Data Science with Python

Data Science with Python, available at $19.99, with 9 lectures, 1 quizzes.

You will learn about Become a Certified Data Scientist Add Data Engineer to your CV Master Python with a crash course Implement Machine Learning Algorithms Perform Classification and Regression Grasp practical Natural Language Processing skills with Python Master Data Science and the Machine Learning workflow Gain an understanding on the correct model to choose for a given problem Explore, visualise, pre-process and interpret large datasets Perform statistical analysis on datasets Work on an entire Data Science and Machine Learning project in Python and add it to your Portfolio This course is ideal for individuals who are Anyone can take this course as it includes a Python Programming crash course to build your fundamentals too. or Students seeking to gain practical knowledge and skills in data analysis, visualisation, and machine learning using Python or Students who want to possess a highly sought skillset that will open up new career opportunities. (Data scientist, Data engineer, Data analyst) It is particularly useful for Anyone can take this course as it includes a Python Programming crash course to build your fundamentals too. or Students seeking to gain practical knowledge and skills in data analysis, visualisation, and machine learning using Python or Students who want to possess a highly sought skillset that will open up new career opportunities. (Data scientist, Data engineer, Data analyst).

Enroll now: Data Science with Python

Summary

Title: Data Science with Python

Price: $19.99

Number of Lectures: 9

Number of Quizzes: 1

Number of Published Lectures: 9

Number of Published Quizzes: 1

Number of Curriculum Items: 11

Number of Published Curriculum Objects: 11

Number of Practice Tests: 1

Number of Published Practice Tests: 1

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Become a Certified Data Scientist
Add Data Engineer to your CV
Master Python with a crash course
Implement Machine Learning Algorithms
Perform Classification and Regression
Grasp practical Natural Language Processing skills with Python
Master Data Science and the Machine Learning workflow
Gain an understanding on the correct model to choose for a given problem
Explore, visualise, pre-process and interpret large datasets
Perform statistical analysis on datasets
Work on an entire Data Science and Machine Learning project in Python and add it to your Portfolio

Who Should Attend

Anyone can take this course as it includes a Python Programming crash course to build your fundamentals too.
Students seeking to gain practical knowledge and skills in data analysis, visualisation, and machine learning using Python
Students who want to possess a highly sought skillset that will open up new career opportunities. (Data scientist, Data engineer, Data analyst)

Target Audiences

Anyone can take this course as it includes a Python Programming crash course to build your fundamentals too.
Students seeking to gain practical knowledge and skills in data analysis, visualisation, and machine learning using Python
Students who want to possess a highly sought skillset that will open up new career opportunities. (Data scientist, Data engineer, Data analyst)

Are you interested in learning data science and machine learning with Python? If so, this course is for you! Designed for students and professionals who want to acquire practical knowledge and skills in data science and machine learningusing Python, this course covers various topics that are essential for building a strong foundation in data analysis, visualisation, and machine learning.

The course covers various essential topics such as an overview of data science and machine learning concepts and terminology. Students will follow a crash course on Python Programming for a strong foundation for Data Science. They will learn about data analysis using Numpy and pandas, and data visualization using Matplotlib and seaborn.

Students will also learn about data preprocessing, cleaning, encoding, scaling, and splitting for machine learning. The course covers a range of machine learning techniques, including supervised, unsupervised, and reinforcement learning, and various models such as linear regression, logistics regression, naives bayes, k-nearest neighbours, decision trees and random forests, support vector machines, and k-means clustering.

In addition, students will get hands-on training with scikit-learn to train, evaluate, tune, and validate models. They will also learn about natural language processing techniques, including pre-processing, sentence segmentation, tokenization, POS tagging, stop word removal, lemmatization, and frequency analysis, and visualizing dependencies in NLP data.

The final week of the course involves working on a final project and taking certification exams.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Introduction to Data Science

Chapter 2: Python Crash Course

Lecture 1: Python Fundamentals

Lecture 2: Advanced Python concepts

Lecture 3: Advanced Python programming

Chapter 3: Data Analysis

Lecture 1: Data analysis

Chapter 4: Machine Learning

Lecture 1: Machine learning

Lecture 2: Hands on: Machine Learning

Chapter 5: Natural Language Processing

Lecture 1: Natural Language Processing

Chapter 6: Project

Chapter 7: Exam

Instructors

Nirmal Rampersand
Lead Software Engineer at Unrism

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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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