Python for Data Science Bootcamp 2023- From Zero to Hero

Python for Data Science Bootcamp 2023: From Zero to Hero, available at $74.99, has an average rating of 4.55, with 126 lectures, based on 157 reviews, and has 955 subscribers.

You will learn about Learn to use Pandas for Data Analysis Use SciKit-Learn for Machine Learning Tasks Learn Static and Interactive Visualization with Pandas NLP: Binary Text Classification Use Python for Data Science and Machine Learning Implement Machine Learning Algorithms Data Cleaning with Python Basic Web Scraping with Python ChatGPT for data science This course is ideal for individuals who are Beginners who want to learn Data Science with Python from scratch It is particularly useful for Beginners who want to learn Data Science with Python from scratch.

Enroll now: Python for Data Science Bootcamp 2023: From Zero to Hero

Summary

Title: Python for Data Science Bootcamp 2023: From Zero to Hero

Price: $74.99

Average Rating: 4.55

Number of Lectures: 126

Number of Published Lectures: 126

Number of Curriculum Items: 126

Number of Published Curriculum Objects: 126

Original Price: $27.99

Quality Status: approved

Status: Live

What You Will Learn

Learn to use Pandas for Data Analysis

Use SciKit-Learn for Machine Learning Tasks

Learn Static and Interactive Visualization with Pandas

NLP: Binary Text Classification

Use Python for Data Science and Machine Learning

Implement Machine Learning Algorithms

Data Cleaning with Python

Basic Web Scraping with Python

ChatGPT for data science

Who Should Attend

Beginners who want to learn Data Science with Python from scratch

Target Audiences

Beginners who want to learn Data Science with Python from scratch

Welcome to the Python for Data Science Bootcamp: From Zero to Hero. In this course, we’re going to learn how to use Python for Data Science. In this practical course, we’ll learn how to collect data, clean data, make visualizations and build a machine learning model using Python.

The main goal of this course is to take your programming and analytical skills to the next level to build your career in Data Science. To achieve this goal, we’re going to solve hundreds of exercises and many cool projects that will help you put into practice all the programming concepts used in Data Science.

We’ll learn the top Python Libraries used in Data Science such as Pandas, Numpy and Scikit Learn and we will use them to learn to solve tasks data scientists  deal with on a daily basis (Data Cleaning, Data Visualization, Data Collection and Model Building)

This course covers 4 main sections.

1. Python for Data Science Crash Course: In the first section, we’ll learn all the Python core concepts you need to know for Data Science. We’ll learn how to use variables, lists, dictionaries and more.

2. Python for Data Analysis: We’ll learn Python libraries used for data analysis such as Pandas and Numpy. Both are great tools for exploring and working with data. We’ll use Pandas and Numpy to deal with data science tasks such as cleaning and preparing data.

3. Python for Data Visualization: In the third section, we’ll learn how to make static and interactive visualizations with Pandas. Also, I’ll show you some techniques to properly make data visualization.

4. Machine Learning with Python: In the fourth section, we’ll learn scikit-learn by solving a text classification problem in Python. This is the most popular machine learning library in Python and we’ll not only learn how to implement machine learning algorithms in Python but also we’ll learn the core concepts behind the most common algorithms using practical examples.

Bonus (Basic Web Scraping with Python): Remember that at the end of this course, there’s a bonus section where you will learn web scraping. Web scraping allows us to build our own dataset by extracting data from websites. This is a must-have skill for data scientists and we’ll learn this technique with the Beautiful Soup library.

What makes this course different from the others, and why you should enroll?

This is the most updated and complete Python course for data science.

Tired of ton of tutorials but no way to practice what you’ve learned? In this course, you will find lots of exercises to learn Python by solving problems.

This is the most project-based course you will find. We will solve 4 projects to put into practice all the concepts we will learn in this course

Learn how to use ChatGPT for data science

30 days money back guarantee by Udemy

After finishing this course, you will be able to do data analysis, create data visualization and build machine learning models with Python.

Join me now and go from zero coding skills to data scientist!

Course Curriculum

Chapter 1: Installation and Setup

Lecture 1: Installing Python and Jupyter Notebook through Anaconda

Lecture 2: Jupyter Notebook Interface

Lecture 3: Cell Types and Modes in Jupyter Notebook

Lecture 4: Most Common Keyboard Shortcuts in Jupyter Notebook

Lecture 5: Read This Before You Start (+Cheat Sheet for The Course)

Chapter 2: Python Crash Course (Optional)

Lecture 1: Optional: Python Crash Course

Lecture 2: Hello World

Lecture 3: Data Types

Lecture 4: Variables

Lecture 5: Lists

Lecture 6: Dictionary

Lecture 7: If Statement

Lecture 8: For loop

Lecture 9: Functions

Lecture 10: Modules

Chapter 3: Introduction to Pandas and Numpy

Lecture 1: Section Overview

Lecture 2: Introduction to Pandas

Lecture 3: How to Create a Dataframe

Lecture 4: Different Ways to Display a Dataframe

Lecture 5: Basic Attributes, Functions and Methods

Lecture 6: Selecting One Column from a Dataframe

Lecture 7: Selecting Two or More Columns from a Dataframe

Lecture 8: Add New Column to a Dataframe (Simple Assignment)

Lecture 9: Add New Column to a Dataframe with assign() and insert()

Lecture 10: Operations on Dataframes (columns and rows)

Lecture 11: The value_counts() method

Lecture 12: Important Note

Lecture 13: Sort a Dataframe with sort_values()

Lecture 14: The set_index() and sort_index() methods

Lecture 15: Rename Columns and Index with rename()

Lecture 16: Exercise for this Section

Chapter 4: Project #1 – Web Scraping with Pandas

Lecture 1: Part 1

Lecture 2: Part 2

Lecture 3: Part 3

Chapter 5: Filtering Data

Lecture 1: Filter a Dataframe Based on 1 Condition

Lecture 2: Creating a Conditional Column from 2 Choices: np.where()

Lecture 3: Filter a Dataframe Based on 2 or More Conditions: &, |

Lecture 4: Creating a Conditional Column from More Than 2 Choices: np.select()

Lecture 5: The isin() Method

Lecture 6: Find Duplicate Rows with the duplicated() method (keep first, last, and false)

Lecture 7: Drop Duplicate Elements with the .drop_duplicates() Method

Lecture 8: Get and Count Unique Values with the unique() and nunique() Methods

Chapter 6: Data Extraction

Lecture 1: Differences between the loc() and iloc() methods

Lecture 2: First Look at The Dataset: Setting Index and Selecting Columns

Lecture 3: Selecting elements by index label with loc()

Lecture 4: Selecting elements by index position with iloc()

Lecture 5: Set New Value for a Cell In a Dataframe

Lecture 6: Drop Rows or Columns from a DataFrame

Lecture 7: Create Random Sample with the sample() Method

Lecture 8: Filter a dataFrame with the query() method

Lecture 9: The apply() method

Lecture 10: Lambda function + apply() method

Lecture 11: Make a Copy of a Dataframe with copy() (Deep Copy vs Shallow Copy)

Chapter 7: Reshaping and Pivoting Dataframes

Lecture 1: Introduction to Pivot Tables

Lecture 2: The .pivot() method

Lecture 3: The pivot_table() method

Chapter 8: Project #2: Making Static and Interactive Data Visualization

Lecture 1: Project Overview (+ Exercise)

Lecture 2: Dataset Overview and Making Pivot Table

Lecture 3: Lineplot

Lecture 4: Barplot

Lecture 5: Piechart

Lecture 6: Boxplot

Lecture 7: Histogram

Lecture 8: Scatterplot

Lecture 9: Save Plot and Export Pivot Table

Lecture 10: Interactive Visualization with Pandas

Chapter 9: GroupBy and Aggregate Function

Lecture 1: Dataset Overview

Lecture 2: The agg() method

Lecture 3: The Split-Apply-Combine Strategy

Lecture 4: The groupby() method

Lecture 5: The groupby() and agg() method

Lecture 6: The groupby() and lambda function

Lecture 7: The filter() method

Chapter 10: Merging and Concatenating Dataframes

Lecture 1: Exploring The Dataset

Lecture 2: Concatenate Vertically

Lecture 3: Concatenate Horizontally

Lecture 4: Inner Joins

Lecture 5: Full Join and Exclusive Full Join

Lecture 6: Left Join and Exclusive Left Join

Lecture 7: Right Join and and Exclusive Right Join

Chapter 11: Regular Expressions

Lecture 1: Section Overview

Lecture 2: Regex Metacharacters and Flags

Lecture 3: Quantifiers (+Greedy and Lazy Matches)

Lecture 4: More Metacharacters

Lecture 5: search() and findall()

Lecture 6: Exercises

Chapter 12: Project #3: Data Cleaning with Pandas

Lecture 1: Dataset Overview

Lecture 2: Identify Missing Data with the isnull() Method

Instructors

Frank Andrade
Data Scientist

Rating Distribution

1 stars: 0 votes

2 stars: 4 votes

3 stars: 11 votes

4 stars: 52 votes

5 stars: 90 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!