Develop your Big Data and Data Science skills with Octopeek Academ’AI

The Octopeek Academ’AI training curriculum grows the understanding of the new technologies and new know-how related to digital transformation through data.

Octopeek Academ’AI supports you in your professional development

The Octopeek Academ’AI training organization is approved by OPCO. The training provided by Octopeek can be financed within the framework of the training plans for your company.

The trainers in our center are all experts in their fields, PhD level for the most part. By relying on their experience in large accounts, our trainers can share their knowledge and their expertise during these sessions. They will mix theory and practice, by integrating concrete cases derived from their business experiences.

Our courses are given in two modes: intra company (organized with employees belonging to the same company) and inter- company (organized with employees belonging to different companies).

They take place in our training center in Enghien les Bains (95). For intra-company training, they can also take place on the client’s premises.

State of the art and fundamentals

Big Data | Issues and opportunities

The purpose of this training is to identify the specific context of Big Data projects in a number of business sectors. The challenge is to understand the social-economic environments and the technological panorama of the projects.

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► Learning objectives:
▪ Understand the issues of Big Data
▪ Discover the opportunities of Big Data
▪ Acquire the necessary knowledge
to implement Big Data technologies
▪ Evaluate and select the appropriate tools as part of a Big Data Implementation Plan
► Target audience:
Project Manager, CIO, IS Architect and anyone interested in Big Data issues and opportunities.

Piloting a Big Data project

The objective of this course is to understand the challenges in setting up a Big Data project. This includes the technological, organizational and legal specificities, as well as the governance and security of Big Data projects.

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► Learning objectives:
▪ Understand the technological, organizational and legal specificities of a Big Data project
▪ Properly understand the challenges in setting up a Big Data project
▪ Properly manage conflicts and the changing of behavior
▪ Control the skills needed to build a Big Data project team
► Target audience:
Project Manager, Project Director, IS Director
and anyone in charge of implementing and managing Big Data projects

Big Data infrastructure & technologies

Big Data | Architecture and technologies

This summary course will present the issues and contributions of Big Data as well as the technologies for its implementation. You will learn to take care of a project managing massive data, as well as the analysis and visualization of the data.

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► Learning objectives:
▪ Discover the key concepts of Big Data
▪ Understand the technological ecosystem of a Big Data project
▪ Learn to implement a Big Data solution.
▪ Obtain an overview of the opportunities of Big Data
► Target audience:
▪ Project Manager,
Architect and anyone wishing to know the impact of Big Data at the organization and architecture levels

Hadoop | Architecture and Administration

Find out more | Download the file Hadoop is the main Big Data platform. It takes care of the storage and processing of huge volumes of data. This training will allow you to understand its architecture so you can install, configure and administer a Hadoop cluster.
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► Learning objectives:
▪ Be able to define the architecture of a Hadoop solution
▪ Install and configure a Hadoop cluster
▪ Configure and customize a Hadoop environment
to optimize the availability and flow of data
► Target audience:
▪ Project Manager
▪ System Administrator and anyone who wants to implement a Hadoop system

Big Data Development and Data Analysis

Pig, Hive & Spark Data Analysis

The purpose of this course is to present tools for processing and analyzing data on Hadoop. It will allow you to develop skills in data analysis focusing on Pig, Hive and Spark, which you will be able to use.

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► Learning objectives:
▪ Understand what Hadoop and YARN are
▪ Be able to handle data under Hadoop
▪ Be able to handle data with Spark
▪ Be able to analyze data with Hive
► Target audience:
Any person wishing to handle and analyze data in a Hadoop cluster

MapReduce & Spark Development on Hadoop

This course will give you necessary skills to develop solutions compatible with the Hadoop platform. You will learn how to develop MapReduce and Spark applications under Hadoop.

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► Learning objectives:
▪ Understand the operation of MapReduce
▪ Understand the operation of Spark
▪ Be able to develop distributed applications in a Hadoop environment
► Target audience:
▪ Project Manager
▪ Developer
▪ Design Engineer
▪ Technical architect and anyone wishing to develop distributed applications in
a Hadoop environment

NoSQL databases

The use of NoSQL databases has exploded in recent years.
The objective of this course is to present the state of the art in this field. It will give you an understanding of the NoSQL database concept, and you will learn how to use the HBase and ElasticSearch databases.

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▪ Understand the principle of NoSQL databases
▪ Be able to use NoSQL databases
Know how to choose the basic NoSQL model that best meets the needs
▪ Understand the operation of HBase and Elasticsearch
and how to manipulate them
► Target audience:
▪ Project Manager
▪ Database administrator, IS Director and anyone responsible for the implementation and management of a NoSQL database

Data Visualization Techniques

DataViz is the art of assigning a view to the data. It is present at all stages of a Big Data project. This course will provide knowledge of visualization tools and methods. This will allow you to better understand your data, and accelerate your decision-making at the same time.

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► Learning objectives:
▪ Understand the principle of data visualization
“DataViz”
▪ Discover DataViz tools and frameworks
▪ Understand how to choose a good representation of data
► Target audience:
Data Scientist,
Data Analyst, Developer, Manager,
Marketing Manager and any person wishing to visualize data

Data Science

Data Science | fundamentals

Data Science is today central to digitized companies. The purpose of this course is to know about the best practices and tools used in Data Science

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► Learning objectives:
▪ Learn to model a Data Science problem
▪ Learn to extract value from large data sets using a variety of
Machine Learning methods
▪ Learn about data cleaning and preparation best practices.
► Target audience:
▪ Developers
▪ Data Scientists and anyone wishing to acquire knowledge in the field of Data Science.

R for Data Science

Data Science requires specific skills to predict customer behavior or anticipate trends. This training will allow you to discover data analysis as well as Machine Learning via R, the different steps involved in the development of a statistical model, as well as textual analysis and Data Visualization.

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► Learning objectives:
▪ Be able to model a Data Science problem in R
▪ Learn how to handle, prepare, clean, treat and
analyze data with R.
► Target audience:
▪ R developers
▪ Data Analysts
▪ Data Scientists and anyone wishing to acquire knowledge in the field of Data Science and R.

Spark for Data Science

Data Science is today central to digitized companies. The purpose of this course is to become familiar with Spark and understand its use to treat Data Science problems

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► Learning objectives:
▪ Become familiar with Spark
▪ Learn to address issues relating
to Data Science with Spark
▪ Understand the principle of Spark operation
and learn about the main libraries (Streaming, SQL, Machine Learning, GraphX ).
► Target audience:
▪ Developers,
▪ Data Analysts,
▪ Data Scientists
▪ Anyone wishing to acquire knowledge in the field of Data Science and Spark.

Python for Data Science

Data Science requires specific skills to predict customer behavior or anticipate trends. This training will allow you to discover Data Science via Python, the different steps involved in the development of a statistical model, as well as textual analysis and Data Visualization.

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► Learning objectives:
▪ Be able to model a Data Science problem in Python
▪ Discover the main digital computing libraries
including Numpy, Pandas and Matplotlib
► Target audience:
▪ Developers
▪ Data Analysts
▪ Data Scientists
▪ Anyone wishing to acquire knowledge
in the field of Data Science and Python

Advanced Data Science

The growing importance of Data Science means that it needs to be mastered and expertise gained, especially in companies with strong digital components. This course aims to take previously acquired basic Data Science knowledge to a deeper level. Introducing advanced Machine Learning algorithms, such as Deep Learning, will help you better leverage traditional methods by learning to choose an appropriate model. You will learn to adapt it to a specific need and optimize its hyper-parameters.

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► Learning objectives:
▪ Discover and use parameter optimization techniques
▪ Understand supervised, unsupervised, Deep Learning and NLP learning techniques
► Target audience:
▪ Data Analysts
▪ Data Scientists and anyone wishing to gain in-depth knowledge of Data Science and Machine Learning.

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