IBM Data Analytics with Excel and R

The IBM Data Analytics with Excel and R Professional Certificate is designed to provide you with the necessary skills to thrive in the rapidly growing field of data analytics. This program focuses on using Microsoft Excel and R programming to analyze data, derive insights, and make informed decisions in a variety of business contexts.

Total Learning Hours: 124

📍 About this Professional Certificate

This Professional Certificate in Data Analytics is designed to prepare you for a career in the fast-growing field of data analytics. Over the course of 9 comprehensive courses, you will acquire essential skills that are highly valued by employers, including expertise in Excel, Cognos Analytics, and the R programming language.

📙 Course Structures

There are 9 Courses in this Professional Certificate Specialization are as follows:

Introduction to Data Analytics

Excel Basics for Data Analysis

Data Visualization and Dashboards with Excel and Cognos

Assessment for Data Analysis and Visualization Foundations

Introduction to R Programming for Data Science

SQL for Data Science with R

Data Analysis with R

Data Visualization with R

Data Science with R - Capstone Project

🌟 Best Final Projects

Project 1: Analyzing Real-World COVID-19 Testing Data with R

• Description: In this project, I served as a data analyst for a news channel's documentary team, focusing on creating a feature story about global COVID-19 testing by country. My primary objective was to provide insightful, data-driven narratives that would enhance the documentary's impact. By leveraging my R programming skills, I extracted, processed, and visualized data to uncover trends and comparisons across various nations.
• Tools Used: R programming
• Link: Here

Project 2: Weather Data Analysis Project: JFK Airport

• Description: In this project, I analyzed the NOAA Weather Dataset for JFK Airport to predict precipitation levels, which helped me enhance my skills in data analysis and modeling.

Key Highlights:

• Model Development: Split the dataset into training and testing sets. Employed the tidymodels framework to test various models, focusing on linear regression for precipitation prediction.

• Model Evaluation: Assessed model performance using metrics such as RMSE (Root Mean Square Error) and R-squared.

• Tools Used: Programming Language: R

  • Libraries: tidymodels, tidyverse, ggplot2, tidyr, dplyr, recipes

• Link: Here

🛠 Skills Developed

• 📊 Utilize Excel for Data Analysis:

  • Perform data manipulation and analysis using advanced Excel functions.
  • Create charts and dashboards to visualize data insights.

• 🔍 IBM Cognos Analytics

  • Learn how to use IBM Cognos Analytics for data visualization and reporting.
  • Develop reports and dashboards that provide actionable insights.

• 📊 R Programming: Utilize R for the complete data analysis process, including:

  • Data Preparation: Handle missing values and convert categorical data to numeric.
  • Statistical analysis
  • Data visualization
  • Predictive Modeling: Compare simple linear, multiple linear, and polynomial regression models.
  • Creating interactive dashboards
  • Data Preparation: Handle missing values and convert categorical data to numeric.
  • Model Evaluation: Identify overfitting and underfitting, and apply regularization and grid search for tuning.
  • Data Examination: Use descriptive statistics and perform ANOVA and correlation analysis.

• 🗃️ SQL Proficiency:

  • Create, read, update, and delete data in databases.
  • Write complex queries to filter, sort, and aggregate data.
  • Join multiple tables to extract meaningful insights.
  • Utilize built-in SQL functions for data analysis.
  • Construct and execute nested queries for advanced data manipulation.

• 📈 Dashboard Development

• 🎨 Data Visualization

---

© 2025 Ali Sufayran