This is to help those in the below listed certificate tracks find research resources.
Business Information Systems and Technology 4.0: New Trends in the Age of Digital Change by 
Fundamentals of Data Analytics: With a View to Machine Learning by 
Principles of Data Wrangling by
A key task that any aspiring data-driven organization needs to learn is data wrangling, the process of converting raw data into something truly useful. This practical guide provides business analysts with an overview of various data wrangling techniques and tools, and puts the practice of data wrangling into context by asking, "What are you trying to do and why?" Wrangling data consumes roughly 50-80% of an analyst's time before any kind of analysis is possible. Written by key executives at Trifacta, this book walks you through the wrangling process by exploring several factors--time, granularity, scope, and structure--that you need to consider as you begin to work with data. You'll learn a shared language and a comprehensive understanding of data wrangling, with an emphasis on recent agile analytic processes used by many of today's data-driven organizations. Appreciate the importance--and the satisfaction--of wrangling data the right way. Understand what kind of data is available Choose which data to use and at what level of detail Meaningfully combine multiple sources of data Decide how to distill the results to a size and shape that can drive downstream analysis
Microsoft Excel Functions Quick Reference by
This condensed syntax reference presents the essential Excel function syntax in a well-organized format that can be used as a quick and handy reference. You can use it to improve your Excel knowledge and increase your productivity. It will help you upgrade the quality of your data analysis, dashboards, models, and templates. The Microsoft Excel Functions Quick Reference helps you set up workbooks, enter data, and format it for easier viewing. It starts by giving an overview of Excel functions explaining the different types of Excel functions available followed by an understanding of string functions and date functions. It then covers time, lookup, aggregate, and logical functions along with practice problems. Further, you will see math functions and information functions in Excel. You will also be able to create sophisticated forecast worksheets, key performance indicators (KPIs), and timelines. Each function in the text is illustrated by helpful, illuminating examples. With this book by your side, you'll always have the answer to your Excel function syntax questions. What You Will Learn Work with basic Excel functions Use the LOOKUP function Take advantage of new functions in information functions Create a mega formula Who This Book Is For Administrators, analysts, and anyone else working with Microsoft Excel.
Python Data Science Handbook by
Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all--IPython, NumPy, pandas, Matplotlib, scikit-learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you'll learn how: IPython and Jupyter provide computational environments for scientists using Python NumPy includes the ndarray for efficient storage and manipulation of dense data arrays Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data Matplotlib includes capabilities for a flexible range of data visualizations Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms
Big Data Science in Finance by
Explains the mathematics, theory, and methods of Big Data as applied to finance and investing Data science has fundamentally changed Wall Street--applied mathematics and software code are increasingly driving finance and investment-decision tools. Big Data Science in Finance examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematically-advanced students and discerning financial practitioners alike, this energizing book presents new, cutting-edge content based on world-class research taught in the leading Financial Mathematics and Engineering programs in the world. Marco Avellaneda, a leader in quantitative finance, and quantitative methodology author Irene Aldridge help readers harness the power of Big Data. Comprehensive in scope, this book offers in-depth instruction on how to separate signal from noise, how to deal with missing data values, and how to utilize Big Data techniques in decision-making. Key topics include data clustering, data storage optimization, Big Data dynamics, Monte Carlo methods and their applications in Big Data analysis, and more. This valuable book: Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samples Explains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) Covers vital topics in the field in a clear, straightforward manner Compares, contrasts, and discusses Big Data and Small Data Includes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slides Big Data Science in Finance: Mathematics and Applications is an important, up-to-date resource for students in economics, econometrics, finance, applied mathematics, industrial engineering, and business courses, and for investment managers, quantitative traders, risk and portfolio managers, and other financial practitioners.
Digital Humans: Thriving in an Online World by
Embrace the Human Side of Organisational Digital Transformation Digital Humans: Thriving in an Online World is an insightful, engaging and interdisciplinary discussion of how best to transform your organisation into a nimble, digital enterprise with human beings firmly established at the centre of it. The authors draw on complexity theory, anthropology, history, organisational transformation and behavioural science to demonstrate the characteristics that define successful digital organisations. You'll discover the importance of focusing on human beings even as you make the shift to digital and learn to understand the importance of our new digital ecosystems. Illuminating case studies and examples of organisations that have successfully made the jump to digital are explored and the book presents new and effective ways to make strategic decisions about your company's future based on our new physical-digital hybrid reality. A can't-miss blueprint to a market environment and world that's increasingly fast-moving, complex and rewarding, Digital Humans will find a place in the libraries of managers, executives, and business leaders looking for an engaging roadmap to digital transformation that wouldn't have us leave our humanity behind.
