Python For Data Science: A Comprehensive Journey To Mastery
Published 9/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 898.02 MB | Duration: 4h 55m

Mastering Python, Data Analysis, and Machine Learning

What you'll learn

Python syntax, data structures, and libraries essential for data science, such as NumPy and Pandas.

Learn how to clean, organize, and transform raw data into usable formats for analysis and visualization.

Understand how to explore datasets to identify patterns, trends, and relationships.

Build predictive models using Scikit-learn, covering supervised learning algorithms.

Develop critical thinking and analytical skills to solve complex data challenges.

Requirements

Students should be comfortable using a computer, navigating files, and installing software.

This course is designed for absolute beginners, so no prior experience in Python or programming is necessary.

A positive attitude, curiosity, and the drive to learn new concepts and solve problems are essential.

Description

Unlock the power of data with our comprehensive Python for Data Science course!Expertly crafted to suit both beginners and experienced professionals, this course will guide you from the basics to advanced mastery in Python, a programming language that continues to dominate the data science landscape. Starting with fundamental concepts, you'll become proficient in Python's syntax and core libraries, and gradually progress to more advanced topics such as data manipulation, visualization, machine learning, and predictive modeling.Our course is rooted in practical, hands-on learning, allowing you to work with real-world datasets and develop models that can drive meaningful decision-making. Whether your goal is to propel your career forward, transition into the rapidly expanding field of data science, or simply sharpen your analytical skills, this course provides everything you need to excel.In addition to technical skills, you'll gain valuable insights into industry best practices, current trends, and the latest tools utilized by leading data scientists. With lifetime access to course materials, ongoing updates, and a supportive community of fellow learners, your journey to becoming a data science expert is both supported and sustained.Enroll today and begin transforming your data into actionable insights that can shape the future of your career and industry!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Data types, operators and data structures in Python

Lecture 2 Python Data Types

Lecture 3 Operators

Lecture 4 Arithmatic Operators

Lecture 5 Assignment Operators

Lecture 6 Comparison Operators

Lecture 7 More on Strings

Lecture 8 String Methods

Lecture 9 Lists

Lecture 10 Tuples

Lecture 11 Sets

Lecture 12 Dictionaries

Lecture 13 Identity Operators

Lecture 14 Compound Data Structures

Section 3: Python Loops and Comprehensions

Lecture 15 Python loops

Lecture 16 Range Understanding

Lecture 17 Creating and Modifying Lists

Lecture 18 Looping Through Dictionaries

Lecture 19 Enumerate Function

Lecture 20 List Comprehentions

Lecture 21 Adding Conditionals to List Comprehentions

Section 4: Comprehensive Guide to Python Functions

Lecture 22 Python Functions

Lecture 23 Functions Parameters

Lecture 24 Return values

Lecture 25 Default Parameters

Lecture 26 Variable-Length Arguments

Lecture 27 Lambda Functions

Lecture 28 Higher Order Functions

Lecture 29 Recursive Functions

Lecture 30 Docstrings

Lecture 31 Functions Annotations

Lecture 32 Nested Functions

Lecture 33 Decorators

Section 5: NumPy for Efficient Numerical Computations

Lecture 34 Introduction to numpy

Lecture 35 Array Attributes

Lecture 36 Array Indexing and Slicing

Lecture 37 Array Operations

Lecture 38 Reshaping Arrays

Lecture 39 Stacking and Splitting Arrays

Lecture 40 Splitting Arrays

Lecture 41 Broadcasting

Lecture 42 Boolean Indexing and Filtering

Lecture 43 Advanced Array Manipulations

Section 6: Data Manipulation with Pandas: A Comprehensive Guide

Lecture 44 Introduction to Pandas

Lecture 45 Pandas Series

Lecture 46 Pandas DataFames

Lecture 47 Loading Data Into a DataFrame

Lecture 48 Handling Missing Data (NaN Values)

Lecture 49 Basic DataFrame Operations

Lecture 50 Grouping Data in Pandas

Lecture 51 Merging and Joining DataFrames

Lecture 52 Data Cleaning

Section 7: Hands-On Machine Learning: Exploring Scikit-Learn

Lecture 53 Introduction to Machine Learning and Scikit-learn

Lecture 54 Data Preprocessing

Lecture 55 Handling Missing Values

Lecture 56 Features Scaling

Lecture 57 Encoding Categorical Variables

Lecture 58 Decision Trees

Lecture 59 Support Vector Machine

Beginners: Individuals with no prior programming or data science experience who want to learn Python and data science from scratch.,Aspiring Data Scientists: Those looking to break into the data science field and build foundational skills needed for a successful career.,Software Engineers: Programmers or engineers who want to expand their knowledge into data analysis, machine learning, and data science techniques.,Data Analysts: Professionals looking to upgrade their Python skills to analyze and visualize data more efficiently.,College Students: Students pursuing degrees in fields such as computer science, statistics, or economics who want to strengthen their data science and Python skills.,Business Analysts: Professionals seeking to use data to drive better decision-making and extract actionable insights from datasets.,Professionals from Other Fields: Individuals from various industries (marketing, finance, healthcare, etc.) who want to enhance their analytical abilities and leverage data science in their work.,Entrepreneurs & Freelancers: Those who want to utilize data science to grow their business, gain insights into customer behavior, or enhance their services.