Data Scientist II | Microsoft | 7,026 reviews
Local Jobs Microsoft posted 5 days ago in Data Scientist ShortlistJob Description
Responsibilities
Qualifications
Required Qualifications:
- PhD (completed or close to completion) in Computer Science or related field
Preferred Qualifications:
- Demonstrate ability to work in a multi-disciplinary team.
- Specialization on graph machine learning
- Research ability demonstrated by journal and conference publications and/or preprints.
- Experience developing and deploying analytics at scale in distributed application frameworks such as Apache Spark.
Data Science Courses
Data Scientist Masters Program
Data Science Masters Program makes you proficient in tools and systems used by Data Science Professionals. It includes training on Statistics, Data Science, Python, Apache Spark & Scala, Tensorflow and Tableau. The curriculum has been determined by extensive research on 5000+ job descriptions across the globe.
Data Science for Everyone
What is data science, why is it so popular, and why did the Harvard Business Review hail it as the “sexiest job of the 21st century”? In this non-technical course, you’ll be introduced to everything you were ever too afraid to ask about this fast-growing and exciting field, without needing to write a single line of code. Through hands-on exercises, you’ll learn about the different data scientist roles, foundational topics like A/B testing, time series analysis, and machine learning, and how data scientists extract knowledge and insights from real-world data. So don’t be put off by the buzzwords. Start learning, gain skills in this hugely in-demand field, and discover why data science is for everyone!
Data Science for Business
What is data science and how can you use it to strengthen your organization? This course will teach you about the skills you need on your data team, and how you can structure that team to meet your organization’s needs. Data is everywhere! This course will provide you with an understanding of data sources your company can use and how to store that data. You’ll also discover ways to analyze and visualize your data through dashboards and A/B tests. To wrap up the course, we’ll discuss exciting topics in machine learning, including clustering, time series prediction, natural language processing (NLP), deep learning, and explainable AI! Along the way, you’ll learn about a variety of real-world applications of data science and gain a better understanding of these concepts through practical exercises.
Data Science Books
Cloud Computing Architecture: Technologies and Practice (Advances in Computer Science)
The book begins by establishing the concept of cloud computing and describing the technological trends, and then discusses cloud computing architecture connotation and key technologies such as computing, storage, network, data, management, access, and security. With abundant project experiences and applications, the book is an essential reference for researchers and industrial engineers in computer science and information management.ed manner
Big Data on Campus: Data Analytics and Decision Making in Higher Education
The continuing importance of data analytics is not lost on higher education leaders, who face a multitude of challenges, including increasing operating costs, dwindling state support, limits to tuition increases, and increased competition from the for-profit sector. To navigate these challenges, savvy leaders must leverage data to make sound decisions. In Big Data on Campus, leading data analytics experts and higher ed leaders show the role that analytics can play in the better administration of colleges and universities.
Big Data with Hadoop MapReduce: A Classroom Approach
The authors of Big Data with Hadoop MapReduce: A Classroom Approach have framed the book to facilitate understanding big data and MapReduce by visualizing the basic terminologies and concepts. They employed over 100 illustrations and many worked-out examples to convey the concepts and methods used in big data, the inner workings of MapReduce, and single node/multi-node installation on physical/virtual machines.
Customer Data and Privacy: The Insights You Need from Harvard Business Review (HBR Insights Series)
Is it possible to take advantage of the benefits of data collection–and mitigate risks–for both companies and customers?
Most consumers are not very skilled at evaluating privacy risks; they’re either unable to determine the cost of sharing personal data online or unaware of what they’re sharing. (Doesn’t everyone scroll down without reading to click “I accept”?) Without much intervention from most federal or state-level governments, companies are on their own to define what qualifies as reasonable use. In today’s digital surveillance economy, there are no clear-cut best practices or guidelines. Gathering and using information can help customers–we see that in personalization and autofill of online forms. But companies must act in the best interest of their customers and treat the sensitive information users give them with the ethical care of doctors, lawyers, and financial advisers. The challenges of operating in a digital ecosystem aren’t going away. Customer Data and Privacy: The Insights You Need from Harvard Business Review will help you understand the tangled interdependencies and complexities and develop strategies that allow your company to be good stewards, collecting, using, and storing customer data responsibly.
