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Intermediate Google Cloud For Data Analysts

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Course Summary

Home > Courses >Cloud Computing Training >Google Cloud >Intermediate Google Cloud For Data Analysts

The Intermediate Google Cloud for Data Analysts training course is designed to advance the skills and knowledge of students already familiar data analysis using Google Cloud to the more advanced features and functionality, including predictive, transactional, and large scale distributed data analytics.

This course will start by using the MapReduce-based, batch data analysis tools available as managed infrastructure services on Google Cloud, including Apache Hive, Apache Pig, and PySpark. Next, students will use Apache Beam to analyze both batch and streaming data using a single data pipeline. The course will conclude by preparing students to perform data analysis operations commonly used in predictive analytics and machine learning, including feature creation and feature pre-processing.

Before attending this course, students should take the Google Cloud for Data Analysts course or be familiar with all of the topics listed here: Google Cloud for Data Analysts

Purpose
Learn how to analyze large scale, distributed, and real-time datasets with MapReduce and Apache Beam based capabilities of Google Cloud and practice identification and analysis of effective data features for predictive analytics with BigQuery ML and TensorFlow.
Audience
Developers using Google Cloud who need to take their MapReduce and Apache Beam capabilities to the next level.
Role
Business Analyst - Data Engineer - Data Scientist - Software Developer - Technical Manager
Skill Level
Intermediate
Style
Fast Track - Targeted Topic - Workshops
Duration
2 Days
Related Technologies
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  • Productivity Objectives:
    • Employ DataProc to perform MapReduce based data analysis.
    • Integrate transactional data from a Cloud SQL database in data analysis.
    • Apply Apache Beam based data analysis pipelines for batch and streaming data.
    • Support data science and machine learning through analysis of effective data features.
    • Use Google Colab and Jupyter notebooks for Python based data analysis.
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about our training

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    Real-World Content

    Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.

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    Expert Practitioners

    Industry experts with 15+ years of industry experience that bring their battle scars into the classroom.

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    Experiential Learning

    More coding than lecture, coupled with architectural and design discussions.

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    Fully Customized

    One-size-fits-all doesn't apply to training teams. That's where we come in!

What You'll Learn

In the Intermediate Google Cloud For Data Analysts training course, you'll learn:

  • MapReduce for Data Analysts
    • Map vs. FlatMap for MapReduce
    • Running Apache Hive, Apache Pig, and PySpark
    • Provisioning Managed Apache Hadoop/Spark/YARN Infrastructure
    • Pre-Emptible Instances for MapReduce
    • Dataproc User Interface
    • Running and Monitoring MapReduce Jobs
  • Cloud SQL
    • Transactional Data for Analysis
    • Provisioning Managed Database Infrastructure
    • Configuration of MySQL on GCP
    • Batch Data Import/Export with Cloud SQL
    • Web-based Interface
    • Integration of MySQL with GCP Services and Applications
    • Recommendation Systems with Cloud SQL
  • Apache Beam for Data Analysts
    • Batch and Streaming Data Processing Pipelines
    • Run Apache Beam in Cloud Shell
    • Apache Beam Combine vs. GroupBy
    • Submitting Apache Beam Pipelines
    • Running Batch and Streaming Dataflow Jobs
    • Apache Beam Pipelines with Side-Inputs
    • Autoscaling Streaming Apache Beam Jobs
    • Apache Beam Windows and Triggers
    • Web-based and Command Line Interface
    • Monitoring Dataflow Jobs
  • Jupyter for Data Analysis
    • Google Colab
    • BigQuery from Colab
    • Pandas DataFrames and Series
    • GroupBy and Pivot Table
    • Data visualization with Seaborn
    • Predictive Analytics with TensorFlow
  • Data Analytics for Data Science
    • Five Criteria for Effective Data Features
    • Feature Engineering Case Studies and Best Practices
    • Feature Crosses, Quantization, One-hot Encoding
    • Feature Creation and Pre-processing in a Machine Learning Pipeline
    • Feature Engineering for Wide-and-Deep Models
Real-world content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.

Expert Practitioners

Industry experts that bring their battle scars into the classroom.

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.

Fully Customized

One-size-fits-all doesn't apply to training teams. That's where we come in!

Elite Instructor Program

We recently launched our internal Elite Instructor Program. The community driven instructor program is designed to support instructors in transforming students’ lives by consistently showing a world-class level of engagement, ability, and teaching prowess. Reach out today to learn more about our instructors.

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