LearnQuest
Introduction to Data Science and scikit-learn in Python
LearnQuest

Introduction to Data Science and scikit-learn in Python

This course is part of AI for Scientific Research Specialization

Taught in English

Some content may not be translated

Sabrina Moore
Rajvir Dua
Neelesh Tiruviluamala

Instructors: Sabrina Moore

5,405 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

3.8

(40 reviews)

Beginner level

Recommended experience

13 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Employ artificial intelligence techniques to test hypothesis in Python

  • Apply a machine learning model combining Numpy, Pandas, and Scikit-Learn

Details to know

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Assessments

9 quizzes

Course

Gain insight into a topic and learn the fundamentals

3.8

(40 reviews)

Beginner level

Recommended experience

13 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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This course is part of the AI for Scientific Research Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 4 modules in this course

In this module, we'll get ourselves started with Programming in Python. After becoming familiar with Python and the Jupyter Notebook interface, we'll dive into some basic coding paradigms such as variables, loops, and functions. We'll also cover data structures in the form of lists and dictionaries. We'll go through one of the most useful things in your Python arsenal - importing and using modules effectively. Finally, we'll introduce scikit-learn and walk through a classification problem to predict the presence/absence of cancer from health data.

What's included

9 videos5 readings2 quizzes4 programming assignments1 discussion prompt5 ungraded labs

In this module, we'll become familiar with the two most important packages for data science: Numpy and Pandas. We'll begin by learning the differences between the two packages. Then, we'll get ourselves familiar with np arrays and their functionalities. Adding text turns our arrays into tables, and gives rise to the Pandas module. After a basic introduction, we'll end with a series of important data manipulation tools such as indexing, merging/combining datasets, and reshaping data.

What's included

8 videos5 readings4 quizzes1 programming assignment1 discussion prompt2 ungraded labs

In this module, we'll work from the ground up to build and test our hypothesis. Learning both the theory and the code, we'll learn to test our predictions with different types of machine learning algorithms. We'll start by going through some of the necessary data preprocessing steps to orient ourselves. Getting familiar with using the Scikit-Learn library starts with the documentation. From there, we'll load in a dataset and analyze some of its most basic properties. Finally, we'll import and use models to make a prediction.

What's included

6 videos3 readings3 quizzes1 programming assignment1 discussion prompt1 ungraded lab

In the final project, we'll try and predict the presence of heart disease using patient data. We'll load in data, create new features, and apply a machine learning algorithm using scikit-learn.

What's included

1 video1 programming assignment1 ungraded lab

Instructors

Instructor ratings
4.0 (12 ratings)
Sabrina Moore
LearnQuest
3 Courses60,567 learners

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LearnQuest

Recommended if you're interested in Data Analysis

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3.8

40 reviews

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DH
5

Reviewed on Apr 4, 2022

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Reviewed on Nov 9, 2021

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