Effective Data Analysis: Hard and soft skills (Final Release)КНИГИ » ОС И БД
Название: Effective Data Analysis: Hard and soft skills (Final Release) Автор: Mona Khalil Издательство: Manning Publications Год: 2025 Страниц: 418 Язык: английский Формат: pdf (true) Размер: 40.3 MB
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.
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.
If your team prefers proprietary software, it may still be beneficial to incorporate the use of a language such as R or Python into your workflow. In R, you can access, interact with, and save SPSS, SAS, and STATA files using the haven library or the upload tool available in the RStudio user interface. All of this can also be accomplished in Python using the Pandas library. R is a popular programming language for statistical computing in the data analytics, data science, and research space. Its use compared to Python (discussed next) varies by industry, team area of expertise, seniority level, and type of project. R tends to be more widely used in the biological sciences, social sciences, and statistics. If you work with an organization or academic institution in these areas, you may be more likely to encounter R as the technology of choice in your work or coursework.
Python quickly became the most popular programming language in the data world and is one of the top languages of choice for developers in general. It tends to be most popular among Data Science teams, especially those working with larger data sources and those developing Machine Learning models as part of their workflow. Those with a math, engineering, or physics background may have been exposed to Python during their education. If you expect your work as an analyst will grow to include predictive modeling or are interested in developing a career in Data Science, Machine Learning, or data engineering, Python may be an ideal choice of language for your work.
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.
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