# Data Academy

The Data Academy is designed for data professionals who want to maintain and grow their data expertise. In today’s world, data is the lifeblood of any successful business, and data professionals are in higher demand than ever before. The Data Academy subscription includes courses built by industry experts, hands-on practice with real datasets, and frequent knowledge checks. At the end, learners will be true data subject matter experts, though some prefer to be called “data deities”.

**WHAT IT IS**A subscription to 100+ in-depth courses, quizzes, and hands-on practice opportunities designed to make you a data whiz.

**WHY YOU NEED IT**Because the world is powered by data, and an expert-level understanding of data principles and applications is key to getting ahead.

**TOPICS COVERED**Data Fundamentals, Data Science, Artificial Intelligence, Machine Learning, Deep Learning, Reinforcement Learning.

Our Academies are not only organized by topic, but suggested orders are included within each, allowing you to go in order or jump around — you control what you learn next, not us.

##### ALREADY SUBSCRIBED TO THE DATA ACADEMY?

GET STARTED WITH DATA STORAGE!

### Introduction to Data Storage

You will build your first AI system, and look at optimization in this course.

#### CHOOSE A DATA ACADEMY TOPIC BELOW TO START LEARNING

### courses included in the data academy subscription

##### The Python Workshop

- Introduction to Python – Math, Strings, Conditionals and Loops
- Getting started with Python Structures
- Executing Python – Programs, Algorithms, and Functions
- Extending Python – Files, Errors, and Graphs
- Constructing Python – Classes and Methods
- Understanding the Standard Library
- Becoming Pythonic
- Software Development with Python
- Discovering Tools for Python Developers
- Introduction to Data Analytics with pandas and NumPy
- Introduction to Machine Learning Models

##### The Statistics and Calculus Workshop

- Introduction to Python: Structures and Tools
- Understanding Python’s Main Tools in Statistics
- Employing Python’s Tools with Statistics
- Using Functions and Algebra with Python
- Mathematics with Python
- Extending Mathematics with Python
- Exploring and Visualizing Statistics with Python
- Applying Foundational Probability Concepts
- Developing Python’s Use in Statistics
- Beginning Calculus with Python
- Extending Calculus with Python
- Practicing Calculus with Python

##### The Applied Data Science Workshop

##### The Data Wrangling Workshop

- Introduction to Data Wrangling with Python
- Advanced Operations on Python Data Structures
- Introduction to NumPy, Pandas and Matplotlib
- A Deep Dive into Data Wrangling with Python
- Reading Data from Different Sources
- The Hidden Secrets of Data Wrangling
- Advanced Web Scraping and Data Gathering
- Relational Database Management Systems and SQL
- Applications in Business Use Cases

##### The Applied SQL Workshop

##### The Artificial Intelligence Infrastructure Workshop

- Introduction to Data Storage
- Understanding Artificial Intelligence Storage Requirements (SCOPHILD)
- Updating Data
- Introduction to the Ethics of AI Data Storage
- Working with Data Stores: SQL and NoSQL Databases
- Handling Big Data File Formats
- Introduction to Analytics Engine (Spark) for Big Data
- Introduction to Data System Design
- Introduction to Workflow Management Platform (Airflow)
- Introduction to Data Storage on Cloud Services (AWS)
- Building an Artificial Intelligence Algorithm
- Productionizing your AI application with Docker

##### The Data Analysis Workshop

- Performing Bike Sharing Analysis
- Exploring Absenteeism at Work
- Analyzing the Bank Marketing Dataset
- Investigating Company Bankruptcy
- Identifying Online Shoppers’ Purchase Intentions
- Interpreting the Credit Card Defaulter Dataset
- Analyzing the Heart Disease Dataset
- Exploring the Online Retail Dataset
- Predicting the Energy Usage of Household Appliances
- Investigating Air Quality in Beijing

##### The Data Visualization Workshop

##### The Applied Artificial Intelligence Workshop

##### The Natural Language Processing Workshop

- Natural Language Processing Fundamentals
- Pre-Processing Data and Feature Extraction
- Machine Learning and Developing a Text Classifier
- Extracting and Analyzing Web Data
- Using and Comparing Topic Modeling Algorithms
- Understanding Word and Document Vectors
- Using Text Generators and Summarization Models
- Performing Sentiment Analysis with NLP

##### The Computer Vision Workshop

##### The Machine Learning Workshop

##### The Supervised Learning Workshop

##### The Unsupervised Learning Workshop

##### The Reinforcement Learning Workshop

- Introduction to Reinforcement Learning
- Introduction to The Markov Decision Process and Dynamic Programming
- Practice Deep Learning with TF2
- Getting Started with OpenAI and TensorFlow for RL
- Introduction to Dynamic Programming
- Introduction to Monte Carlo Methods
- Introduction to Temporal-Difference Learning
- Solving the Multi Armed Bandit Problem
- Introduction to Deep Q Learning
- Playing an Atari Game with a Deep Recurrent Q Network
- Introduction to Policy Based Methods for Reinforcement Learning
- Discussing Evolutionary Strategies for Reinforcement Learning
- Discussing Advancements for Reinforcement Learning

##### The Applied AI and Natural Language Processing Workshop

##### The Deep Learning with Tensorflow Workshop

##### The Deep Learning with PyTorch Workshop

- Introduction to Deep Learning and PyTorch
- Discovering the Building Blocks of Neural Networks with PyTorch
- Solving a Classification Problem with DNNs Using PyTorch
- Introduction to Convolutional Neural Networks with PyTorch
- Performing Style Transfer with PyTorch
- Analyzing the Sequence of Data with RNNs Using PyTorch

##### The Deep Learning with Keras Workshop

- Machine Learning Fundamentals with Keras
- Building Artificial Neural Networks in Keras
- Deep Neural Networks with Keras
- Cross-Validation and Keras Wrappers
- Regularization for Neural Networks in Keras
- Model Evaluation
- Computer Vision with Convolutional Neural Networks
- Transfer Learning with Pre-Trained Networks
- Sequential Modeling with Recurrent Neural Networks