Foundations Of Data Science: Python To Ml
Published 7/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.38 GB | Duration: 6h 21m
Published 7/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.38 GB | Duration: 6h 21m
Learn Python, Statistics, NumPy, Pandas, Visualization & Supervised Machine Learning with Real-World Examples.
What you'll learn
Build a solid foundation in Python programming, progressing from beginner to intermediate-level skills tailored for data science use cases.
Apply core statistical concepts to analyze data effectively, interpret distributions, and make data-driven decisions.
Manipulate and transform data using NumPy and Pandas to prepare clean, structured datasets ready for insightful analysis.
Create powerful data visualizations with Matplotlib and Seaborn to tell compelling stories with numbers.
Understand the fundamentals of machine learning, including key concepts like supervised vs. unsupervised learning, with hands-on real-world examples.
Implement basic supervised learning algorithms (like linear regression and classification) and evaluate their performance using practical metrics. - Gain conf
Gain confidence in solving data-related problems by working on guided, real-life inspired projects throughout the course.
Develop a holistic view of the data science lifecycle, from raw data processing to analytical modeling and interpretation.
Requirements
No prior programming or data science knowledge is required—this course is designed to be your gateway into the world of data. However, to get the most out of this course, it would be helpful if learners have: - A curiosity to explore and work with data - Basic computer literacy and comfort navigating software installations - Access to a PC or laptop with an internet connection (Windows/Mac/Linux) - Willingness to practice and apply concepts through exercises and mini-projects provided in the course All coding will be done in Python, and setup guidance will be provided to ensure learners start smoothly without technical hurdles.
Description
Unlock the gateway to data-driven success with Data Science Fundamentals, a comprehensive beginner-friendly course designed to build your confidence from the ground up. Whether you're just starting your journey into data or looking to solidify your foundational understanding, this course equips you with the essential tools and techniques used by data professionals worldwide.In this hands-on, project-oriented course, you'll:Start with core Python programming, laying the groundwork even if you have zero coding experience.Dive into foundational statistics to make sense of data and guide intelligent decision-making.Master essential data manipulation and analysis libraries like NumPy and Pandas to wrangle messy datasets into meaningful formats.Learn to visualize your findings with Matplotlib and Seaborn—because the best insights deserve to be seen clearly.Get introduced to the exciting world of Machine Learning, with practical coverage of supervised learning techniques such as regression and classification—each explained through real-life inspired examples.Apply every concept in context, gaining confidence through mini-projects that simulate real analytical challenges.Whether your goal is to break into data science, transition roles, or simply explore a powerful set of skills, this course offers a structured path from curiosity to confidence.What Is Primarily Taught in Your Course?Python programming fundamentalsDescriptive and inferential statisticsNumPy and Pandas for data manipulationData visualization using Matplotlib and SeabornAn introduction to machine learningSupervised learning algorithms with practical use casesReal-world application of data science methodsLet me know if you'd like a version of this tailored for SEO optimization or to convert more effectively on LinkedIn, your portfolio, or your personal website. I'm here to help position this to attract exactly the learners you're looking for.
Overview
Section 1: Basic of Python
Lecture 1 Introduction to Python
Lecture 2 Libraries for Data Science - Overview
Lecture 3 Variables
Lecture 4 Arithmetic Operator
Lecture 5 Boolean and Comparison Operator
Lecture 6 Getting input from user
Lecture 7 Conditional statement
Lecture 8 looping statement
Lecture 9 Data Structure - Overview
Lecture 10 Function
Lecture 11 String Handling
Lecture 12 Methods in String Handing
Section 2: Numpy
Lecture 13 Introduction to Numpy library
Lecture 14 Range of values in Array
Lecture 15 Properties of Array
Lecture 16 Reshaping the array
Lecture 17 Array Slicing
Lecture 18 Mathematical Operations in Array
Lecture 19 Stacking in Array
Lecture 20 Hands-on - Numpy
Section 3: Statistics in Python
Lecture 21 What is statistics
Lecture 22 Descriptive Statistics
Lecture 23 Descriptive Statistics in Detail
Lecture 24 Inferential Statistics
Lecture 25 Statistics - Summary
Section 4: Pandas
Lecture 26 Introduction to Pandas
Lecture 27 Series and DataFrame
Lecture 28 Getting Data from file
Lecture 29 Details about Dataset
Lecture 30 Data Slicing
Lecture 31 Conditional Filter and sorting
Lecture 32 Editing the Dataset
Lecture 33 Saving the dataset
Lecture 34 Check and Fix NaN values in dataset
Lecture 35 Mapping certain values to another in dataset
Lecture 36 Hands-on - Pandas
Section 5: Matplotlib
Lecture 37 Matplotlib - Overview
Lecture 38 Line Plot
Lecture 39 Bar Plot
Lecture 40 Histogram Plot
Lecture 41 Scatter Plot
Lecture 42 Pie Plot
Lecture 43 Multi Plot in single figure
Lecture 44 Hands-on Matplotlib Basics
Lecture 45 Hands-on Matplotlib Figure Object
Lecture 46 Hands-on Matplotlib sub plots
Section 6: Seaborn
Lecture 47 Introduction to Seaborn
Lecture 48 Hands-on sales data visualization
Lecture 49 Hands-on Statistical Relationship
Section 7: Machine Learning
Lecture 50 Introduction to Machine Learning
Lecture 51 Types of ML - Supervised Learning
Lecture 52 Types of ML - Unsupervised Learning
Lecture 53 Types of ML - Reinforcement Learning
Lecture 54 How to learn Machine Learning Concept
Lecture 55 Why Machine Learning
Lecture 56 Applications of Machine Learning
Section 8: Supervised Learning
Lecture 57 Classification based Algorithms - overview
Lecture 58 Evaluation Methods
Lecture 59 Regression based Algorithms - overview
Lecture 60 Hands-on Energy Meter
This course is designed for ambitious learners at all stages of their journey who want to master the foundations of data science using Python. It’s an ideal fit for: - Absolute beginners seeking a friendly, structured introduction to data science - Students or recent graduates looking to enhance their resume with real, job-ready data skills - Working professionals aiming to pivot into data-driven roles across tech, business, finance, or research - Developers and analysts interested in building a stronger analytical toolkit for tackling data-rich challenges - Anyone curious about machine learning and eager to understand not just how models work, but why they work Whether you're exploring data science out of curiosity or making a serious career move, this course will give you the knowledge and confidence to take the leap. If you’d like, I can also help you craft a high-converting course subtitle or eye-catching promotional message. Let me know!