker Studio. This platform will give you the ability to create various charts while assembling a dashboard that is appealing and easy to understand. Your dashboard will contain your data analysis, which should be intuitive and allow for the drill-down of data.
- Final Assignment: Present Your Findings
- You have analyzed the data in the previous modules, and now it is time to demonstrate your storytelling skills. In this module, you will create a compelling story that helps to clarify your analysis in an easy-to-understand presentation.
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IBM Data Analyst Professional Capstone project
ashwinisingh98/Data-Analyst-Capstone-Project-IBM
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Data-analyst-capstone-project-ibm.
Analysis Dashboard Link: 🔗
Project Presentaion Link: 🔗
In this project, assuming myself as the Data Analyst for the company, I undertook the tasks of collecting data from multiple sources, performing exploratory data analysis, data wrangling and preparation, statistical analysis and mining the data, creating charts and plots to visualize data, and building an interactive dashboard using IBM Cognos Analytics.
Key requirements of analysis are identifying skills requirements for future, top programming languages, database skills in demand and IDEs in demand
At the end of project,I prepared a presentation of my data analysis report, with an executive summary for the various stakeholders in the organization including Human Resource and IT Head.
- Jupyter Notebook 100.0%
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This repository contains the Capstone Project for the IBM Data Analyst Certificate by Coursera. This repository contains the raw data, jupyter notebooks under Cheat Sheets, and visualizations of the results. The dashboard can be viewed from the link below: click here. The final certificate of the course can be viewed from this link.
When the data is ready you will then want to apply statistical techniques to analyze the data. Then bring all of your information together by using IBM Cognos Analytics to create your dashboard. And finally, show off your storytelling skills by sharing your findings in a presentation. You will be evaluated using quizzes in each module as well ...
Gain the job-ready skills for an entry-level data analyst role through this eight-course Professional Certificate from IBM and position yourself competitively in the thriving job market for data analysts, which will see a 20% growth until 2028 (U.S. Bureau of Labor Statistics).. Power your data analyst career by learning the core principles of data analysis and gaining hands-on skills practice.
By completing this final capstone project you will apply various Data Analytics skills and techniques that you have learned as part of the previous courses in the IBM Data Analyst Professional Certificate. You will assume the role of an Associate Data Analyst who has recently joined the organization and be presented with a business challenge ...
This badge earner has demonstrated an application of Data Analytics skills and techniques using a hands-on project involving a real-world dataset. The individual has gathered the required datasets, cleaned and wrangled data, performed data mining and analysis, created charts and visualizations, built interactive dashboards, and presented the results using a PowerPoint presentation.
In this capstone project, I worked on everything I learnt in this course; from building dashboards in IBM Cognos analytics to extracting data using web scraping and APIs, to cleaning the datasets and analyzing and exploring the datasets in python using Jupyter notebook which was imported in IBM Watson studio.
RESULTS In the Results section of the IBM Data Analyst Capstone Final Project, participants present data-driven insights, highlighting trends, patterns, and correlations. They use visuals like charts and graphs for clarity. Statistical significance, as demonstrated by p-values and confidence intervals, lends credibility to their findings.
This is last assignment in completion of IBM Data Analyst Certificate. It is to test the skills learned in the course including the Data Collection, Data Extracting, Data Wrangling, Data Visualisation, and Presentation of the findings using SQL, Python, IBM Cognos.
incorporate Python into their curriculum to meet the preferences and demands of the younger generation. The variation in database preferences across countries highlights the importance of understanding local. market trends and preferences when it comes to database technologies. Organizations operating in different.
Offered by IBM. By completing this final capstone project you will apply various Data Analytics skills and techniques that you have learned ... Enroll for free.
Data Collection is the first step in solving any analysis problem and can be collected in many formats and from many sources. In the first module of the Capstone, we will collect data by scraping the internet and using web APIs. Data Wrangling. In this module, you will be focusing on the cleaning of your dataset with various techniques.
This is my capstone project from the IBM Data Analyst course. In each analytics process, the data is stored in the Jupyter notebooks that are uploaded. Week 1 in the project is data collection; 2 is data wrangling, 3 is exploratory data analysis, 4 is Data visualisation, 5 is Building a dashboard and the last week is Presentation of findings.
Data wrangling: Cleaning the dataset with various techniques: identifying duplicate rows, finding missing values and normalizing the data •. Exploratory Data Analysis: Finding the distribution of data, identify outliers and the correlation between different columns •. Data Visualization: Creating visualizations using IBM Cognos and PowerPoint.
It will be applied various Data Analytics skills and techniques that wehave learned in the IBM Data Analyst Professional Certificate. It will be assumed the role of an Associate Data Analyst and be presented with a business challenge that requires data analysis to be performed on real-world datasets. Topics
IBM Data Analyst Capstone Project Marta S. September 2023. OUTLINE •Executive Summary •Introduction •Methodology •Results •Visualization - Charts ... •Data Wrangling •Exploratory Data Analysis •Distribution, Outliers, and Correlation •Data Visualization •IBM Cognos Analytics •Dashboard Creation •Presentation.
IBM Data Analyst Professional Capstone project. Analysis Dashboard Link: 🔗 Project Presentaion Link: 🔗 In this project, assuming myself as the Data Analyst for the company, I undertook the tasks of collecting data from multiple sources, performing exploratory data analysis, data wrangling and preparation, statistical analysis and mining the data, creating charts and plots to visualize ...
EXECUTIVE SUMMARY. This research project investigated the relationship between demand for bike rentals, the weather and date/time. The data collection used webscraping to create csv files from wikipedia and the Open Weather API. The data analysis focused on Seoul, South Korea and used SQL queries and a variety of graphs and models.