Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales. In this course, you will learn the landscape of relevant systems, the principles on which they rely, their tradeoffs, and how to evaluate their utility against your requirements. You will learn how practical systems were derived from the frontier of research in computer science and what systems are coming on the horizon. Cloud computing, SQL and NoSQL databases, MapReduce and the ecosystem it spawned, Spark and its contemporaries, and specialized systems for graphs and arrays will be covered. You will also learn the history and context of data science, the skills, challenges, and methodologies the term implies, and how to structure a data science project. At the end of this course, you will be able to: Learning Goals: 1. Describe common patterns, challenges, and approaches associated with data science projects, and what makes them different from projects in related fields. 2. Identify and use the programming models associated with scalable data manipulation, including relational algebra, mapreduce, and other data flow models. 3. Use database technology adapted for large-scale analytics, including the concepts driving parallel databases, parallel query processing, and in-database analytics 4. Evaluate key-value stores and NoSQL systems, describe their tradeoffs with comparable systems, the details of important examples in the space, and future trends. 5. “Think” in MapReduce to effectively write algorithms for systems including Hadoop and Spark. You will understand their limitations, design details, their relationship to databases, and their associated ecosystem of algorithms, extensions, and languages. write programs in Spark 6. Describe the landscape of specialized Big Data systems for graphs, arrays, and streams

Understand the terminology and recurring principles associated with data science, and understand the structure of data science projects and emerging methodologies to approach them. Why does this emerging field exist? How does it relate to other fields? How does this course distinguish itself? What do data science projects look like, and how should they be approached? What are some examples of data science projects?

Relational Databases are the workhouse of large-scale data management. Although originally motivated by problems in enterprise operations, they have proven remarkably capable for analytics as well. But most importantly, the principles underlying relational databases are universal in managing, manipulating, and analyzing data at scale. Even as the landscape of large-scale data systems has expanded dramatically in the last decade, relational models and languages have remained a unifying concept. For working with large-scale data, there is no more important programming model to learn.

The MapReduce programming model (as distinct from its implementations) was proposed as a simplifying abstraction for parallel manipulation of massive datasets, and remains an important concept to know when using and evaluating modern big data platforms.

NoSQL systems are purely about scale rather than analytics, and are arguably less relevant for the practicing data scientist. However, they occupy an important place in many practical big data platform architectures, and data scientists need to understand their limitations and strengths to use them effectively.

Graph-structured data are increasingly common in data science contexts due to their ubiquity in modeling the communication between entities: people (social networks), computers (Internet communication), cities and countries (transportation networks), or corporations (financial transactions). Learn the common algorithms for extracting information from graph data and how to scale them up.

Dates:

- Free schedule

Included in selections:

Модели и методы анализа проектных решений

4 курс МИЭМ ВШЭ, 6 кредитов.

4 курс МИЭМ ВШЭ, 6 кредитов.

More on this topic:

Data Manipulation at Scale: Systems and Algorithms

Data analysis has replaced data acquisition as the bottleneck to evidence-based...

Data analysis has replaced data acquisition as the bottleneck to evidence-based...

More from 'Mathematics, Statistics and Data Analysis':

Digital Analytics Fundamentals

This three-week course provides a foundation for marketers and analysts seeking...

This three-week course provides a foundation for marketers and analysts seeking...

Model-Based Automotive Systems Engineering

Learn how to model and simulate system dynamics in automotive engineering Modeling...

Learn how to model and simulate system dynamics in automotive engineering Modeling...

Calculus 1B: Integration

Discover the integral—what it is and how to compute it. See how to use...

Discover the integral—what it is and how to compute it. See how to use...

Maths Essentials

Discover and acquire the fundamental maths skills that you will need to use...

Discover and acquire the fundamental maths skills that you will need to use...

Data Analysis Essentials

Discover and acquire the quantitative data analysis skills that you will typically...

Discover and acquire the quantitative data analysis skills that you will typically...

More from 'Coursera':

First Year Teaching (Secondary Grades) - Success from the Start

Success with your students starts on Day 1. Learn from NTC's 25 years developing...

Success with your students starts on Day 1. Learn from NTC's 25 years developing...

Understanding 9/11: Why Did al Qai’da Attack America?

This course will explore the forces that led to the 9/11 attacks and the policies...

This course will explore the forces that led to the 9/11 attacks and the policies...

Aboriginal Worldviews and Education

This course will explore indigenous ways of knowing and how this knowledge can...

This course will explore indigenous ways of knowing and how this knowledge can...

Analytic Combinatorics

Analytic Combinatorics teaches a calculus that enables precise quantitative...

Analytic Combinatorics teaches a calculus that enables precise quantitative...

Accountable Talk®: Conversation that Works

Designed for teachers and learners in every setting - in school and out, in...

Designed for teachers and learners in every setting - in school and out, in...

© 2013-2019