Learn Python for Data Science from Scratch -with 10 Projects

Learn Python for Data Science from Scratch -with 10 Projects, available at $19.99, has an average rating of 4.88, with 75 lectures, based on 4 reviews, and has 26 subscribers.

You will learn about Foundations of Python Programming for Data Science: Students will gain a solid understanding of Python, the programming language widely used in the field of da Data Manipulation and Analysis Skills: Participants will acquire proficiency in handling data by exploring various data types (integers, floats, strings, boole Visualization Techniques with Matplotlib: Students will develop the ability to visually represent data using Matplotlib, a popular data visualization library. Introduction to Machine Learning with Scikit-Learn: The course will introduce students to the fundamentals of machine learning using the Scikit-Learn library. By the end of the course, students will have acquired a strong foundation in Python programming, data manipulation, visualization, and the basics of machine lea This course is ideal for individuals who are This course is designed for individuals who are interested in entering the field of data science and want to build a strong foundation in Python programming for data analysis and machine learning It is particularly useful for This course is designed for individuals who are interested in entering the field of data science and want to build a strong foundation in Python programming for data analysis and machine learning.

Enroll now: Learn Python for Data Science from Scratch -with 10 Projects

Summary

Title: Learn Python for Data Science from Scratch -with 10 Projects

Price: $19.99

Average Rating: 4.88

Number of Lectures: 75

Number of Published Lectures: 75

Number of Curriculum Items: 75

Number of Published Curriculum Objects: 75

Original Price: $79.99

Quality Status: approved

Status: Live

What You Will Learn

Foundations of Python Programming for Data Science: Students will gain a solid understanding of Python, the programming language widely used in the field of da

Data Manipulation and Analysis Skills: Participants will acquire proficiency in handling data by exploring various data types (integers, floats, strings, boole

Visualization Techniques with Matplotlib: Students will develop the ability to visually represent data using Matplotlib, a popular data visualization library.

Introduction to Machine Learning with Scikit-Learn: The course will introduce students to the fundamentals of machine learning using the Scikit-Learn library.

By the end of the course, students will have acquired a strong foundation in Python programming, data manipulation, visualization, and the basics of machine lea

Who Should Attend

This course is designed for individuals who are interested in entering the field of data science and want to build a strong foundation in Python programming for data analysis and machine learning

Target Audiences

This course is designed for individuals who are interested in entering the field of data science and want to build a strong foundation in Python programming for data analysis and machine learning

Unlock the Power of Data with Python!

Embark on a transformative journey into the dynamic world of data science with our Udemy course, “Learn Python for Data Science from Scratch.” Whether you’re a coding novice or looking to elevate your skills, this course is your gateway to mastering Python and unleashing its potential in data analysis and machine learning.

What You’ll Learn:

Python Foundations: Grasp the essentials with an in-depth introduction to Python and the Jupyter Notebook, culminating in a hands-on project to create a personalized calculator program.

Data Manipulation Mastery: Dive into data types, structures, and learn the art of sorting with a practical project, setting the stage for your journey into the heart of data science.

Visualization Wizardry: Harness the power of Matplotlib to craft captivating visualizations, creating line charts and bar charts from real-world datasets.

Machine Learning Magic: Explore Scikit-Learn to understand supervised and unsupervised learning, predict housing prices, customer behavior, and more. Elevate your skills with hands-on projects that bridge theory and application.

Projects: Conclude your learning adventure with 10 captivating projects. From data preparation and model training to evaluation and deployment, you’ll showcase your newfound skills in a real-world scenario.

Who Is This For?

Beginners eager to enter the exciting field of data science.

Professionals looking to transition into data-driven roles.

Students and graduates seeking practical skills for their careers.

Enthusiasts exploring Python’s potential in data analysis and machine learning.

Why Enroll?

Structured curriculum designed for seamless learning progression.

Real-world projects to reinforce theoretical concepts.

Engaging and interactive content for an immersive learning experience.

Join a supportive community of learners passionate about data science.

Ready to embark on your data science journey? Enroll now and equip yourself with the tools to transform raw data into actionable insights!

Course Curriculum

Chapter 1: Introduction to Python and the Jupyter Notebook

Lecture 1: Introduction

Lecture 2: What is Python?

Lecture 3: Overview of the Jupyter Notebook

Lecture 4: The Print Function

Lecture 5: Basic Arithmetic Functions

Lecture 6: Variables

Lecture 7: Project 1

Lecture 8: Project 1 (Solution)

Chapter 2: Data Types and Structures in Python

Lecture 1: Strings

Lecture 2: Strings Numerical Data Types

Lecture 3: Lists

Lecture 4: Tuples

Lecture 5: Dictionaries

Lecture 6: Project 2

Lecture 7: Project 2 Solution

Chapter 3: Control Flow in Python

Lecture 1: Overview of Control Flow

Lecture 2: Conditional Statements

Lecture 3: For Loops

Lecture 4: While loops

Lecture 5: Project 3

Lecture 6: Project 3 Solution

Chapter 4: Functions and Modules in Python

Lecture 1: Functions

Lecture 2: Lambda Functions

Lecture 3: Modules

Lecture 4: Project 4

Lecture 5: Project 4 Solution

Chapter 5: Introduction to Numpy

Lecture 1: Introduction to Numpy

Lecture 2: Creating arrays in Numpy

Lecture 3: Indexing and Slicing Arrays

Lecture 4: Copy and View in Numpy

Lecture 5: Shape and reshaping arrays

Lecture 6: Basic Operations in Numpy Arrays

Lecture 7: Data Analytics operations in Numpy

Lecture 8: Project 5

Lecture 9: Project 5 Solution

Chapter 6: Introduction to Pandas

Lecture 1: Introduction to Pandas

Lecture 2: Reading in Files in Pandas

Lecture 3: Looking at data in the dataframe

Lecture 4: Accessing, filtering and Sorting data

Lecture 5: Indexing, loc and iloc in Pandas

Lecture 6: Groupby and aggregate functions

Lecture 7: Merge, Join and Concatenate

Lecture 8: Data Cleaning in Pandas 1

Lecture 9: Data Cleaning in Pandas 2

Lecture 10: Data Visualization in Pandas

Lecture 11: Project 6

Lecture 12: Project 6 Solution

Chapter 7: Introduction to Matplotlib

Lecture 1: Introduction to Matplotli

Lecture 2: Basic Plots in Matplotlib

Lecture 3: Project 7

Lecture 4: Project 7 Solution

Chapter 8: Basic Machine Learning with Scikit-Learn

Lecture 1: Introduction to Machine Learning

Lecture 2: Supervised & Unsupervised Learning

Lecture 3: Machine Learning Techniques

Lecture 4: Introduction to Scikit-Learn

Chapter 9: Regression Models with Scikit-Learn

Lecture 1: Introduction to Regression Models

Lecture 2: Building your First Linear Regression Model 1

Lecture 3: Building your First Linear Regression Model 2

Lecture 4: Building your First Linear Regression Model 3

Lecture 5: Building your First Linear Regression Model 4

Lecture 6: Project 8

Lecture 7: Project 8 Solution

Chapter 10: Classification Models with Scikit-Learn

Lecture 1: Introduction to Classification Models

Lecture 2: Building your First Classification Model 1

Lecture 3: Building your First Classification Model 2

Lecture 4: Building your First Classification Model 3

Lecture 5: Building your First Classification Model 4

Lecture 6: Project 9

Lecture 7: Project 9 Solution

Chapter 11: Clustering Models with Scikit-Learn

Lecture 1: Introduction to Clustering Models

Lecture 2: Building your First Clustering Model 1

Lecture 3: Building your First Clustering Model 2

Lecture 4: Project 10

Lecture 5: Project 10 Solution

Chapter 12: Wrap up

Lecture 1: Wrap – Up

Instructors

Damilare Abolaji
Senior Business Intelligence Developer

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4 stars: 1 votes

5 stars: 3 votes

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