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Data Just Right LiveLessons

Course Summary

Video Training


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    Course Syllabus

    • Introduction
      • Introduction to Data Just Right LiveLessons
    • Lesson 1: Four Rules for Data Success
      • Learning objectives
      • 1.1 Why is big data such a hot concept now?
      • 1.2 Four strategies for tackling big data problems
      • 1.3 Anatomy of a data pipeline
      • 1.4 What the ideal database would look like
    • Lesson 2: Hosting and Sharing Terabytes of Raw Data
      • Learning objectives
      • 2.1 Challenges of hosting and sharing large amounts of data
      • 2.2 Choosing the right data format
      • 2.3 Best practices for physically storing and sharing large amounts of data
      • 2.4 Understanding data serialization formats
    • Lesson 3: Building a NoSQL-Based Web App to Collect Crowd-Sourced Data
      • Learning objectives
      • 3.1 History and use of relational databases
      • 3.2 Databases and the Internet: Understanding the CAP theorem
      • 3.3 Non-relational databases: Document and key-value stores
      • 3.4 Introduction to Redis
      • 3.5 Sharding Redis across a cluster of machines
      • 3.6 Future trends in database technology
    • Lesson 4: Strategies for Dealing with Data Silos
      • Learning objectives
      • 4.1 History and meaning of business intelligence
      • 4.2 Data warehousing and Hadoop
      • 4.3 Data silos can be good
      • 4.4 Convergence and the future of the business intelligence concept
    • Lesson 5: Using Hadoop, Hive, and Shark to Ask Questions about Large Data
      • Learning objectives
      • 5.1 Introduction to Apache Hive
      • 5.2 Loading data into Hive
      • 5.3 Querying data with Hive
      • 5.4 Introduction to AMPLab's Shark
      • 5.5 Data warehousing in the cloud
    • Lesson 6: Building a Data Dashboard with Google BigQuery
      • Lesson 6: Building a Data Dashboard with Google BigQuery
      • 6.1 Introduction to analytical databases
      • 6.2 Google's Dremel and BigQuery
      • 6.3 Running a BigQuery query and retrieving the result
      • 6.4 Visualizing BigQuery query results
      • 6.5 The future of analytical query engines
    • Lesson 7: Visualization Strategies for Exploring Large Datasets
      • Learning objectives
      • 7.1 History and goals of data visualization
      • 7.2 Strategies for dealing with visualization of very large datasets
      • 7.3 Building interactive visualizations with R and ggplot()
      • 7.4 Building 2D plots with Python and matplotlib
      • 7.5 Building interactive visualizations for the Web with D3.js
    • Lesson 8: Putting it Together: MapReduce Data Pipelines
      • Learning objectives
      • 8.1 Writing a simple data pipeline script
      • 8.2 Introduction to the Hadoop MapReduce framework
      • 8.3 Writing a Hadoop streaming MapReduce job in Python
      • 8.4 Writing a multistep MapReduce job using the mrjob Python library
      • 8.5 Running mrjob scripts on Amazon Elastic MapReduce
    • Lesson 9: Building Data Transformation Workflows with Pig and Cascading
      • Learning objectives
      • 9.1 Challenges of building complex data workflows
      • 9.2 Writing a MapReduce workflow script with Apache Pig
      • 9.3 Creating a MapReduce workflow application with Cascading
      • 9.4 When to use Pig versus Cascading
    • Lesson 10: Building a Data Classification System with Mahout
      • Learning objectives
      • 10.1 Use cases and limitations of machine learning
      • 10.2 Bayesian classification, clustering, and recommendation engines
      • 10.3 Using Apache Mahout for bayesian classification
      • 10.4 Introduction to MLbase
    • Lesson 11: Using R with Large Datasets
      • Learning objectives
      • 11.1 Understanding memory usage with R
      • 11.2 Working with large matrices using bigmemory and biganalytics
      • 11.3 Manipulating large data frames with ff
      • 11.4 Running a linear regression over large datasets using biglm
      • 11.5 Interfacing with Hadoop using R and RHadoop
    • Lesson 12: Build Analytics Workflows Using Python and Pandas
      • Learning objectives
      • 12.1 Choosing a programming language for analytics
      • 12.2 Working with NumPy and SciPy
      • 12.3 Using the Pandas library for analysing time series data
      • 12.4 Using the iPython notebook
    • Lesson 13: When to Build, When to Buy, When to Outsource
      • Learning objectives
      • 13.1 Understanding Your Data Problem
      • 13.2 A playbook for the build versus buy problem
      • 13.3 Investing in a data center: Public versus private
      • 13.4 Understanding the costs of open-source software
      • 13.5 Using analytics as a service technologies
    • Lesson 14: The Future: Trends in Data Technology
      • Learning objectives
      • 14.1 Trends driving innovation in data analytics technology
      • 14.2 Hadoop: The disruptor and the disrupted
      • 14.3 Analytics move toward the cloud
      • 14.4 The evolving definition of “data scientist”
      • 14.5 Converging technologies
    • Summary
      • Summary of Data Just Right LiveLessons


Course Fee:
USD 299

Course Type:

Self-Study

Course Status:

Active

Workload:

1 - 4 hours / week

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Awards & Accolades for MyTechLogy
Winner of
REDHERRING
Top 100 Asia
Finalist at SiTF Awards 2014 under the category Best Social & Community Product
Finalist at HR Vendor of the Year 2015 Awards under the category Best Learning Management System
Finalist at HR Vendor of the Year 2015 Awards under the category Best Talent Management Software
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