Businesses have come a long way. In the past, market research, understanding the needs of customers and choosing the best marketing strategies was a big challenge. Organizations had to invest a great deal of resources to conduct interviews in the fields, collect data manually then analyze them before they could make crucial management decisions. Currently, research has not changed but the tools used to collect and model the data have improved and created better ways of representing information to the various stakeholders. The internet has been a great game changer and enabler in this age. So what is Data Modelling?
According to IBM, data modeling is the process of creating a visual representation of either a whole information system or parts of it to communicate connections between data points and structures. The goal is to illustrate the types of data used and stored within the system, the relationships among these data types, the ways the data can be grouped and organized and its formats and attributes. It should be noted that Data models are built around business needs that evolve along with changing business needs.
Professionals who design these Data Models are very important in the modern organizations because when done right, such models can be used to make sustainable business decisions based on right information. For all those desiring such a profession, this article looks at some resources you can use to get acquainted with the field as well as deepen their skill in this sphere of expertise.
1. Designing Data-Intensive Applications
Martin is a researcher in distributed systems at the University of Cambridge. Previously he was a software engineer and entrepreneur at Internet companies including LinkedIn and Rapportive, where he worked on large-scale data infrastructure.
Time will come in the professional path of a data engineer when the need for scale erupts and becomes urgent. Being ready for such times is the best path of action and if you want to learn how to make data systems scalable, author Martin has this as a gift fot you. You will then be able to design Data-Intensive Applications that can support web or mobile apps with millions of users.
Software engineers, software architects, and technical managers will find this text especially relevant if they need to make decisions about the architecture of the systems they work on—for example, if they need to choose tools for solving a given problem and figure out how best to apply them.
A sneak peek inside:
- Peer under the hood of the systems you already use, and learn how to use and operate them more effectively
- Make informed decisions by identifying the strengths and weaknesses of different tools
- Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity
- Understand the distributed systems research upon which modern databases are built
- Peek behind the scenes of major online services, and learn from their architectures
If you develop applications that have some kind of server/backend for storing or processing data, and your applications use the internet (e.g., web applications, mobile apps, or internet-connected sensors), then this book is for you. Click below to get it as soon as possible from Amazon.
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
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2. Learning SQL
Whether you will be using a relational database or not, if you are working in data science, business intelligence, software development or some other facet of data analysis, you will likely need to know SQL. Data scientist have to encounter and even us SQL and having a strong background in SQL will boost data modeling skills remarkably.
Author Alan Beaulieu helps developers get up to speed with SQL fundamentals for writing database applications, performing administrative tasks, and generating reports. You will find new chapters on SQL and big data, analytic functions, and working with very large databases.
Each chapter presents a self-contained lesson on a key SQL concept or technique using numerous illustrations and annotated examples. Exercises let you practice the skills you learn. Knowledge of SQL is a must for interacting with data. With Learning SQL, you will quickly discover how to put the power and flexibility of this language to work.
What you will encounter inside:
- Move quickly through SQL basics and several advanced features
- Use SQL data statements to generate, manipulate, and retrieve data
- Create database objects, such as tables, indexes, and constraints with SQL schema statements
- Learn how datasets interact with queries; understand the importance of subqueries
- Convert and manipulate data with SQL’s built-in functions and use conditional logic in data statements
For those who would wish to start with SQL before jumping into Data modeling and design, this is the kind of resource that will serve you well. The author has been designing, building, and implementing custom database applications for over 25 years and is the best teacher to get your SQL fixed. Pick on his brain by clicking on the link provided where you will be able to order your copy from Amazon.
Learning SQL: Generate, Manipulate, and Retrieve Data
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3. Python for Data Analysis
This book is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python.Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. Wes’ goal is to offer a guide to the parts of the Python programming language and its data-oriented library ecosystem and tools that will equip you to become an effective data analyst. It is ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material you will find in the book are available on GitHub.
What you will find inside:
- Use the IPython shell and Jupiter notebook for exploratory computing
- Learn basic and advanced features in NumPy (Numerical Python)
- Get started with data analysis tools in the pandas library
- Use flexible tools to load, clean, transform, merge, and reshape data
- Create informative visualizations with matplotlib
- Apply the pandas group by facility to slice, dice, and summarize datasets
- Analyze and manipulate regular and irregular time series data
- Learn how to solve real-world data analysis problems with thorough, detailed examples.
This hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You will learn the latest versions of pandas, NumPy, IPython, and Jupiter in the process of becoming the best Data modeler and designer. Get this spiced up resource to begin you Data Modelling career with the best.
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
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4. SQL QuickStart Guide
In this age of huge amounts of data, now more than ever there is a burning need to warehouse, access, and understanding the contents of massive databases quickly and efficiently. SQL has proven to be resilient and reliable even as new technologies come and go.
Any database management professional will tell you that despite trendy data management languages that come and go, SQL remains the most widely used and most reliable to date, with no signs of stopping. In this comprehensive guide, experienced mentor and SQL expert Walter Shields draws on his considerable knowledge to make the topic of relational database management accessible, easy to understand, and highly actionable.
In this guide, you will discover:
- The basic structure of databases—what they are, how they work, and how to successfully navigate them
- How to use SQL to retrieve and understand data no matter the scale of a database (aided by numerous images and examples)
- The most important SQL queries, along with how and when to use them for best effect
- Professional applications of SQL and how to “sell” your new SQL skills to your employer, along with other career-enhancing considerations.
No matter who you are, whether a job seeker, manager, beginner, developer or a professional looking to augment your job skills in preparation for a data-driven future, this book will be accessible, easy to read and understand. Take advantage of our inevitably data-driven future with this guide in your hands. Click below to order it today from Amazon.
SQL QuickStart Guide: The Simplified Beginner’s Guide to Managing, Analyzing, and Manipulating Data With SQL
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5. Data Science for Business
Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the “data-analytic thinking” necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.
After reading the book you will not only learn how to improve communication between business stakeholders and data scientists, but also how to participate intelligently in your company’s data science projects.
What you will take home:
- Understand how data science fits in your organization—and how you can use it for competitive advantage
- Treat data as a business asset that requires careful investment if you’re to gain real value
- Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way
- Learn general concepts for actually extracting knowledge from data
- Apply data science principles when interviewing data science job candidates.
A review by Craig Vaughan, Global Vice President at SAP, reads: “A must-read resource for anyone who is serious about embracing the opportunity of big data.” If that is you, the book can be ordered from Amazon below.
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
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6. Data Science from Scratch
Author Joel Grus is a research engineer at the Allen Institute for Artificial Intelligence. Previously he worked as a software engineer at Google and a data scientist at several startups where he built his skillset and expertise. Joel advices that in order to really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them.
This second edition of Data Science from Scratch is updated for Python 3.6, and shows you how the data science tools and algorithms work by implementing them from scratch. Packed with New material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data
- Get a crash course in Python
- Learn the basics of linear algebra, statistics, and probability—and how and when they’re used in data science
- Collect, explore, clean, munge, and manipulate data
- Dive into the fundamentals of machine learning
- Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering
- Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. It is an excellent introduction to key principles in data science that every beginner and intermediate professional will utterly enjoy. Click below to order a copy of Joel’s work and get it delivered from Amazon.
Data Science from Scratch: First Principles with Python
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7. The Art of Statistics
In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us solve some of the most challenging problems we counter in life.
The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems — it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and — perhaps even more importantly — we learn how to responsibly interpret the answers we receive.
Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to stats that every modern person needs. A review from The Evening Standard (UK) reads: “This is an excellent book. Spiegelhalter is great at explaining difficult ideas…Yes, statistics can be difficult. But much less difficult if you read this book“. Order your copy from Amazon below:
The Art of Statistics: How to Learn from Data
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8. The Model Thinker
Organizations all over the world are inundating in data. But as anyone who has ever worked with data before, having it lying in your database, spreadsheet or in a hard drive somewhere does not do anyone any good. We need to find a way of making the data speak up to us.
In The Model Thinker, social scientist Scott E. Page shows us the mathematical, statistical, and computational models (from linear regression to random walks and far beyond) that can turn anyone into a genius. At the core of the book is Scott’s “many-model paradigm,” which shows the reader how to apply multiple models to organize the data, leading to wiser choices, more accurate predictions, and more robust designs.
The Model Thinker provides a toolkit for business people, students, scientists, pollsters, and bloggers to make them better, clearer thinkers, able to leverage data and information to their advantage. You can have this resource delivered from Amazon by visiting the store below. Do not hesitate to take this opportunity to get a hold of data with a whole new perspective.
The Model Thinker: What You Need to Know to Make Data Work for You
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9. Build a Career in Data Science
Building a career as a Data Scientist is quite a young filed and hence troubled by inadequate roadmaps to follow especially for every beginner there. Authors Jacqueline Nolis and Emily Robinson who are senior data scientists present the keys to a data scientist’s long-term success. They advice that blending technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career.
This book as it suggests is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book.
- Creating a portfolio of data science projects
- Assessing and negotiating an offer
- Leaving gracefully and moving up the ladder
- Interviews with professional data scientists
If you would want to begin or advance a data science career, this is the right guide to chew and digest due to the ease of reading coupled with the experience that the authors bring on-board. A copy can be ordered from Amazon below:
Build a Career in Data Science
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10. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
This practical book shows you how to can use simple, efficient tools to implement programs capable of learning from data which accommodates even programmers who know close to nothing about this technology.
By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and Tensor Flow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.
- Explore the machine learning landscape, particularly neural nets
- Use Scikit-Learn to track an example machine-learning project end-to-end
- Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
- Use the Tensor Flow library to build and train neural nets
- Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
- Learn techniques for training and scaling deep neural nets
Whether you are a beginner or know something about Machine Learning, there is no need to fret at all. This book assumes that you know close to nothing about Machine Learning. Its goal is to give you the concepts, tools, and intuition you need to implement programs capable of learning from data. Expert Aurélien Géron will hold your hand and walk you down the learning path with as much ease as possible. Be his student and extract the gems you need. Click below to get started.
Every aspiring data scientist, data designer or any other filed around data requires a great deal of advice in order to succeed. It is an ever changing field and can bring about more confusions than answers. Modern organizations have the need to stay ahead and competitive and as such they are in need of good data models in order to make sustainable business decisions based on right information. For all those desiring such a profession, we hope that the books presented above will serve as a foundation as well as stepping stones to greater heights in your career.
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