Course : Big Data: Practical methods and solutions for data analysis
Practical course - 5d
- 35h00 - Ref. BID
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 | 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.
Customer reviews
4,4 / 5
Customer reviews are based on end-of-course evaluations. The score is calculated from all evaluations within the past year. Only reviews with a textual comment are displayed.
BOB B.
08/12/25
4 / 5
Overall, satisfied with the educational activities.
DIDIER M.
08/12/25
4 / 5
A good understanding of the HADOOP and Bigdata ecosystems
VINCENT J.
01/12/25
5 / 5
Khalil is an extremely competent and attentive trainer. He has a perfect command of the subjects and is an exceptional teacher. He also goes beyond the training to answer any questions and share his knowledge and experience. He is THE strong point of this course.
PARTICIPANTS
PREREQUISITES
TRAINER QUALIFICATIONS
The experts leading the training are specialists in the covered subjects. They have been approved by our instructional teams for both their professional knowledge and their teaching ability, for each course they teach. They have at least five to ten years of experience in their field and hold (or have held) decision-making positions in companies.
ASSESSMENT TERMS
The trainer evaluates each participant’s academic progress throughout the training using multiple choice, scenarios, hands-on work and more.
Participants also complete a placement test before and after the course to measure the skills they’ve developed.
TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training.
ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you need special accessibility accommodations? Contact Mrs. Fosse, Disability Manager, at psh-accueil@orsys.fr to review your request and its feasibility.
PREREQUISITES
TRAINER QUALIFICATIONS
The experts leading the training are specialists in the covered subjects. They have been approved by our instructional teams for both their professional knowledge and their teaching ability, for each course they teach. They have at least five to ten years of experience in their field and hold (or have held) decision-making positions in companies.
ASSESSMENT TERMS
The trainer evaluates each participant’s academic progress throughout the training using multiple choice, scenarios, hands-on work and more.
Participants also complete a placement test before and after the course to measure the skills they’ve developed.
TEACHING AIDS AND TECHNICAL RESOURCES
• The main teaching aids and instructional methods used in the training are audiovisual aids, documentation and course material, hands-on application exercises and corrected exercises for practical training courses, case studies and coverage of real cases for training seminars.
• At the end of each course or seminar, ORSYS provides participants with a course evaluation questionnaire that is analysed by our instructional teams.
• A check-in sheet for each half-day of attendance is provided at the end of the training, along with a course completion certificate if the trainee attended the entire session.
• The main teaching aids and instructional methods used in the training are audiovisual aids, documentation and course material, hands-on application exercises and corrected exercises for practical training courses, case studies and coverage of real cases for training seminars.
• At the end of each course or seminar, ORSYS provides participants with a course evaluation questionnaire that is analysed by our instructional teams.
• A check-in sheet for each half-day of attendance is provided at the end of the training, along with a course completion certificate if the trainee attended the entire session.
TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training.
ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you need special accessibility accommodations? Contact Mrs. Fosse, Disability Manager, at psh-accueil@orsys.fr to review your request and its feasibility.
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