Linux system and Bash Script

• Presentation of Linux Systems
• Handling and use of a terminal
• Setting up Bash Script

Python & Object Oriented Python

• Mastering variables and types
• Presentation of the various operators and their applications
• Introduction to the concept of loops and control structures
• Definition of a function on Python and presentation of their applications
• Introduction to classes and modules
• Preparation of the implementation, the parameterization and the chaining of Decorators
• Differentiation and implementation of multithreading and multiprocessing on Python
• Application of an asynchronous function on Python
• Introduction to annotations and use of the mypy library

SQL

• Introduction to relational databases relational databases
• Presentation of SQL Alchemy and applications
• Introduction to the basics of the SQL language
• Learning SQL and its applications

Mongo DB

• Introduction to databases NoSQL (document, column, graph oriented databases document, column, graph oriented databases)
• Presentation of MongoDB
• Familiarization with the syntax of
• MongoDB queries

ElasticSearch

• Description of a search engine
• Presentation of an index and instructions for use
• Development of a Mapping
• Discovery of the different operations
• Data pre-processing with Ingest Node
• Extraction of data with the Text analyzer

Neo4J

• Introduction to graph-oriented databases
• Setting up a first graph
• Introduction to the Cypher query language
• Loading data into Neo40
• Using a Python client for Neo45

Statistics

• Exploration of numerical variables
• Exploration of categorical variables fry
• Study of relationships between variables

Machine Learning

• Data pre-processing
• Selection and optimization of a Machine Learning algorithm
• Definition and application of a regression regression algorithm
• Definition and application of a classification algorithm
• Development of clustering
• introduction to PCA

Data Visualization with Matplotlib

• Presentation of different types of graphs:
  Bar graphs (Barplots)
  Scatter plotsHistograms

• Box plots

• Pie Plots

-Dash application creation

Hadoop & Hive

• How Hadoop works
• Installation and configuration of Hadoop
• Data processing and storage with HDFS
• Introduction to MapReduce
• Using Hadoop Streaming to run a Map/ Reduce file
• Setting up data warehouses
• Presentation of how Hive works

Spark

• Distinction between Spark and Hadoop
• Introduction to distributed computing with Spark
• Overview of Spark's RDD and Dataframe APIs
• Distributed Data Processing Pipeline with PySpark
• Distributed Machine Learning with Spark MLLib

HBase

• Presentation of column-oriented databases
• Association of Hadoop (HDFS) and Hbase
• Data queries
• Data modification by P/thon and happybase

Git

• Introduction to version management system
• Initialization of a Git repository
• Presentation and deepening of git™
  concepts:

-Branches

-Tag

-Merge

Github

• Implementation of unit tests with Pytest
• Introduction to Integration Tests and their functions
• Presentation of the advantages of testing time saving, readability, quality and quality and improvement of code

Quality Assurance

• Discover the Github platform for collaborative work on Git
• Presentation of the major features of GitHub:
  • Fork
  • Pull Request
  • Issues
• Share your modifications with pull and push
• Participation in the improvement of projects public (open source)
• Overview of main git workflows

Streaming architecture

• Real-time data flow management
• Design of a hybrid Big Data architecture (batch and real time)
• Implementation of a Lambda architecture

Spark Streaming

• Presentation of the distributed streaming platform Kafka:
  • Architecture            • Advantages

• Management of Producers settings

• Partitioning key

• Mastering Consumers settings
  • Consumer group

Kafka

• Getting started with Spark Streaming for real-time data processing
• Presentation of the mini-batch streaming necessary for the operation of Spark
  Streaming

APIs

• Introduction to APIs and discovery of microservices architectures
• Presentation of the different HTTP methods and their functions
• Use of the FastAPl and Flask libraries to develop RESTful APIs
• Documentation of an API with the
  OpenAPI specification
• Error and performance management of anAPI

Docker

• Presentation of containerization and its usefulness in relation to virtualization
• Introduction to the functioning of Docker
• Handling images and containers
• Communication with containers
• Data persistence thanks to volumes
• Creating a Docker image via a Dockerfile
• Sharing images on the Dockerhub
• Use of docker-compose

Airflow

• Discovery of the Airflow concepts:

• Presentation of the principles of orchestration principles and usefulness
• Directed Acyclic Graphs or DAG
(Directed Acyclic Graphs)
• Operators
• Task management through specific
Operators
• Monitoring of DAGs via the graphical graphical interface

Kubernetes

• Deploy and manage containers