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Skills / Javascript

Javascript

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The JavaScript programming language is fun and flexible. It’s one of the core technologies of web development and can be used on both the front-end and the back-end.

JavaScript, often abbreviated as JS, is a programming language that is one of the core technologies of the World Wide Web, alongside HTML and CSS. As of 2022, 98% of websites use JavaScript on the client side for webpage behavior, often incorporating third-party libraries

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Learners Over Time

Average Skill Level Across all Learners

300

Skill Score

0

12th percentile

81

basic knowledge

limited experience

practical application

applied theory

recognized authority

percentile

Average Skill IQ Across all Learners

Expert 201-300

Advanced 101-200

Beginner 0-100

Skills IQ

158

75-300

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21

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25 Pathways for Javascript

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Courses

42 Courses for Javascript

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20 Videos for Javascript

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Assesments

23 Assesments for Javascript

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18 Articles for Javascript

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18 Books for Javascript

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28 Documents for Javascript

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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

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FAQ

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.

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.

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COURSE

Python for Data Science, AI & Development

IBM

COURSE

Python for Data Science, AI & Development

IBM

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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

Jupyter notebooks

Machine learning is the science of getting computers to act without being explicitly programmed.

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Python for Beginners

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.

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.

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.

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