Foundations Of Data Science: Python To Ml

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!