The python programming language is one of the most popular languages that data professionals use to do data analytics and AI. In this section, you will practice Python basics which act as a foundation for the rest of the sections in this course.

List of Lessons
Lesson 1: Introduction to Python (DataCamp)
Lesson 2: Python Data Science Toolbox (Part 1) (DataCamp)
Lesson 3: Python Data Science Toolbox (Part 2) (DataCamp)

What makes Python so powerful is that people like yourselves have written ‘libraries’ or toolkits that condense and optimize complicated programming methods to simple commands for us to use quickly and easily. Learning how to use libraries is a key skill in becoming a proficient data professional.

List of Lessons
Lesson 4: Intermediate Python (DataCamp)
Lesson 5: Cleaning Data in Python (DataCamp)
Lesson 6: pandas Foundations (DataCamp)
Lesson 7: Manipulating DataFrames with pandas (DataCamp)
Lesson 8: Merging DataFrames with pandas (DataCamp)

The first order of business whenever you get a new dataset is to explore it. In this section, we will learn the best practices for exploring and visualizing data.

List of Lessons
Lesson 9: Introduction to Data Visualisation in Python (DataCamp)
Lesson 10: Preprocessing for Machine Learning in Python (New – DataCamp)
Lesson 11:  Feature Engineering for Machine Learning in Python (New – DataCamp)
Lesson 12: Feature Engineering for NLP in Python (New – DataCamp)

To understand your data and find out the hidden properties you need to account for, a grounding in basic statistical thinking is required. This is the core foundation to reliable machine learning models.

List of Lessons
Lesson 13: Statistical Thinking in Python (Part 1) (DataCamp)
Lesson 14: Statistical Thinking in Python (Part 2) (DataCamp)
Lesson 15: Dimensionality Reduction in Python (New – DataCamp)

“Tell the machine that something is a cat over and over again, and it will start to learn the properties that separate cat pictures from other pictures”. Supervised learning is a machine learning task to learn a function that maps a given input to a given output (X -> Y). It is one of the most common and basic types of machine learning used in data science and AI.

List of Lessons
Lesson 16: Supervised Learning with scikit-learn (DataCamp)
Lesson 17: Regression (Part 2) (New – AI Makerspace)
Lesson 18: Classification (Part 2) (New – AI Makerspace)
Lesson 19: Machine Learning with Tree-based Models in Python (DataCamp)
Lesson 20: Model Validation in Python (New – DataCamp)

Challenge Yourself

• XGBoost Gradient Boosting (New)
• Machine Learning for Time Series in Python (New)

Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. It is slightly more complex than supervised learning, as it requires human interpretation, but is still a key pillar of machine learning techniques used by data professionals.

List of Lessons
Lesson 21: Unsupervised Learning in Python (DataCamp)
Lesson 22: Clustering Methods in SciPy (New – DataCamp)
Lesson 23: Unsupervised Learning (Part 2) (New – AI Makerspace)

Deep learning is a sub-type of machine learning where the algorithms used are inspired by biology. It is responsible for many of the breakthroughs in recent times, but is more complex and requires more computational power.

List of Lessons
Lesson 24: Deep Learning in Python (DataCamp)
Lesson 25: Introduction to Tensorflow (New – DataCamp)
Lesson 26: Introduction to Keras (New – DataCamp)
Lesson 27: Deep Learning (Part 2) (New – AI Makerspace)

Challenge Yourself

• Advanced Deep Learning with Keras (New)
• Image Processing in Python (New)
• Image Processing with Keras in Python (New)

Now that you have mastered the theoretical foundations of statistical learning and AI, how do you put it into practice in the real world?

List of Lessons
Lesson 28: Data Science and AI Project Lifecycle (New – AI Makerspace)
Lesson 29: Set up your own AI System using an AI Makerspace ‘brick’ (New – AI Makerspace)

Machine learning and AI do not just stop at Python and Deep learning. There are many other software tools, concepts and languages that good data professionals will need to be conversant with as they progress in their career. A well balanced data professional will be equally skilled at software engineering and AI engineering.

List of Lessons
Lesson 30: Introduction to SQL (DataCamp)
Lesson 31: Joining Data in SQL (DataCamp)
Lesson 32: Introduction to Shell (DataCamp)
Lesson 33: Conda Essentials 
Lesson 34: Hyperparameter Tuning in Python (New – DataCamp)
Lesson 35: Setting up your Environment (New – DataCamp)