Full Stack Data Science Course – Become a Data Scientist

Full Stack Data Science Course – Become a Data Scientist, available at $44.99, has an average rating of 4.2, with 33 lectures, 4 quizzes, based on 104 reviews, and has 1799 subscribers.

You will learn about Engineer data pipelines using the ETL process. Perform statistical and graphical analysis. Execute a 6-stage Machine Learning (ML) workflow. Deploy data models into a production environment using Web APIs. This course is ideal for individuals who are Any beginner, intermediate, or expert developer looking to upskill as a full-stack data scientist. It is particularly useful for Any beginner, intermediate, or expert developer looking to upskill as a full-stack data scientist.

Enroll now: Full Stack Data Science Course – Become a Data Scientist

Summary

Title: Full Stack Data Science Course – Become a Data Scientist

Price: $44.99

Average Rating: 4.2

Number of Lectures: 33

Number of Quizzes: 4

Number of Published Lectures: 33

Number of Published Quizzes: 4

Number of Curriculum Items: 37

Number of Published Curriculum Objects: 37

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Engineer data pipelines using the ETL process.
Perform statistical and graphical analysis.
Execute a 6-stage Machine Learning (ML) workflow.
Deploy data models into a production environment using Web APIs.

Who Should Attend

Any beginner, intermediate, or expert developer looking to upskill as a full-stack data scientist.

Target Audiences

Any beginner, intermediate, or expert developer looking to upskill as a full-stack data scientist.

Get educated and obtain the skills necessary as a Data Scientist to engineer, analyze, build, and deploy intelligent Machine Learning models in this immersive, Full Stack Data Science Coursecreated by The Click Reader.

This course addresses the huge demand for data scientists and covers each stage of the entire data science project lifecycle. You will learn how to collect, clean, and store data into a data warehouse as well as perform Exploratory Data Analysis (EDA) on the collected data using statistical and graphical analysis.

Then onwards, this course will guide you through a six-stage Machine Learning workflow aimed at creating powerful and robust data models from scratch. This course will also take you through the process of deploying the created data model into production using a fast, simple, and extensible Web API framework called Flask.

By the end of this course, you will leave with the necessary skills to make your next data science project a reality.

Why you should take this course?

Updated 2021 course content: All our course content is updated as per the latest technologies and tools available in the market
Practical hands-on knowledge: This course is oriented to providing a step-by-step implementation guide rather than just sticking to the theory.
Guided support: We are always there to guide you through the Q/As so feel free to ask us your queries.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome to the Full-Stack Data Science Course!

Lecture 2: About the Coding Exercises

Lecture 3: How to install Python and Jupyter Notebook?

Chapter 2: Data Engineering

Lecture 1: Introduction to Data Engineering

Lecture 2: Web Scraping

Lecture 3: The ETL Process [Coding Exercise]

Lecture 4: The ETL Process – Extract [Coding Exercise]

Lecture 5: The ETL Process – Transform [Coding Exercise]

Lecture 6: The ETL Process – Load [Coding Exercise]

Chapter 3: Exploratory Data Analysis

Lecture 1: Introduction to Exploratory Data Analysis

Lecture 2: Statistical Analysis

Lecture 3: Graphical Analysis

Lecture 4: Exploratory Data Analysis [Coding Exercise]

Lecture 5: Exploratory Data Analysis [Coding Exercise]

Chapter 4: Data Modeling

Lecture 1: Introduction to Data Modeling

Lecture 2: Dependent and Independent Variables

Lecture 3: Machine Learning Workflow

Lecture 4: Data Modeling [Coding Exercise]

Lecture 5: Data pre-processing

Lecture 6: Data pre-processing [Coding Exercise]

Lecture 7: Data Splitting

Lecture 8: Data Splitting [Coding Exercise]

Lecture 9: Model Building and Training

Lecture 10: Model Building and Training [Coding Exercise]

Lecture 11: Model Evaluation

Lecture 12: Model Evaluation [Coding Exercise]

Lecture 13: Saving and Loading Models [Coding Exercise]

Chapter 5: Model Deployment

Lecture 1: Introduction to Model Deployment

Lecture 2: Web API

Lecture 3: Model Deployment [Coding Exercise]

Lecture 4: Model Deployment [Coding Exercise]

Chapter 6: End of the course

Lecture 1: End of the course

Lecture 2: Bonus Lecture

Instructors

The Click Reader
Teaching Data Science and Machine Learning
Merishna Singh Suwal
Data Scientist at The Click Reader

Rating Distribution

1 stars: 2 votes
2 stars: 6 votes
3 stars: 20 votes
4 stars: 28 votes
5 stars: 48 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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