Data-driven Problem Solving for Data Analysts

Overview

Data analysts must handle complex business problems and translate raw data into actionable insights, leveraging multiple tools to implement the complete lifecycle of a data analysis project. In this course, Data-driven Problem Solving for Data Analysts, you'll gain the ability to understand the typical workflow of a data analysis project, from defining problems to presenting it to stakeholders, using tools like Microsoft Excel, Python, and Tableau along the way.

First, you'll explore how to define data-driven business problems and the process of data exploration. Next, you'll discover the process of cleaning and preparing data, and identifying key trends.

Finally, you’ll learn how the data analysis process can culminate in creating compelling visualizations and presenting your findings effectively to stakeholders. When you're finished with this course, you'll have the skills and knowledge of how the end-to-end data analysis workflow is applied to tackle real-world business problems and deliver actionable recommendations backed by data.

Syllabus

• Introduction to Data-driven Problem Solving
Course Overview and Objectives
Introduction to the Data Analysis Lifecycle
• Defining Data-driven Business Problems
Understanding Business Needs and Objectives
Translating Business Problems into Data Questions
Techniques for Effective Problem Definition
• Data Exploration
Introduction to Data Sources and Data Collection
Exploring Data with Microsoft Excel
Data Exploration using Python
• Data Cleaning and Preparation
Importance of Data Cleaning
Techniques for Data Cleaning in Excel
Data Cleaning and Preparation in Python
Handling Missing Data and Outliers
• Identifying Key Trends and Insights
Data Analysis Techniques
Using Excel for Trend Analysis
Python for Statistical Analysis
• Visualization and Insights Communication
Principles of Effective Data Visualization
Creating Visualizations with Tableau
Advanced Visualization Techniques in Python
• Presenting Findings to Stakeholders
Crafting a Compelling Narrative
Designing Presentation Materials with Key Insights
Techniques for Effective Communication
• Case Study: End-to-end Data Analysis Project
Defining the Problem
Data Exploration and Preparation
Trend Analysis and Visualization
Presentation and Recommendations
• Course Wrap-up and Next Steps
Summary of Key Learnings
Additional Resources and Further Learning
Final Project Submission and Feedback

Taught by

Ria Cheruvu

Subjects

Data Science