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Course : Big Data: Practical methods and solutions for data analysis

Big Data: Practical methods and solutions for data analysis






INTER
IN-HOUSE
CUSTOM

Training at your location, our location or remotely

Ref. BID
  5d - 35h00
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Teaching objectives
At the end of the training, the participant will be able to:
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

Practical details
Hands-on work
Set up a Hadoop platform and its basic components, use an ETL to manage the data, create analysis modules and dashboards.

Course schedule

1
Understanding the concepts and challenges of Big Data

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

2
Big Data technologies

  • Description of the architecture and components of the Hadoop platform.
  • Storage methods (NoSQL, HDFS).
  • Operating principles of MapReduce, Spark, Storm, etc.
  • Most popular distributions on the market (Hortonworks, Cloudera, MapR, Elastic Map Reduce, Biginsights).
  • Installing a Hadoop platform.
  • Technologies for the data scientist.
Exercise
Exercise

3
Installing a Hadoop Big Data platform (via Cloudera Quickstart or other software).

  • 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
Operating principles of the Hadoop Distributed File System (HDFS).

4
Importing outside data into HDFS.

  • Creating SQL requests with HIVE.
  • Using PIG to process the data.
  • The principle of ETL (Talend, etc.).
  • Managing massive data streaming (NIFI, Kafka, Spark, Storm, etc.)
Exercise
Implementing massive data flows

5
Big Data Analytics techniques and methods

  • Machine Learning: A component of artificial intelligence.
  • Discovering the three families: Regression, Classification, and Clustering.
  • Data preparation, feature engineering.
  • Generating models in R or Python.
  • Ensemble Learning.
Exercise
Exercise

6
Setting up analyses with the tools studied.

  • Takeaways.
  • Summary of best practices.
  • Bibliography.


Dates and locations
Select your location or opt for the remote class then choose your date.
Remote class

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