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04/01/2023

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03/27/2023

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03/15/2023

What is the purpose of a constructor in Java?

03/08/2023

What is a binary tree and how does it work?

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02/21/2023

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What is the purpose of the Seaborn library in Python?

04/02/2023

What is the purpose of the Pandas library in Python?

04/02/2023

What is a decision tree and how does it work?

04/01/2023

What is a neural network and how does it work?

03/27/2023

What is a random forest and how does it work?

03/15/2023

What is the purpose of a constructor in Java?

03/08/2023

What is a binary tree and how does it work?

03/05/2023

What is the purpose of version control systems like Git?

02/21/2023

How can you optimize the performance of a website?

02/12/2023

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Python for Data Science, AI & Development

Python for Data Science

Eliott Hanson

Offered By

$120

English

Certificate

2 Months

9th Jan, 2023

Beginner

Arabic, French, Portuguese, Italian, German, Russian, English, Spanish, Korean, Japanese

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About

Instructors

Syllabus

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About this Course

This course covers foundational data science tools and techniques, including getting, cleaning, and exploring data, programming in Python, and conducting reproducible research.

Show all

The five courses in this specialization are the very same courses that make up the first half of the Data Science Specialization. This specialization is presented for learners who want to start and complete the foundational part of the curriculum first, before moving onto the more advanced topics in Data Science: Statistics and Machine Learning.

WHAT YOU WILL LEARN

Use Python to clean, analyze and visualize data.

Use GitHub to manage data science projects.

Learn how to ask the right questions, obtain data,

Set up Python, Github and other useful tools

SKILLS YOU WILL GAIN

Python

Machine Learning

GitHub

Exploratory Data Analysis

Instructors

Eliott Hanson, Phd

Computer Science Professor

School of Computer Science

Jean Long

Computer Science Professor

School of Computer Science

Syllabus

Introduction

Welcome to Introduction to Data Science with Python!

Syllabus Quiz (8 Questions)

Important Pre-Course Survey

Section 1: Linear Regression

Pre-reading

1.1 Introduction to Regression

1.2 Error Evaluation and Model Comparison

1.3 Linear Regression

Section 2: Multiple and Polynomial Regression

Pre-reading

2.1 Multiple Regression

2.2 Techniques for Multilinear Modeling

2.3 Polynomial Regression

Section 3: Model Selection and Cross-Validation

Pre-reading

3.1 Model Selection

3.2 Cross-Validation

Section 4: Bias, Variance, and Hyperparameters

Pre-reading

4.1 Introductory Exercise

4.2 Bias and Variance

4.3 Ridge and LASSO

Section 5: Classification and Logistic Regression

Pre-reading

5.1 Classification and kNN

5.2 Logistic Regression

Section 6: Multi-logstic Regression and Missingness

Pre-reading

6.1 Multinomial Logistic Regression

6.2 Missingness

Section 7: Bootstrap, Confidence Intervals, and Hypothesis Testing

Pre-reading

7.1 Inference in Linear Regression

7.2 Bootstrap and Confidence Intervals

7.3 Evaluating Predictor Significance

Section 8: Capstone Project

No Pre-reading

8.1 Introduction

8.2 Why is accuracy not enough?

8.3 Bayes Threshold

Wrap-up

Important post-course surveys

Review Videos

Week 3

Final Notes

Reviews

Inez Sierra

"It has been an amazing journey. I learnt a lot from it and I will go through different topics especially, functions and OOPS to have a better understanding."

Inez Sierra

"It has been an amazing journey. I learnt a lot from it and I will go through different topics especially, functions and OOPS to have a better understanding."

Emir O'Sullivan

"It has been an amazing journey. I learnt a lot from it and I will go through different topics especially, functions and OOPS to have a better understanding."

Frequently Asked Questions

When will I have access to the lectures and assignments?

Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

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 are the requirements to attend this course?

Access to a computer with an internet connection.

PDF

URL

DOCX

TXT

Audio

Video

YouTube

GitHub

Course

From URL

Thank you! Your submission has been received!

Oops! Something went wrong while submitting the form.

Add Resource

COURSE

Python for Data Science, AI & Development

IBM

PATHWAY

Machine Learning Foundations

Google

VIDEO

Introduction to Artificial Intelligence

Stanford University

COURSE

Advanced Python for Machine Learning

Google

PATHWAY

Data Visualization with Javascript

IBM

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Oops! Something went wrong while submitting the form.

Python for Beginners

Eliott Hanson

Offered By

20 Minutes

View this Video

About this Video

This video covers foundational data science tools and techniques, including getting, cleaning, and exploring data, programming in Python, and conducting reproducible research.

Show all

Instructors

Eliott Hanson, Phd

Computer Science Professor

School of Computer Science

Jean Long

Computer Science Professor

School of Computer Science

Syllabus

Introduction

Welcome to Introduction to Data Science with Python!

Syllabus Quiz (8 Questions)

Important Pre-Course Survey

Section 1: Linear Regression

Pre-reading

1.1 Introduction to Regression

1.2 Error Evaluation and Model Comparison

1.3 Linear Regression

Section 2: Multiple and Polynomial Regression

Pre-reading

2.1 Multiple Regression

2.2 Techniques for Multilinear Modeling

2.3 Polynomial Regression

Section 3: Model Selection and Cross-Validation

Pre-reading

3.1 Model Selection

3.2 Cross-Validation

Section 4: Bias, Variance, and Hyperparameters

Pre-reading

4.1 Introductory Exercise

4.2 Bias and Variance

4.3 Ridge and LASSO

Section 5: Classification and Logistic Regression

Pre-reading

5.1 Classification and kNN

5.2 Logistic Regression

Section 6: Multi-logstic Regression and Missingness

Pre-reading

6.1 Multinomial Logistic Regression

6.2 Missingness

Section 7: Bootstrap, Confidence Intervals, and Hypothesis Testing

Pre-reading

7.1 Inference in Linear Regression

7.2 Bootstrap and Confidence Intervals

7.3 Evaluating Predictor Significance

Section 8: Capstone Project

No Pre-reading

8.1 Introduction

8.2 Why is accuracy not enough?

8.3 Bayes Threshold

Wrap-up

Important post-course surveys

Review Videos

Week 3

Final Notes

Reviews

Inez Sierra

"It has been an amazing journey. I learnt a lot from it and I will go through different topics especially, functions and OOPS to have a better understanding."

Inez Sierra

"It has been an amazing journey. I learnt a lot from it and I will go through different topics especially, functions and OOPS to have a better understanding."

Emir O'Sullivan

"It has been an amazing journey. I learnt a lot from it and I will go through different topics especially, functions and OOPS to have a better understanding."

Frequently Asked Questions

When will I have access to the lectures and assignments?

What will I get if I subscribe to this Specialization?

What are the requirements to attend this course?

Access to a computer with an internet connection.