Upon completing the course, you will be able to:
Gaining solid understanding of the Big Data ecosystem and various real-world use cases
Launching and setting up Hadoop clusters in various environments: AWS EMR, Hortonworks, Cloudera, VM
ETL and querying large datasets with Apache Hive/Pig as well as SQL on Hadoop tools such as Presto, Impala and Phoenix
Ingesting data into NoSQL databases such as MongoDB, DynamoDB, Cassandra and HBase
Data manipulation for machine learning with Spark SQL, Dataframe and Dataset
Developing machine learning models with Spark ML/MLlib
Deploying machine learning models for real-time analytics with Apache Kafka and Spark Streaming
It is hard, but you can make it. Plus, we're here to help.
Chief Instructor & Cofounder
A self-trained data scientist and an expert in applied big data technologies, Shaohua has over ten years of experience in applied data science and has built a reputation for building high-performance data science teams. He is currently the cofounder of two startup companies in data field, WeCloudData and iFuture. Prior to his own business, he worked as a senior data scientist at Kik Interactive Inc., helped the billion-dollar Canadian tech unicorn grow its big data initiative. Before Kik, he built a high-performance data science team at BlackBerry that focused on building innovative data science solutions for marketing, CRM, and product teams. He specializes in user interest graph modelling, targeted advertising, scalable location intelligence, and large-scale recommendation engines for mobile personalization. He also collaborated with Ryerson’s Data Science Lab on several big data research projects. In his spare time, Shaohua has helped build the big data course at Ryerson University, where he trained more than 150 professionals on big data technologies such as Hadoop, Spark, and data sciences.
At WeCloudData, we believe that only you fail fast that you could learn faster. WeCloudData’s teaching assistants and program coordinator are committed to provide guidance and support throughout and after the course.