Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
Our Courses
Serverless Data Processing with Dataflow: Develop Pipelines en Español
En esta segunda parte de la serie de cursos sobre Dataflow, analizaremos en profundidad el desarrollo de canalizaciones con el SDK de Beam. Comenzaremos con un repaso de los conceptos de Apache Beam. A continuación, analizaremos el procesamiento de datos de transmisión con ventanas, marcas de agua y activadores. Luego, revisaremos las opciones de fuentes y receptores en sus canalizaciones, los esquemas para expresar datos estructurados y cómo realizar transformaciones con estado mediante las API de State y de Timer.
-
Course by
-
Self Paced
-
Spanish
GPU Programming
This specialization is intended for data scientists and software developers to create software that uses commonly available hardware. Students will be introduced to CUDA and libraries that allow for performing numerous computations in parallel and rapidly. Applications for these skills are machine learning, image/audio signal processing, and data processing.
-
Course by
-
Self Paced
-
English
Serverless Data Processing Dataflow em Português Brasileiro
Está se tornando cada vez mais difícil manter uma pilha de tecnologia que possa acompanhar as crescentes demandas de um negócio orientado a dados. Todo praticante de Big Data está familiarizado com os três V’s do Big Data: volume, velocidade e variedade. E se houvesse uma tecnologia à prova de escala projetada para atender a essas demandas? Entre no Google Cloud Dataflow. O Google Cloud Dataflow simplifica o processamento de dados unificando o processamento em lote e fluxo e fornecendo uma experiência sem servidor que permite que os usuários se concentrem na análise, não na infraestrutura.
-
Course by
-
Self Paced
-
Portuguese
用Python玩转数据 Data Processing Using Python
本课程 (Please click https://www.coursera.org/learn/python-data-processing for English version) 主要面向非计算机专业学生，从Python基本语法开始，到Python中如何从本地和网络上进行数据获取，如何解析和表示数据，再到如何利用Python开源生态系统SciPy对数据进行基础和高级的统计分析及可视化，包括数据探索和预处理的具体方法，到最后如何设计一个简单的GUI界面来表示和处理数据，层层推进。 整个课程以财经数据为基础，通过构建一个个喜闻乐见的案例，让大家可以以更直观的方式领略Python的简洁、优雅和健壮，同时探讨Python除了在商业领域之外在文学、社会学和新闻等人文社科类领域以及在数学和生物等理工类领域同样拥有便捷高效的数据处理能力，并可以触类旁通将其灵活应用于各专业中。 近期（2019年11月6日已更新完毕）本课程进行了全面改版，新版主要在以下几个方面做了改变： 1. 丰富了Python基础的案例实际操作和讲解； 2. 增加和扩展了如NumPy包的矢量运算和广播思想及常见应用，数据探索与预处理的多个环节，基于pandas的数据分析及数据挖掘案例等。
-
Course by
-
Self Paced
-
Chinese
Big Data Analysis Deep Dive
The job market for architects, engineers, and analytics professionals with Big Data expertise continues to increase. The Academy’s Big Data Career path focuses on the fundamental tools and techniques needed to pursue a career in Big Data. This course includes: data processing with python, writing and reading SQL queries, transmitting data with MaxCompute, analyzing data with Quick BI, using Hive, Hadoop, and spark on E-MapReduce, and how to visualize data with data dashboards.
-
Course by
-
Self Paced
-
14 hours
-
English
Social Media Data Analytics
Learner Outcomes: After taking this course, you will be able to: - Utilize various Application Programming Interface (API) services to collect data from different social media sources such as YouTube, Twitter, and Flickr. - Process the collected data - primarily structured - using methods involving correlation, regression, and classification to derive insights about the sources and people who generated that data. - Analyze unstructured data - primarily textual comments - for sentiments expressed in them. - Use different tools for collecting, analyzing, and exploring social media data for resea
-
Course by
-
13 hours
-
English
Advanced Data Science with IBM
As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You'll un…
-
Course by
-
Self Paced
-
English
Data Processing using Python Collections
By the end of this project you will use the Python Collections Counter, the CSV package's DictReader, and the Collections UserList to read student test data and find the most common test scores.
-
Course by
-
Self Paced
-
3 hours
-
English
NoSQL, Big Data, and Spark Foundations
Big Data Engineers and professionals with NoSQL skills are highly sought after in the data management industry. This Specialization is designed for those seeking to develop fundamental skills for working with Big Data, Apache Spark, and NoSQL databases.
-
Course by
-
Self Paced
-
English
Data Processing with Azure
This Azure training course is designed to equip students with the knowledge need to process, store and analyze data for making informed business decisions. Through this Azure course, the student will understand what big data is along with the importance of big data analytics, which will improve the students mathematical and programming skills. Students will learn the most effective method of using essential analytical tools such as Python, R, and Apache Spark.
-
Course by
-
Self Paced
-
13 hours
-
English
Data Structures and Algorithms
This course explores data structures and algorithms for back-end development, focusing on performance and scalability.
-
Course by
-
Self Paced
-
English
Vertex AI: Qwik Start
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will use BigQuery for data processing and exploratory data analysis, and the Vertex AI platform to train and deploy a custom TensorFlow Regressor model to predict customer lifetime value (CLV). The goal of the lab is to introduce to Vertex AI through a high value real world use case - predictive CLV. Starting with a local BigQuery and TensorFlow workflow, you will progress toward training and deploying your model in the cloud with Vertex AI.
-
Course by
-
Self Paced
-
2 hours
-
English
I/O-efficient algorithms
I/O-efficient algorithms, also known as external memory algorithms or cache-oblivious algorithms, are a class of algorithms designed to efficiently process data that is too large to fit entirely in the main memory (RAM) of a computer. These algorithms are particularly useful when dealing with massive datasets, such as those found in large-scale data processing, database management, and file systems. Operations on data become more expensive when the data item is located higher in the memory hierarchy.
-
Course by
-
Self Paced
-
10 hours
-
English
Data Processing and Manipulation
The "Data Processing and Manipulation" course provides students with a comprehensive understanding of various data processing and manipulation concepts and tools. Participants will learn how to handle missing values, detect outliers, perform sampling and dimension reduction, apply scaling and discretization techniques, and explore data cube and pivot table operations. This course equips students with essential skills for efficiently preparing and transforming data for analysis and decision-making. Learning Objectives: 1.
-
Course by
-
Self Paced
-
29 hours
-
English
Serverless Data Processing with Dataflow
It is becoming harder and harder to maintain a technology stack that can keep up with the growing demands of a data-driven business. Every Big Data practitioner is familiar with the three V’s of Big Data: volume, velocity, and variety. What if there was a scale-proof technology that was designed to meet these demands? Enter Google Cloud Dataflow. Google Cloud Dataflow simplifies data processing by unifying batch & stream processing and providing a serverless experience that allows users to focus on analytics, not infrastructure.
-
Course by
-
Self Paced
-
English
Preparing for Google Cloud Certification: Cloud Data Engineer
This program provides the skills you need to advance your career in data engineering and provides a pathway to earn the industry-recognized Google Cloud Professional Data Engineer certification.
-
Course by
-
Self Paced
-
English
How Computers Work: Demystifying Computation
Explore the fundamentals of computing: computer architecture, binary logic, data processing, circuits & more.
-
Course by
-
Self Paced
-
12
-
English
Introduction to Data Engineering
Start your journey in one of the fastest growing professions today with this beginner-friendly Data Engineering course! You will be introduced to the core concepts, processes, and tools you need to know in order to get a foundational knowledge of data engineering. as well as the roles that Data Engineers, Data Scientists, and Data Analysts play in the ecosystem. You will begin this course by understanding what is data engineering as well as the roles that Data Engineers, Data Scientists, and Data Analysts play in this exciting field.
-
Course by
-
Self Paced
-
13 hours
-
English
Predictive Modeling and Machine Learning with MATLAB
In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB and Data Processing and Feature Engineering with MATLAB to increase your ability to harness the power of MATLAB to analyze data relevant to the work you do. These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background.
-
Course by
-
Self Paced
-
22 hours
-
English
Introduction to Big Data with Spark and Hadoop
This self-paced IBM course will teach you all about big data! You will become familiar with the characteristics of big data and its application in big data analytics. You will also gain hands-on experience with big data processing tools like Apache Hadoop and Apache Spark. Bernard Marr defines big data as the digital trace that we are generating in this digital era. You will start the course by understanding what big data is and exploring how insights from big data can be harnessed for a variety of use cases.
-
Course by
-
Self Paced
-
24 hours
-
English
Introduction to Relational Databases (RDBMS)
Are you ready to dive into the world of data engineering? In this beginner level course, you will gain a solid understanding of how data is stored, processed, and accessed in relational databases (RDBMSes). You will work with different types of databases that are appropriate for various data processing requirements. You will begin this course by being introduced to relational database concepts, as well as several industry standard relational databases, including IBM DB2, MySQL, and PostgreSQL.
-
Course by
-
Self Paced
-
19 hours
-
English
Building Batch Data Pipelines on Google Cloud
Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.
-
Course by
-
Self Paced
-
17 hours
-
English
Data Processing and Feature Engineering with MATLAB
In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB to lay the foundation required for predictive modeling. This intermediate-level course is useful to anyone who needs to combine data from multiple sources or times and has an interest in modeling. These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background.
-
Course by
-
Self Paced
-
20 hours
-
English
Basic Data Processing and Visualization
This is the first course in the four-course specialization Python Data Products for Predictive Analytics, introducing the basics of reading and manipulating datasets in Python. In this course, you will learn what a data product is and go through several Python libraries to perform data retrieval, processing, and visualization. This course will introduce you to the field of data science and prepare you for the next three courses in the Specialization: Design Thinking and Predictive Analytics for Data Products, Meaningful Predictive Modeling, and Deploying Machine Learning Models.
-
Course by
-
Self Paced
-
11 hours
-
English
Introduction to Business Analytics with R
Nearly every aspect of business is affected by data analytics. For businesses to capitalize on data analytics, they need leaders who understand the business analytic workflow. This course addresses the human skills gap by providing a foundational set of data processing skills that can be applied to many business settings. In this course you will use a data analytic language, R, to efficiently prepare business data for analytic tools such as algorithms and visualizations.
-
Course by
-
Self Paced
-
17 hours
-
English