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Introduction to NumPy

NumPy is an essential Python library. TensorFlow and scikit-learn use NumPy arrays as inputs, and pandas and Matplotlib are built on top of NumPy. In this Introduction to NumPy course, you'll become a master wrangler of NumPy's core object: arrays! You'll discover why NumPy is so efficient and use broadcasting and vectorization to make your NumPy code even faster. By the end of the course, you'll be using 3D arrays to alter a Claude Monet painting.

By Izzy Weber, Curriculum Developer @ DataCamp

1. Understanding NumPy Arrays

Create and change array shapes to suit your needs. Discover NumPy's many data types and how they contribute to speedy array operations.

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Introduction to version control with Git

Discover the importance of version control when working on data science projects and explore how to use Git to track files, compare differences, modify and save files, undo changes, and allow collaborative development through the use of branches. Introduction to the structure of a repository, create new repositories and clone existing ones, and show how Git stores data. Skills to handle conflicting files.

By George Boorman, Analytics and Data Science Curriculum Manager, DataCamp

Ressources: Git Cheatsheet

1. Introduction to Git

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GitHub Concepts

Learn How to Use GitHub and explore Key GitHub Concepts.

Building on the topics covered in Introduction to Version Control with Git, this conceptual course enables you to navigate the user interface of GitHub effectively. Create public and private repositories, create and modify files, branches, and issues, assign tasks, tag users, review pull requests, and merge branches. Clone and fork repositories and generate private access tokens (PAT).

By Jasmin Ludolf, Data Science Content Developer, DataCamp

1. Introduction to GitHub

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Introduction to Testing in Python

Learn the very basics of creating tests in Python. Meet four types of software testing methods. Know the types of testing methods, and choose the most suitable ones for a specific context. Design tests and implement them in Python using the pytest and the unittest libraries.

Creating Tests with pytest

• Why is testing so important?
• What is testing?
• Testing with pytest - a simple example

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CI/CD for Machine Learning

Streamline your machine learning development processes, enhancing efficiency, reliability, and reproducibility in your projects. Develop a comprehensive understanding of CI/CD workflows and YAML syntax, utilizing GitHub Actions (GA) for automation, training models in a pipeline, versioning datasets with DVC, and performing hyperparameter tuning.

Introduction

Essential principles of Continuous Integration/Continuous Delivery (CI/CD) and YAML. Software development life cycle and key terms like build, test, and deploy. Continuous Integration, Continuous Delivery, and Continuous Deployment. Significance of CI/CD in machine learning and experimentation.

• SDLC Overview

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Introduction to AWS

Discover the world of Amazon Web Services (AWS) and understand why it's at the forefront of cloud computing.

By Amar Suchak - Software Engineer, Microsoft

Introduction AWS

An in-depth introduction to AWS. Learn about its impact on cloud computing, explore its core service domains - Compute, Storage, Database, and Networking, and understand its global architecture.

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Introduction to NoSQL

NoSQL databases have seen a surge in popularity due to their ability to handle large volumes of unstructured data. Learning to leverage NoSQL tools such as column-oriented, document, key-value, and graph databases allows data engineers, analysts, and scientists to interact with a wider breadth of data. These NoSQL databases bring additional functionality to data storage and retrieval that isn’t available in traditional relational databases.

By Jake Roach, Senior AI Engineer

Introduction to NoSQL databases

Basics of NoSQL databases. Identify the pros and cons of using column-oriented, document, key-value, and graph databases to make data available to downstream processes and data consumers.

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Introduction to GCP

Introduction to GCP's architecture and core services, including Storage, Database, and Compute. GCP's data handling capabilities, covering Cloud Spanner, BigQuery, and the integration of AI and ML technologies. Modernization of business using GCP, addressing legacy systems, cloud migration, and digital transformation.

By Nabeel Imam - Data scientist

Cloud basics and beyond

Unique advantages and diverse services in Google Cloud Platform (GCP) storage, database, and compute. Architecture, core components, and service interconnectivity. Floud data storage, virtual machines, container orchestration, and serverless computing.

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Introduction to Redshift

Foundational understanding of Amazon Redshift, a distributed columnar database within the AWS cloud. Integration into the AWS ecosystem and its SQL constructs, including common table expressions and analytics queries. Create tables, manage data distribution, handle raw data files, and extract information from JSON. Optimize queries, tune performance, and enhance data security.

By Jason Myers, Co-Author of Essential SQLAlchemy and Software Engineer

Redshift, a data warehouse

• Distributed Columnar database

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Reinforcement Learning with Gymnasium in Python

Exploration of Reinforcement Learning (RL), a pivotal branch of machine learning. Core principles of RL, training intelligent agents, teaching them to make strategic decisions and maximize rewards. Agents will learn to navigate a whole host of different environments from OpenAI's gym toolkit, including navigating frozen lakes and mountains.

Presented by Fouad Trad, Machine Learning Engineer

Introduction to Reinforcement Learning

Foundational concepts, roles, and applications. RL framework, agent-environment interaction. Use the Gymnasium library to create environments, visualize states, and perform actions - practical foundation in RL concepts and applications.