Data Science Career

In the age of Big Data we often believe that our predictions about the future are better than ever before. But as risk expert Gerd Gigerenzer shows, the surprising truth is that in the real world, we often get better results by using simple rules and considering less information. In Risk Savvy, Gigerenzer reveals that most of us, including doctors, lawyers, financial advisers, and elected officials, misunderstand statistics much more often than we think, leaving us not only misinformed, but vulnerable to exploitation.

Optimization is the driving principle of our modern world. We now can manufacture, transport, and organize things more cheaply and faster than ever. Optimized models underlie everything from airline schedules to dating site matches. We strive for efficiency in our daily lives, obsessed with productivity and optimal performance. How did a mathematical concept take on such outsize cultural shape? And what is lost when efficiency is gained? Optimal Illusions traces the fascinating history of optimization from its roots in America’s founding principles to its modern manifestations.

As tech products become more prevalent today, the demand for machine learning professionals continues to grow. But the responsibilities and skill sets required of ML professionals still vary drastically from company to company, making the interview process difficult to predict. In this guide, data science leader Susan Shu Chang shows you how to tackle the ML hiring process. She'll take you through the highly selective recruitment process by sharing hard-won lessons she learned along the way.

If you’re considering a shift to data analysis, you’re likely exploring various programming languages to kickstart your journey. Take a cue from industry giants like Netflix, Facebook, and Amazon, who rely on SQL for data analysis. Don’t let misconceptions hinder your data analysis journey. Equip yourself with this comprehensive guide and master SQL to unlock new opportunities.

Data. It's the benchmark that informs corporate projections, decision-making, and analysis. But, why do many organizations that see themselves as data-driven fail to thrive? In Leading with AI and Analytics, two renowned experts from the Kellogg School of Management show business leaders how to transform their organization to become evidence-driven, which leads to real, measurable changes that can help propel their companies to the top of their industries.

While everyone is talking about "big data", the truth is that understanding the "little data" - the stats that underlie newspaper headlines, stock reports, weather forecasts, and so on - is what helps you make smarter decisions at work, at home, and in every aspect of your life. The average person consumes approximately 30 gigabytes of data every single day but has no idea how to interpret it correctly. Everydata explains, through the eyes of an expert economist and statistician, how to decipher the small bytes of data we consume in a day.

What To Do When Machines Do Everything is a guidebook to succeeding in the next generation of the digital economy. When systems running on Artificial Intelligence can drive our cars, diagnose medical patients, and manage our finances more effectively than humans it raises profound questions on the future of work and how companies compete. Drawing upon real-world cases, data, and insight, the authors provide clear strategic guidance and actionable steps to help you and your organization move ahead in a world where exponentially developing new technologies are changing how value is created.

One of the most common questions asked to me still remains same - How can I become an Oracle DBA! After replying to so many emails and helping students over calls, I decided to create this eBook on everything you must know in order to become Oracle DBA. By the end of this eBook, you will be able to: Get to know everything you need to become an Oracle DBA Download fresher DBA checklist to accelerate your career Understand the difference between Oracle DBA and Oracle developer Figure out how to find DBA jobs as a fresher and where to start hunting Know the different types of DBAs and what are...

It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets.

Cómo tomar decisiones estratégicas a partir de la analítica de datos. "¿Cuál es la función de los datos, entonces, y de su análisis profundo y racional antes de tomar una decisión en una empresa o en una organización?... ¿Quiénes toman las decisiones en una empresa? ¿Qué decisiones toman?", se pregunta el autor de este libro que invita a líderes de equipos de trabajo a pensar en un término muy presente hoy: la data. Y, sobre todo, qué hacer con ella.

Melding the long-term history of technology with the latest headlines and findings of computer science and social science, The Efficiency Paradox questions our ingrained assumptions about efficiency, persuasively showing how relying on the algorithms of digital platforms can in fact lead to wasted efforts, missed opportunities, and above all an inability to break out of established patterns. Edward Tenner offers a smarter way of thinking about efficiency, revealing what we and our institutions can learn from the random and unexpected.

When you're pursuing a career in a new field, or just improving your skills in hopes of finding a better job, you're going to ask yourself this question: Should I teach myself, or do online courses? In the fields of Data Science, Data Analytics, and Data Engineering, both are viable options, but you need to choose which is right for you. This strategy guide will help you decide whether online learning or the self-teaching is best for your goals, and you'll be set up for success. Don't sign up for a course or hit the books until you read this guide!

Interested in the field of data, but not sure where to start? Wondering what the difference between a Data Scientist, Data Analyst, and Data Engineer is? Look no further- "Discover Your Path in Data" is the top strategy guide for determining which career path is best for you based on your interests and strong skills. Whether you're a new grad or looking for a career change, this guide will help you find your path in data and set you up for success in the long term.

Marcin Zukowski, Thierry Cruanes, and Benoit Dageville had a mission. As Snowflake’s founders, they wanted to make all types of data available to anybody with permission to use it via a simple and inexpensive mechanism. Their collective efforts drove the breakthrough creation of the Data Cloud. Snowflake’s Data Cloud platform ingests available data and transforms it into information. Without the ability to effectively process, data is just a collection of raw facts.

Two books in one - use the power of analytics and probability to discover what your target customers are thinking and how to use that as an edge in identifying solutions and strategies to help you move forward. Includes: Analytics and Probability.

By one expert's prediction, within 20 years half of Americans will have body implants that tell retailers how they feel about specific products as they browse their local stores. The notion may be outlandish, but it reflects executives' drive to understand shoppers in the aisles with the same obsessive detail that they track us online. In fact, a hidden surveillance revolution is already taking place inside brick-and-mortar stores, where Americans still do most of their buying.

When author Kate Strachnyi wanted to learn more about data science, she went straight to the source. In a series of more than twenty interviews, she asks leading data scientists questions about starting in the field and the future of the industry. With their stories, learn about the many different positions available for data scientists, the criteria recruiters look for when hiring, the best options for building your portfolio, the recruitment and interviewing process, the typical workday for a data scientist, the changing industry and its impact on other industries, the wide variety of ...

In this book, you’ll find methods and techniques to organize your data, extract the information, and analyze it. There’s a way to make sure your company is going in the right direction, and this audiobook will reveal the secret behind a successful business.

When it comes to making big decisions, we might consult friends and family, read advice online or turn to self-help books for guidance. In the end, though, we usually just do what feels right. But what if our gut is wrong? As economist and former Google data scientist Seth Stephens-Davidowitz argues, our gut is actually not that reliable—and data can prove this. In Don’t Trust Your Gut, he unearths the startling conclusions that the right data can teach us about who we are and what will make our lives better.

Have you logged into Facebook recently? Searched for something on Google? Chosen a movie on Netflix? If so, you've probably been an unwitting participant in a variety of experiments - also known as randomized controlled trials - designed to test the impact of different online experiences. In this book, Michael Luca and Max Bazerman explain the importance of experiments for decision making in a data-driven world.