Managing Data Analysis

  • 4.6
Approx. 9 hours to complete

Course Summary

This course teaches you how to manage data analysis with advanced Excel techniques and data visualization tools. You'll learn how to create structured data models, use advanced functions and formulas, and build interactive dashboards to communicate insights.

Key Learning Points

  • Learn advanced Excel techniques for managing and analyzing data
  • Gain skills in data modeling, using functions and formulas, and building dashboards
  • Explore data visualization tools and best practices for communicating insights

Job Positions & Salaries of people who have taken this course might have

  • Data Analyst
    • USA: $62,453
    • India: ₹480,000
    • Spain: €30,000
  • Business Intelligence Analyst
    • USA: $73,459
    • India: ₹600,000
    • Spain: €35,000
  • Data Scientist
    • USA: $117,345
    • India: ₹1,000,000
    • Spain: €50,000

Related Topics for further study


Learning Outcomes

  • Create structured data models using advanced Excel techniques
  • Use data visualization tools to communicate insights effectively
  • Build interactive dashboards to support data-driven decision making

Prerequisites or good to have knowledge before taking this course

  • Familiarity with basic Excel functions and formulas
  • Basic understanding of data analysis concepts

Course Difficulty Level

Intermediate

Course Format

  • Self-paced
  • Online
  • Video Lectures

Similar Courses

  • Data Analysis and Presentation Skills: the PwC Approach
  • Data Visualization by IBM

Related Education Paths


Notable People in This Field

  • Excel Guru
  • Data Visualization Expert

Related Books

Description

This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results.

Knowledge

  • Differentiate between various types of data pulls
  • Describe the basic data analysis iteration
  • Explore datasets to determine if data is appropriate for a project
  • Use statistical findings to create convincing data analysis presentations

Outline

  • Managing Data Analysis
  • What this Course is About
  • Data Analysis Iteration
  • Stages of Data Analysis
  • Six Types of Questions
  • Characteristics of a Good Question
  • Exploratory Data Analysis Goals & Expectations
  • Using Statistical Models to Explore Your Data (Part 1)
  • Using Statistical Models to Explore Your Data (Part 2)
  • Exploratory Data Analysis: When to Stop
  • Making Inferences from Data: Introduction
  • Populations Come in Many Forms
  • Inference: What Can Go Wrong
  • General Framework
  • Associational Analyses
  • Prediction Analyses
  • Inference vs. Prediction
  • Interpreting Your Results
  • Routine Communication in Data Analysis
  • Making a Data Analysis Presentation
  • Pre-Course Survey
  • Course Textbook: The Art of Data Science
  • Conversations on Data Science
  • Data Science as Art
  • Epicycles of Analysis
  • Six Types of Questions
  • Characteristics of a Good Question
  • EDA Check List
  • Assessing a Distribution
  • Assessing Linear Relationships
  • Exploratory Data Analysis: When Do We Stop?
  • Factors Affecting the Quality of Inference
  • A Note on Populations
  • Inference vs. Prediction
  • Interpreting Your Results
  • Routine Communication
  • Post-Course Survey
  • Data Analysis Iteration
  • Stating and Refining the Question
  • Exploratory Data Analysis
  • Inference
  • Formal Modeling, Inference vs. Prediction
  • Interpretation
  • Communication

Summary of User Reviews

Learn how to manage and analyze data effectively with this comprehensive course on Managing Data Analysis. Students have given this course very positive reviews, highlighting its practical applications and real-world relevance.

Key Aspect Users Liked About This Course

Many users have praised the practical approach of this course, which focuses on teaching real-world skills and techniques that can be applied to various industries and contexts.

Pros from User Reviews

  • Hands-on exercises and case studies help students apply concepts to real-world scenarios
  • Course content is well-structured and easy to follow
  • Instructors are knowledgeable and provide helpful feedback and guidance
  • Course materials and resources are comprehensive and useful

Cons from User Reviews

  • Some users have reported technical issues with the online platform
  • Course may be too basic for advanced users with extensive data analysis experience
  • Not all topics are covered in-depth, some users would have liked more advanced content
English
Available now
Approx. 9 hours to complete
Jeff Leek, PhD, Brian Caffo, PhD, Roger D. Peng, PhD
Johns Hopkins University
Coursera

Instructor

Jeff Leek, PhD

  • 4.6 Raiting
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