Learn the technical and soft skills you need to succeed in your career as a data analyst. In Effective Data Analysis you'll learn skills for succeeding at data analysis including: - Maximizing the value of your analytics projects and deliverables
- Identifying data sources that enhance your organization's insights
- Understanding statistical tests, their strengths, limitations, and appropriate usage
- Navigating the caveats and challenges of every stage of an analytics project
- Applying your new skills across diverse domains 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 maximize the impact of your work, from choosing the right analysis approach to effectively communicating with stakeholders. You'll soon understand the nuances and challenges of real data science projects, with the kind of insights that only come from years of experience. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Without doubt, technical skills in Python, R, SQL, along with knowledge of statistics and data science are vital to your success as an analyst. However, they're only part of the picture. This one-of-a-kind guide reveals the soft skills, best practices, and tools that help you maximize your effectiveness and deliver accurate data-driven decisions in your organization. About the book Effective Data Analysis teaches you to deliver productive data science in business and research. It assumes you've mastered the basics and supports you with best practices normally learned through trial-and-error or careful mentorship. Author Mona Khalil shares her expertise through visuals, cartoons, examples from across industries, and even a few laugh-out-loud jokes. You'll start with asking the right questions of your stakeholders and turning often-vague requirements into realistic data pipelines. Once you've mastered the people skills, you'll move on to the technical bits--including defining your metrics, testing, and more. Build out your analyst's toolbox with techniques for statistical modeling, sourcing your data, automation, and more. Finally, finish up with realistic advice on developing a data-informed organizational culture that will ensure your skills are delivering to their full potential. About the reader For early-career data analysts who want to enhance their technical knowledge with industry insights. About the author 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.
- Identifying data sources that enhance your organization's insights
- Understanding statistical tests, their strengths, limitations, and appropriate usage
- Navigating the caveats and challenges of every stage of an analytics project
- Applying your new skills across diverse domains 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 maximize the impact of your work, from choosing the right analysis approach to effectively communicating with stakeholders. You'll soon understand the nuances and challenges of real data science projects, with the kind of insights that only come from years of experience. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Without doubt, technical skills in Python, R, SQL, along with knowledge of statistics and data science are vital to your success as an analyst. However, they're only part of the picture. This one-of-a-kind guide reveals the soft skills, best practices, and tools that help you maximize your effectiveness and deliver accurate data-driven decisions in your organization. About the book Effective Data Analysis teaches you to deliver productive data science in business and research. It assumes you've mastered the basics and supports you with best practices normally learned through trial-and-error or careful mentorship. Author Mona Khalil shares her expertise through visuals, cartoons, examples from across industries, and even a few laugh-out-loud jokes. You'll start with asking the right questions of your stakeholders and turning often-vague requirements into realistic data pipelines. Once you've mastered the people skills, you'll move on to the technical bits--including defining your metrics, testing, and more. Build out your analyst's toolbox with techniques for statistical modeling, sourcing your data, automation, and more. Finally, finish up with realistic advice on developing a data-informed organizational culture that will ensure your skills are delivering to their full potential. About the reader For early-career data analysts who want to enhance their technical knowledge with industry insights. About the author 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.
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