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Big Data, practical methods and solutions for data analysis Training

Hands-on course
Best
Duration : 5 days
Ref : BID
Price  2019 : Contact us
  • Program
  • Participants / Prerequisite
  • Intra/Tailored
Program

This course will enable you to understand the issues and benefits of Big Data as well as the technologies to implement it. You'll learn how to integrate massive volumes of structured and unstructured data via an ETL, then to analyze them using statistical models and dynamic dashboards.

Learning objectives

  • Understand the concepts and benefits of Big Data with respect to business challenges
  • Understand the technological ecosystem needed to carry out a Big Data project
  • Acquire the technical skills to manage massive, unstructured, complex data flows
  • Implement statistical analysis models to address business needs
  • Learn about a data visualization tool for reporting dynamic analyses

Hands-on work

Set up a Hadoop platform and its basic components, use an ETL to manage the data, create analysis modules and dashboards.
PROGRAM

Understanding the concepts and challenges of Big Data

  • Origins and definition of Big Data: BI faced with the growth and diversity of data.
  • Key figures in the international and French markets.
  • The challenges of Big Data: ROI, organization, data privacy.
  • An example of Big Data architecture.

Big Data technologies

  • Description of the architecture and components of the Hadoop platform.
  • Storage methods (NoSQL, HDFS).
  • Operating principles of MapReduce.
  • Overview of the main distributions on the market and additional tools (Hortonworks, Cloudera, MapR, Aster).
  • Installing a Hadoop platform.
  • Overview of Big Dataspecific technologies (Talend, Tableau, Qlikview, etc.).

Exercise
Installing a full Big Data platform via Cloudera and its components.

Managing structured and unstructured data

  • Operating principles of the Hadoop Distributed File System (HDFS).
  • Importing outside data into HDFS.
  • Creating SQL requests with HIVE.
  • Using PIG to process the data.
  • Using an ETL to industrialize the creation of massive data flows.
  • Overview of Talend For Big Data.

Exercise
Implementing massive data flows

Data analysis methods for Big Data

  • Exploration methods.
  • Segmentation and classification.
  • Estimating and prediction.
  • Implementing models.

Exercise
Setting up analyses with the software R.

Data visualization and concrete use cases

  • Offtheshelf visualization tools
  • Report formatting methodology
  • Benefits of Big Data for “Social Business”.
  • Measuring the ereputation and fame of a brand.
  • Measuring customer satisfaction and experience, optimizing the customer's path.

Exercise
Installing and using a Data Visualization tool to create dynamic analyses, retrieving data from social media, and creating ereputation analyses.

Conclusion

  • Takeaways.
  • Summary of best practices.
  • Bibliography.
Participants / Prerequisite

» Participants

Dataminers, statistical researchers, developers, project managers, business intelligence consultants.

» Prerequisite

Basic knowledge of relational models, statistics, and programming languages. Basic knowledge of Business Intelligence concepts.
Intra/Tailored

Contact Informations

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Time schedule

Generally, courses take place from 9:00 to 12:30 and from 14:00 to 17:30.
However, on the first day attendees are welcomed from 8:45, and there is a presentation of the session between 9:15 and 9:30.
The course itself begins at 9:30. For the 4- or 5-day hands-on courses, the sessions finish at 15:30 on the last day
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