Data Science has taken the world by storm. Organizations are using Data Science to enhance their products, business decisions, and marketing effectiveness. There has been a 29 percent increase in demand for data scientists year over year and a dramatic upswing of 344% increase since 2013. Data Science career opportunities are limitless, and professionals with data expertise will continue to drive the technology revolution. Thinking of ways to get into this field? We have just the right help for you: Data Science Career Guide.
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Table of Contents
Data Science OverviewThis section of the Data Science Career Guide provides a general overview and gives a brief introduction to the field of Data Science.
Data Science Industry UpdateSince data is the omnipresent force ruling our lives now and will be for the foreseeable future, jobs in this domain are booming like never before. And this section of the Data Science Career Guide explains how Data Science is opening up a multitude of job roles and bringing drastic changes, and faster processes across sectors, ranging from the real-estate to entertainment.
Hottest Opportunities in Data ScienceThis section of the Data Science career guide includes top job roles in the field of Data as well as the most in-demand job role, that is, Data Scientist, and the skills needed to become one. From a birds-eye-view, a Data Scientist determines the business questions that need answers, and then comes up with different approaches to try and solve the problem, and gather, merge and analyze data to gain insights, and simplify data problems to develop strategies and predictive models. But is that it? Download the Data Science Career Guide to know the day-to-day challenges and responsibilities of a Data Scientist.
Data Science Learning PathThis section of the Data Science career guide provides a detailed roadmap (including tools and skills) needed to become a Data Scientist. On the other hand, this section also mentions all the top companies that are hiring machine learning pros ranging from Apple to Facebook, and a personalized Data Science learning path to reach your dream organization.
Starting Your JourneyThis section of the Data Science Career Guide provides you several pathways and certifications that will help you either start or fastrack your machine learning career.
The growth of fields like Data Science and AI has increased the demand for talented minds to help solve pressing business challenges faster and better. However, quitting a full-time job to go back to school isn’t realistic for most people, and this is where Simplilearn comes into play. With our highly-detailed course of study, you’ll master everything you need to know to make a splash in these thriving fields.
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Post Graduate Program in Data Science with Purdue UniversityThis Post Graduate Program in Data Science gives you broad exposure to key concepts and tools from Python, R to Machine Learning and more. Hands-on labs and project work bring these concepts to life with our trainers and teaching assistants to guide you along the way. You get to learn Data Science via a comprehensive curriculum covering statistics, key programming languages, Machine Learning algorithms, and more -- with a capstone project culminating your learning experience.
Our Data Science Career Guide will give you insights into the most trending technologies, the top companies that are hiring, the skills required to jumpstart your career in the thriving field. This Data Science Career Guide also offers you a personalized roadmap to becoming a successful Data Science expert.
Download the Data Science Career Guide to explore and step into the exciting world of data, and follow the path towards your dream career.
James CitrinBOOK EXCERPT:
There is no HR-related topic more popular in the business press than performance management (PM). There has been an explosion in writing on this topic in the past 5 years, condemning it as a failure and calling for fundamental change. The vast majority of organizations use the same basic process which I call “Last Generation Performance Management” or PM 1.0 for short. Despite widespread agreement that PM 1.0 is failing, few companies have abandoned it or made fundamental changes to it. While everyone agrees it is broken, few agree on how to fix it. Companies continue to tinker with their systems, making incremental changes every few years with no lasting improvement in effectiveness. Employees continue to achieve amazing things in organizations every day, despite this process not because of it. Nothing has worked because organizations, business leaders and HR professionals focus on PM practices instead of the fundamental purpose of PM and the paradigms, assumptions, and beliefs that underlie the practices. Companies ask their performance management process to do too many things and it fails at all of them as a result. At the foundation of PM 1.0 practices is the ideology of a meritocracy and paradigms rooted in standard economic and psychological theories. While these theories were adequate explanations for motivation and behavior in the 19th and 20th centuries, they fail to account for the increasingly complex nature of organizations and their environments today. Despite the ineffectiveness of PM 1.0, there are powerful forces holding it in place. Information on rigorous, evidence-based recommendations is crowded out by benchmarking information, case studies of high-profile companies, and other propaganda coming from HR think tanks and consultants. Business leaders and HR professionals learn about common practices not effective practices. This book confronts the traditional dogma, paradigms, and practices of PM 1.0 and holds them up to the bright light of scientific scrutiny. It encourages HR professionals and business leaders to abandon PM 1.0 and it offers up a more appropriate purpose for PM, alternative paradigms to guide them and practical solutions that are better supported by scientific research, referred to as “Next Generation Performance Management” or PM 2.0 for short.
Product Details :
|Genre||: Business & Economics|
|Author||: Alan L. Colquitt|
|File||: 235 Pages|
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