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Python Data Science Fundamentals: Getting Started

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Python Data Science Fundamentals: Getting Started, available at $19.99, has an average rating of 4.2, with 22 lectures, based on 114 reviews, and has 13922 subscribers.

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You will learn about Master data analysis using NumPy & Pandas for efficient manipulation Create impactful data visualizations with Matplotlib, conveying insights effectively Gain an introduction to Scikit-learn, building and evaluating predictive models Enhancing practical skills in data analysis, visualization, & basic machine learning techniques This course is ideal for individuals who are Beginners with basic programming experience looking to enter the field of data science. or Aspiring data analysts or data scientists seeking to build a strong foundation in Python for data manipulation and analysis or Professionals from diverse backgrounds aiming to enhance their data analysis and visualization skills or Individuals interested in understanding the basics of machine learning and its applications in real-world scenarios It is particularly useful for Beginners with basic programming experience looking to enter the field of data science. or Aspiring data analysts or data scientists seeking to build a strong foundation in Python for data manipulation and analysis or Professionals from diverse backgrounds aiming to enhance their data analysis and visualization skills or Individuals interested in understanding the basics of machine learning and its applications in real-world scenarios.

Enroll now: Python Data Science Fundamentals: Getting Started

Summary

Title: Python Data Science Fundamentals: Getting Started

Price: $19.99

Average Rating: 4.2

Number of Lectures: 22

Number of Published Lectures: 22

Number of Curriculum Items: 22

Number of Published Curriculum Objects: 22

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

  • Master data analysis using NumPy & Pandas for efficient manipulation
  • Create impactful data visualizations with Matplotlib, conveying insights effectively
  • Gain an introduction to Scikit-learn, building and evaluating predictive models
  • Enhancing practical skills in data analysis, visualization, & basic machine learning techniques

Who Should Attend

  • Beginners with basic programming experience looking to enter the field of data science.
  • Aspiring data analysts or data scientists seeking to build a strong foundation in Python for data manipulation and analysis
  • Professionals from diverse backgrounds aiming to enhance their data analysis and visualization skills
  • Individuals interested in understanding the basics of machine learning and its applications in real-world scenarios

Target Audiences

  • Beginners with basic programming experience looking to enter the field of data science.
  • Aspiring data analysts or data scientists seeking to build a strong foundation in Python for data manipulation and analysis
  • Professionals from diverse backgrounds aiming to enhance their data analysis and visualization skills
  • Individuals interested in understanding the basics of machine learning and its applications in real-world scenarios

Course Description: Python for Machine Learning: A Beginner’s Kickstart

Welcome to the Python for Machine Learning: A Beginner’s Kickstart course! This introductory course is designed to provide you with the fundamental skills and knowledge needed to dive into the exciting world of machine learning using Python.

Course Overview: In this course, you’ll gain hands-on experience with essential Python libraries for data manipulation, analysis, visualization, and machine learning. The course focuses on three core libraries: NumPy, Pandas, Matplotlib, and Scikit-learn. These libraries are the backbone of data science and machine learning in Python, and mastering them will give you a solid foundation to explore more advanced machine learning topics.

What You’ll Learn:

  • NumPy: Learn how to efficiently work with arrays and matrices, perform mathematical operations, and manipulate data in Python using NumPy.

  • Pandas: Discover the power of Pandas for data wrangling and manipulation, from handling data frames to performing data analysis and cleaning.

  • Matplotlib: Explore data visualization techniques using Matplotlib to create meaningful plots and charts.

  • Scikit-learn: Dive into the world of machine learning with Scikit-learn. Understand the basics of data preprocessing, model building, training, evaluation, and prediction.

Launch Your Data Science Journey: Embark on a transformative learning journey that will equip you with the fundamental skills and knowledge needed to excel in the field of data science. With Python at the heart of this course, you’ll harness the power of NumPy, Pandas, Matplotlib, and Scikit-learn to become a proficient data scientist.

Why Start Here: This course is designed to be your first step into the thrilling world of data science and machine learning. We’ll take you on a beginner-friendly adventure, focusing on essential Python libraries: NumPy for numerical computing, Pandas for data manipulation, Matplotlib for data visualization, and Scikit-learn for introductory machine learning.

Build Essential Skills: Discover the power of Python in data science as we guide you through the fundamental concepts of each library. By the end of the course, you’ll have a solid understanding of how to perform basic data analysis, visualization, and even create simple machine learning models.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to Course : Python Data Science Fundamentals: Getting Started

Lecture 2: Welcome Note by the Author

Lecture 3: Prerequisite for This Course: Embark on Your Data Science Journey

Chapter 2: Setting up the Environment

Lecture 1: Download and Install Anaconda Environment

Chapter 3: Introduction to NumPy: Foundations of Numerical Computing

Lecture 1: Introduction to NumPy

Lecture 2: Create a NumPy ndarray Object

Lecture 3: NumPy Array Indexing

Lecture 4: NumPy Array Slicing [ Numerical Python ]

Lecture 5: NumPy Array Copy vs View [ Numerical Python ]

Lecture 6: NumPy Array Reshaping [ Numerical Python ]

Lecture 7: NumPy Array Iterating [ Numerical Python ]

Chapter 4: Introduction to Pandas: A Powerful Data Analysis Library

Lecture 1: Pandas Introduction

Lecture 2: Pandas DataFrames

Lecture 3: Pandas Read CSV & Analyzing DataFrames

Lecture 4: Pandas – Cleaning Empty Cells

Lecture 5: Pandas – Removing Duplicates

Lecture 6: Pandas – Data Correlations

Chapter 5: Matplotlib Tutorial

Lecture 1: Matplotlib Tutorial Part 1

Chapter 6: Scikit-learn Essentials: Python's ML Powerhouse [Getting started ]

Lecture 1: Python Machine Learning: Scikit-Learn [Getting started ]

Lecture 2: Python Machine Learning: Scikit-Learn [Data Preprocessing ]

Lecture 3: Python Machine Learning: Scikit-Learn [ Model Training ]

Lecture 4: Python Machine Learning: Scikit-Learn [Model Building & Evaluation ]

Instructors

Rating Distribution

  • 1 stars: 2 votes
  • 2 stars: 3 votes
  • 3 stars: 27 votes
  • 4 stars: 36 votes
  • 5 stars: 46 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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