This course is part of the AI for Scientific Research Specialization

4.0
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33 ratings

Sabrina Moore +2 more instructors

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Offered By

AI for Scientific Research SpecializationLearnQuest

About this Course

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This course will teach you how to leverage the power of Python and artificial intelligence to create and test hypothesis. We'll start for the ground up, learning some basic Python for data science before diving into some of its richer applications to test our created hypothesis. We'll learn some of the most important libraries for exploratory data analysis (EDA) and machine learning such as Numpy, Pandas, and Sci-kit learn. After learning some of the theory (and math) behind linear regression, we'll go through and full pipeline of reading data, cleaning it, and applying a regression model to estimate the progression of diabetes. By the end of the course, you'll apply a classification model to predict the presence/absence of heart disease from a patient's health data.

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Earn a Certificate upon completion

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Course 1 of 4 in the

AI for Scientific Research Specialization

Beginner Level

None

Approx. 13 hours to complete

English

What you will learn

Employ artificial intelligence techniques to test hypothesis in Python
Apply a machine learning model combining Numpy, Pandas, and Scikit-Learn

Skills you will gain

Data Science
Machine Learning
regression
Statistical Hypothesis Testing
medical data

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 1 of 4 in the

AI for Scientific Research Specialization

Beginner Level

None

Approx. 13 hours to complete

English

Instructors

Instructor rating

3.88/5 (10 Ratings)

Offered by

LearnQuest

LearnQuest is the preferred training partner to the world’s leading companies, organizations, and government agencies. Our team boasts 20+ years of experience designing, developing and delivering a full suite industry-leading technology education classes and training solutions across the globe. Our trainers, equipped with expert industry experience and an unparalleled commitment to quality, facilitate classes that are offered in various delivery formats so our clients can obtain the training they need when and where they need it.

Syllabus - What you will learn from this course

Week1

Week 1

3 hours to complete

Introduction to Python Programming for Hypothesis Testing

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.

3 hours to complete

9 videos (Total 30 min), 5 readings, 6 quizzes

9 videos

Course Introduction55sModule Introduction17sPython and Jupyter Notebook Basics4mSetting Up the Environment2mLists2mDictionaries3mLoops5mFunctions6mLibraries and Modules4m

5 readings

A Note on Coding Alongside the Videos1mJupyter Notebook Basics10mPython Docs: Data Structures10mList Comprehensions10mKeyword Arguments10m

2 practice exercises

Practice Quiz: Data Structures10mIntroduction to Python30m

Week2

Week 2

5 hours to complete

Creating a Hypothesis: Numpy, Pandas, and Scikit-Learn

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.

5 hours to complete

8 videos (Total 35 min), 5 readings, 5 quizzes

8 videos

Module Introduction30sDeep Dive into Numpy (Part I)3mDeep Dive Into Numpy (Part II)2mIntroduction to Pandas5mPandas Deep Dive4mJoining and Manipulating Dataframes (I)4mJoining and Manipulating Dataframes (II)5mJoining and Manipulating Dataframes (III)6m

5 readings

Numpy, Pandas, and scikit-learn10m10 min to Pandas10mPandas Docs: Reshaping and Combining Data20mSplit-Apply-Combine15mPython Docs: Sort_values()10m

4 practice exercises

Practice Quiz: Numpy Basics15mPractice Quiz: Pandas10mPractice Quiz: Combining Data15mNumpy and Pandas Quiz30m

Week3

Week 3

2 hours to complete

Scikit-Learn Revisited: ML for Hypothesis Testing

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.

2 hours to complete

6 videos (Total 22 min), 3 readings, 4 quizzes

6 videos

Module Introduction40sUsing the Scikit-Learn Docs2mLoading and Analyzing datasets6mApplying Linear Regression (I)5mMath of Machine Learning3mScikit-Learn Conclusion2m

3 readings

Introduction to scikit-learn15mTrain/Test Split and Cross-Validation10mLeast Squares10m

3 practice exercises

Practice Quiz: Using the scikit-learn Docs10mPractice Quiz: Math of Linear Regression10mLinear Regression with Scikit-Learn30m

Week4

Week 4

3 hours to complete

Using Classification to Predict the Presence of Heart Disease

3 hours to complete

1 video (Total 4 min)

Reviews

4.0

12 reviews

5 stars
54.54%
4 stars
15.15%
3 stars
12.12%
2 stars
9.09%
1 star
9.09%

TOP REVIEWS FROM INTRODUCTION TO DATA SCIENCE AND SCIKIT-LEARN IN PYTHON

by DHApr 4, 2022

The topic is great, and the linkage and references provided are valuable.

The hands-on quiz should be supported with better instructions and descriptions regarding what to do.

by AGNov 27, 2021

Good introduction. A bit too short for a 4-week course. The autograder is not very good, and some solutions are wrong.

by RZNov 9, 2021

meskipun agak eror dalam lab penugasan tapi alhamdulillah sudah bisa

by CTJan 30, 2022

It could be better if we can see where we did wrong after each assignment. Good and well-paced course otherwise

View all reviews

About the AI for Scientific Research Specialization

In the AI for Scientific Research specialization, we'll learn how to use AI in scientific situations to discover trends and patterns within datasets. Course 1 teaches a little bit about the Python language as it relates to data science. We'll share some existing libraries to help analyze your datasets. By the end of the course, you'll apply a classification model to predict the presence or absence of heart disease from a patient's health data. Course 2 covers the complete machine learning pipeline, from reading in, cleaning, and transforming data to running basic and advanced machine learning algorithms.In the final project, we'll apply our skills to compare different machine learning models in Python. In Course 3, we will build on our knowledge of basic models and explore more advanced AI techniques. We’ll describe the differences between the two techniques and explore how they differ. Then, we’ll complete a project predicting similarity between health patients using random forests. In Course 4, a capstone project course, we'll compare genome sequences of COVID-19 mutations to identify potential areas a drug therapy can look to target. By the end, you'll be well on your way to discovering ways to combat disease with genome sequencing.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Introduction to Data Science and scikit-learn in Python

About this Course

What you will learn

Skills you will gain

Instructors

Sabrina Moore

Rajvir Dua

Neelesh Tiruviluamala

Offered by

LearnQuest

Syllabus - What you will learn from this course

Introduction to Python Programming for Hypothesis Testing

Creating a Hypothesis: Numpy, Pandas, and Scikit-Learn

Scikit-Learn Revisited: ML for Hypothesis Testing

Using Classification to Predict the Presence of Heart Disease

Reviews

TOP REVIEWS FROM INTRODUCTION TO DATA SCIENCE AND SCIKIT-LEARN IN PYTHON

About the AI for Scientific Research Specialization

Frequently Asked Questions

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Introduction to Data Science and scikit-learn in Python

About this Course

What you will learn

Skills you will gain

Instructors

Sabrina Moore

Rajvir Dua

Neelesh Tiruviluamala

Offered by

LearnQuest

Syllabus - What you will learn from this course

Introduction to Python Programming for Hypothesis Testing

Creating a Hypothesis: Numpy, Pandas, and Scikit-Learn

Scikit-Learn Revisited: ML for Hypothesis Testing

Using Classification to Predict the Presence of Heart Disease

Reviews

TOP REVIEWS FROM INTRODUCTION TO DATA SCIENCE AND SCIKIT-LEARN IN PYTHON

About the AI for Scientific Research Specialization

Frequently Asked Questions

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