Learn Python For Data Science From Scratch –With 10 Projects

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

Homepage

https://www.udemy.com/course/learn-python-for-data-science-from-scratch-with-10-projects/