Published 9/2024
MP4 | Video: h264, 1920×1080 | 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.
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