Learn Python For Data Science From Scratch -With 10 Projects

Unleash Data Potential: Master Python for Data Science, Visualization, and Machine Learning from Ground Zero to Pro!

What you'll 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

Requirements

Basic Computer Literacy.

Critical Thinking and Problem-Solving Skills.

No Prior Programming Experience Required

Description

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!

Overview

Section 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)

Section 2: Data Types and Structures in Python

Lecture 9 Strings

Lecture 10 Strings Numerical Data Types

Lecture 11 Lists

Lecture 12 Tuples

Lecture 13 Dictionaries

Lecture 14 Project 2

Lecture 15 Project 2 Solution

Section 3: Control Flow in Python

Lecture 16 Overview of Control Flow

Lecture 17 Conditional Statements

Lecture 18 For Loops

Lecture 19 While loops

Lecture 20 Project 3

Lecture 21 Project 3 Solution

Section 4: Functions and Modules in Python

Lecture 22 Functions

Lecture 23 Lambda Functions

Lecture 24 Modules

Lecture 25 Project 4

Lecture 26 Project 4 Solution

Section 5: Introduction to Numpy

Lecture 27 Introduction to Numpy

Lecture 28 Creating arrays in Numpy

Lecture 29 Indexing and Slicing Arrays

Lecture 30 Copy and View in Numpy

Lecture 31 Shape and reshaping arrays

Lecture 32 Basic Operations in Numpy Arrays

Lecture 33 Data Analytics operations in Numpy

Lecture 34 Project 5

Lecture 35 Project 5 Solution

Section 6: Introduction to Pandas

Lecture 36 Introduction to Pandas

Lecture 37 Reading in Files in Pandas

Lecture 38 Looking at data in the dataframe

Lecture 39 Accessing, filtering and Sorting data

Lecture 40 Indexing, loc and iloc in Pandas

Lecture 41 Groupby and aggregate functions

Lecture 42 Merge, Join and Concatenate

Lecture 43 Data Cleaning in Pandas 1

Lecture 44 Data Cleaning in Pandas 2

Lecture 45 Data Visualization in Pandas

Lecture 46 Project 6

Lecture 47 Project 6 Solution

Section 7: Introduction to Matplotlib

Lecture 48 Introduction to Matplotli

Lecture 49 Basic Plots in Matplotlib

Lecture 50 Project 7

Lecture 51 Project 7 Solution

Section 8: Basic Machine Learning with Scikit-Learn

Lecture 52 Introduction to Machine Learning

Lecture 53 Supervised & Unsupervised Learning

Lecture 54 Machine Learning Techniques

Lecture 55 Introduction to Scikit-Learn

Section 9: Regression Models with Scikit-Learn

Lecture 56 Introduction to Regression Models

Lecture 57 Building your First Linear Regression Model 1

Lecture 58 Building your First Linear Regression Model 2

Lecture 59 Building your First Linear Regression Model 3

Lecture 60 Building your First Linear Regression Model 4

Lecture 61 Project 8

Lecture 62 Project 8 Solution

Section 10: Classification Models with Scikit-Learn

Lecture 63 Introduction to Classification Models

Lecture 64 Building your First Classification Model 1

Lecture 65 Building your First Classification Model 2

Lecture 66 Building your First Classification Model 3

Lecture 67 Building your First Classification Model 4

Lecture 68 Project 9

Lecture 69 Project 9 Solution

Section 11: Clustering Models with Scikit-Learn

Lecture 70 Introduction to Clustering Models

Lecture 71 Building your First Clustering Model 1

Lecture 72 Building your First Clustering Model 2

Lecture 73 Project 10

Lecture 74 Project 10 Solution

Section 12: Wrap up

Lecture 75 Wrap - Up

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