Intro to Python for Data Science (edX 1.1)

Intro to Python for Data Science (edX 1.2)

Intro to Python for Data Science (edX 2.1)

Intro to Python for Data Science (edX 2.2)

Intro to Python for Data Science (edX 2.3)

Intro to Python for Data Science (edX 3.1)

Intro to Python for Data Science (edX 3.2)

Intro to Python for Data Science (edX 3.3)

Intro to Python for Data Science (edX 4.1)

Intro to Python for Data Science (edX 4.2)

Intro to Python for Data Science (edX 4.3)

Lab exercises on Basic Plots with matplotlib.

Intro to Python for Data Science (edX 5.1)

Intro to Python for Data Science (edX 5.2)

Intro to Python for Data Science (edX 5.3)

Lab exercises on Boolean Logic & Control Flow.

Intro to Python for Data Science (edX 6.1)

Intro to Python for Data Science (edX 6.2)

One of the primary goals of any scientist is to find patterns in data and build models to describe, predict, and extract insight from those patterns. The most fundamental of these patterns is a linear relationship between two variables. This course provides an introduction to exploring, quantifying, and modeling linear relationships in data, by demonstrating techniques such as least-squares, linear regression, estimatation, and bootstrap resampling. Here you will apply the most powerful modeling tools in the python data science ecosystem, including scipy, statsmodels, and scikit-learn, to build and evaluate linear models. By exploring the concepts and applications of linear models with python, this course serves as both a practical introduction to modeling, and as a foundation for learning more advanced modeling techniques and tools in statistics and machine learning.

Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.

Learn how to explore, quantify, and model linear relationships in data, by using techniques like least-squares, linear regression, estimation and more.

Introduction to Linear Modeling in Python

The most successful companies today are the ones that know their customers so well that they can anticipate their needs. Data analysts play a key role in unlocking these in-depth insights, and segmenting the customers to better serve them. In this course, you will learn real-world techniques on customer segmentation and behavioral analytics, using a real dataset containing anonymized customer transactions from an online retailer. You will first run cohort analysis to understand customer trends. You will then learn how to build easy to interpret customer segments. On top of that, you will prepare the segments you created, making them ready for machine learning. Finally, you will make your segments more powerful with k-means clustering, in just few lines of code! By the end of this course, you will be able to apply practical customer behavioral analytics and segmentation techniques.

Learn how to segment customers in Python.

Learn techniques to segmment your customer data in Python, including behaviour, transaction, and cohort data. You will learn about RFM analysis and more.

Customer Segmentation in Python

Building good models only succeeds if you have a decent base table to start with. In this course you will learn how to construct a good base table, create variables and prepare your data for modeling. We finish with advanced topics on the matter.

Learn how to prepare and organize your data for predictive analytics.

Learn how to prepare and organize your data for predictive analytics using data preparation and advanced base table concepts with variables in Python.

Intermediate Predictive Analytics in Python

Not long ago, cutting-edge computer vision algorithms couldn’t differentiate between images of cats and dogs. Today, a skilled data scientist equipped with nothing more than a laptop can classify tens of thousands of objects with greater accuracy than the human eye. In this course, you will use TensorFlow 2.6 to develop, train, and make predictions with the models that have powered major advances in recommendation systems, image classification, and FinTech. You will learn both high-level APIs, which will enable you to design and train deep learning models in 15 lines of code, and low-level APIs, which will allow you to move beyond off-the-shelf routines. You will also learn to accurately predict housing prices, credit card borrower defaults, and images of sign language gestures.

Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.

Learn how to use TensorFlow 2.6 and high-level APIs, which enable you to design and train deep learning models and low-level APIs in 15 lines of code.

Introduction to TensorFlow in Python

Bokeh is a powerful Python package for interactive data visualization, enabling you to go beyond static plots and allow stakeholders to modify your visualizations! In this interactive data visualization with Bokeh course, you&apos;ll work with a range of datasets, including stock prices, basketball player statistics, and Australian real-estate sales data. Through hands-on exercises, you’ll build and customize a range of plots, including scatter, bar, line, and grouped bar plots. You&apos;ll also get to grips with configuration tools to change how viewers interact with your plot, discover Bokeh&apos;s custom themes, learn how to generate subplots, and even how to add widgets to your plots!

Learn how to create interactive data visualizations, including building and connecting widgets using Bokeh!

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