Intro to Data Science Using Python: Your Best Starting Point

Intro to Data Science Using Python: Your Best Starting Point, available at $19.99, has an average rating of 4.3, with 29 lectures, based on 27 reviews, and has 287 subscribers.

You will learn about Introduction to Data Science Data Science Most Used Packages Data Wrangling Model Development Model Refinement Model Evaluation Techniques This course is ideal for individuals who are Python Developers Who Want To Specialize In the Field Of Data Science or Machine Learning or Beginners in Data Science and Machine Learning Fields Who Are Looking For A Starting Point to This Career or Data Science and Machine Learning Learners Who Are Looking For the Basic Knowledge of the Field It is particularly useful for Python Developers Who Want To Specialize In the Field Of Data Science or Machine Learning or Beginners in Data Science and Machine Learning Fields Who Are Looking For A Starting Point to This Career or Data Science and Machine Learning Learners Who Are Looking For the Basic Knowledge of the Field.

Enroll now: Intro to Data Science Using Python: Your Best Starting Point

Summary

Title: Intro to Data Science Using Python: Your Best Starting Point

Price: $19.99

Average Rating: 4.3

Number of Lectures: 29

Number of Published Lectures: 29

Number of Curriculum Items: 34

Number of Published Curriculum Objects: 34

Original Price: $69.99

Quality Status: approved

Status: Live

What You Will Learn

Introduction to Data Science
Data Science Most Used Packages
Data Wrangling
Model Development
Model Refinement
Model Evaluation Techniques

Who Should Attend

Python Developers Who Want To Specialize In the Field Of Data Science or Machine Learning
Beginners in Data Science and Machine Learning Fields Who Are Looking For A Starting Point to This Career
Data Science and Machine Learning Learners Who Are Looking For the Basic Knowledge of the Field

Target Audiences

Python Developers Who Want To Specialize In the Field Of Data Science or Machine Learning
Beginners in Data Science and Machine Learning Fields Who Are Looking For A Starting Point to This Career
Data Science and Machine Learning Learners Who Are Looking For the Basic Knowledge of the Field

Welcome to “Introduction to Data Science Using Python” where you will set a good foot in the fields of Data Science and Machine Learning.

I’m your instructor Ali Desoki and I start from scratch going clearly over all the points in the course along with hands-on practical exercises and projects to summarize all the skills you’ve learned.

This course is designed for Beginners covering all Aspects of what you need to know to start in the fields of data science and machine learning with practice notebooks which summarize all the skills you’ve learned.

At the end of this course, you will be able to analyze and manipulate data with python and be able to start your career in this field.

This course covers a lot of useful and essential topics including:

Introduction to Data Science

Data Science Most Used Packages

Data Wrangling

Model Development

Model Refinement

Model Evaluation Techniques and more…

The ideal student for this course is someone who looks to start in the mentioned fields from scratch.

All you need to know is Python and basic statistics to start this course.

So what are you waiting for! Enroll now and jump-start your career in Data Science and Machine Learning.

Course Curriculum

Chapter 1: Pre Introduction: Installation and Guides

Lecture 1: Installing Anaconda

Lecture 2: Your Way Around Jupyter Notebooks

Lecture 3: Dealing with The Course's Notebooks

Chapter 2: Review Introduction

Lecture 1: The Problem

Lecture 2: Understanding the Data

Lecture 3: Python Packages for Data Science

Lecture 4: Importing and Exporting Data in Python

Lecture 5: Getting Started Analyzing Data in Python

Chapter 3: Data Wrangling

Lecture 1: Pre-processing Data in Python

Lecture 2: Dealing with Missing Values in Python

Lecture 3: Data Formatting in Python

Lecture 4: Data Normalization in Python

Lecture 5: Binning in Python

Lecture 6: Turning Categorical Variables into Quantitative Variables in Python

Chapter 4: Exploratory Data Analysis

Lecture 1: Exploratory Data Analysis

Lecture 2: Descriptive Statistics

Lecture 3: GroupBy in Python

Lecture 4: Correlation

Lecture 5: Correlation Statistics

Lecture 6: Analysis of Variance ANOVA

Chapter 5: Model Development

Lecture 1: Model Development

Lecture 2: Linear Regression and Multiple Linear Regression

Lecture 3: Model Evaluation Using Visualization

Lecture 4: Polynomial Regression and Pipelines

Lecture 5: Measures for In-Sample Evaluation

Lecture 6: Prediction and Decision Making

Chapter 6: Model Evaluation and Refinement

Lecture 1: Model Evaluation and Refinement

Lecture 2: Overfitting, Underfitting and Model Selection

Lecture 3: Ridge Regression

Instructors

‫Ali Desoki
Engineer and Freelancer

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 3 votes
4 stars: 1 votes
5 stars: 23 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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