Effective Data Analysis: Hard and Soft Skills

Learn the technical and soft skills you need to succeed in your career as a data analyst.

You’ve learned how to use Python, R, SQL, and the statistical skills needed to get started as a data analyst—so, what’s next? Effective Data Analysis bridges the gap between foundational skills and real-world application. This book provides clear, actionable guidance on transforming business questions into impactful data projects, ensuring you’re tracking the right metrics, and equipping you with a modern data analyst’s essential toolbox.

In Effective Data Analysis, you’ll gain the skills needed to excel as a data analyst, including:

• Maximizing the impact of your analytics projects and deliverables
• Identifying and leveraging data sources to enhance organizational insights
• Mastering statistical tests, understanding their strengths, limitations, and when to use them
• Overcoming the challenges and caveats at every stage of an analytics project
• Applying your expertise across a variety of domains with confidence

Effective Data Analysis is full of sage advice on how to be an effective data analyst in a real production environment. Inside, you’ll find methods that enhance the value of your work—from choosing the right analysis approach, to developing a data-informed organizational culture.

Foreword by Barry McCardel.

About the technology

Data analysts need top-notch knowledge of statistics and programming. They also need to manage clueless stakeholders, navigate messy problems, and advocate for resources. This unique book covers the essential technical topics and soft skills you need to be effective in the real world.

About the book

Effective Data Analysis helps you lock down those skills along with unfiltered insight into what the job really looks like. You’ll build out your technical toolbox with tips for defining metrics, testing code, automation, sourcing data, and more. Along the way, you’ll learn to handle the human side of data analysis, including how to turn vague requirements into efficient data pipelines. And you’re sure to love author Mona Khalil’s illustrations, industry examples, and a friendly writing style.

What's inside

• Identify and incorporate external data
• Communicate with non-technical stakeholders
• Apply and interpret statistical tests
• Techniques to approach any business problem

About the reader

Written for early-career data analysts, but useful for all.

About the author

Mona Khalil is the Senior Manager of Analytics Engineering at Justworks.

Table of Contents

Part 1
1 What does an analyst do?
2 From question to deliverable
3 Testing and evaluating hypotheses
Part 2
4 Statistics you (probably) learned: T-tests, ANOVAs, and correlations
5 Statistics you (probably) missed: Non-parametrics and interpretation
6 Are you measuring what you think you’re measuring?
7 The art of metrics: Tracking performance for organizational success
Part 3
8 Navigating sensitive and protected data
9 The world of statistical modeling
10 Incorporating external data into analyses
11 The magic of well-structured data
12 Tools and tech for modern data analytics

Mona Khalil is a Data Science Manager at Greenhouse Software. Mona holds a degree in psychology from Fordham University and statistics at Baruch College, as well as having a decade of experience working with analytics and data science. Mona has worked with cross-functional teams in a variety of industries, including government, education, and HR technology.