Welcome To Data Science

Master the Fundamentals of Data Science - From Core Concepts to Career Paths in the Data Science Revolution

What you'll learn

Application of Algorithms in Data Science: learners will be capable of applying algorithms in the field of data science.

Machine Learning Fundamentals: Introduction to machine learning concepts & Deep learning

Understand Python for Machine Learning: Students will gain proficiency in using Python and essential libraries (such as NumPy, Pandas, and Matplotlib etc)

Exploratory Data Analysis (EDA): Gain skills in analyzing datasets to discover patterns, trends, and insights.

Proficiency in Implementing Algorithms: Learners will gain proficiency in implementing algorithms in programming, specifically using Python.

Explore Advanced Topics and Ethical Considerations: Participants will explore advanced machine learning topics

Algorithm Design Techniques: Delve into different algorithm design paradigms such as divide and conquer, dynamic programming, and greedy methods.

Understand the Machine Learning Pipeline & Its components

Understand & Experience in Model Training, Deployment, & Operational Best Practices

Requirements

Desire to Learn and research

Beginners in Programming and Data Science

Interest in Data Science, Machine Learning& Artificial Intelligence

Description

Embark on your data science journey with this comprehensive beginner-friendly course that demystifies the world of data science. Whether you're a complete beginner, career changer, or professional looking to understand data science better, this course provides a solid foundation in data science concepts, tools, methodologies, and career opportunities.You'll explore what data science really means, discover the four pillars that support the field, understand the tools and technologies that power data-driven decisions, and learn about various career paths including Data Scientist, ML Engineer, Data Engineer, and AI Engineer roles. Through practical examples, real-world case studies, and hands-on exercises, you'll gain the knowledge and confidence to either pursue a career in data science or effectively collaborate with data science teams. You Ready?What is Primarily Taught in Your Course:Fundamental concepts and definitions of data scienceThe four foundational pillars of data scienceEssential tools and technologies used in the fieldReal-world applications and impact of data science across industriesComprehensive overview of data science career roles and responsibilitiesPractical guidance for transitioning into data science careersHands-on exposure to data science thinking and problem-solving approachesEducational Assignments, quizzes and testsImpact & Ethics: How data science transforms businesses and society responsibly

Overview

Section 1: What is all the fuss about Data Science?

Lecture 1 Promotional Video

Lecture 2 Course Curriculum

Lecture 3 What is all the fuss about Data Science?

Lecture 4 Introduction to Data Science

Lecture 5 Introduction to Statistics and Machine learning in Data Science

Lecture 6 Important Notice!

Lecture 7 Programming Languages, Domain Knowledge & Skills Required in Data Science

Lecture 8 Deep Dive into Machine Learning & Statistics in Data Science

Lecture 9 Machine Learning & Statistics in Data Science

Lecture 10 Impact of Data Science on Society & Business

Lecture 11 Explainable AI (XAI) & AI Ethics

Lecture 12 Section Completed!

Section 2: The Pillars of Data Science

Lecture 13 Programming & Software engineering principles

Lecture 14 Learning Framework

Lecture 15 Probability and Statistics Essentials

Lecture 16 Calculus In Data Science

Lecture 17 Essential Concepts

Lecture 18 Python Basics & Libraries

Lecture 19 Welcome to Pandas | NumPy | Matplotlib | Seaborn

Lecture 20 NumPy and Pandas for Data Manipulation

Lecture 21 Data Visualization with Matplotlib

Lecture 22 Data Visualization with Seaborn

Lecture 23 Introduction to Algorithms

Lecture 24 Introduction to building an algorithm with Python (Basic)

Lecture 25 Important Notice!

Lecture 26 Introduction to EDA

Lecture 27 Can we go Deeper Into EDA?

Lecture 28 An Actual EDA

Lecture 29 The Future Of EDA with Machine Learning

Lecture 30 Introduction to TensorFlow and Keras

Lecture 31 Introduction to PyTorch Fundamentals

Lecture 32 Introduction to Scikit-learn

Lecture 33 Design, implement, and deploy an end-to-end AI solution

Lecture 34 Beginner End to End Tutorial Project

Lecture 35 Section Completed!

Section 3: Machine learning, Deep learning & Artificial Intelligence in Data science

Lecture 36 What is machine Learning?

Lecture 37 The History of Machine Learning

Lecture 38 Python in Machine learning

Lecture 39 Mathematics for Machine Learning

Lecture 40 Mathematics In ML/AI

Lecture 41 Algebra for Machine Learning

Lecture 42 Introduction to Supervised learning

Lecture 43 Supervised learning

Lecture 44 Introduction to Unsupervised Learning

Lecture 45 Unsupervised Learning

Lecture 46 What is deep Learning?

Lecture 47 Deep learning

Lecture 48 Reinforcement Learning

Lecture 49 Stil An Important Notice!

Lecture 50 Ensemble Methods (Random Forests, Gradient Boosting)

Lecture 51 Hyperparameter Optimization in AI/ML

Lecture 52 Introduction To Convolutional Neural Networks (CNNs)

Lecture 53 Introduction To Recurrent Neural Networks (RNNs) and LSTMs

Lecture 54 End to end Python walkthrough tutorial

Lecture 55 Section Completed

Section 4: Introduction To large language models and foundation models

Lecture 56 Introduction to LLMs and Foundation Models

Lecture 57 Transformers

Lecture 58 Fine-tuning Pre-trained Models

Lecture 59 Prompt Engineering and Few-shot Learning

Lecture 60 Ethical Considerations in AI

Lecture 61 Section Completed!

Section 5: Roles in Data Science

Lecture 62 Major Roles in Data Science

Lecture 63 What is a Data Scientist?

Lecture 64 The Role of AI Engineers vs. Data Scientists

Lecture 65 What is an Artificial intelligence Engineer?

Lecture 66 What is a Machine Learning Engineer?

Lecture 67 Future of Roles In Machine Learning

Lecture 68 What is a Data Engineer ?

Section 6: This is where we part way

Lecture 69 Parting Thoughts from Course Instructor

All Curious About Data Science,This course is designed to accommodate learners with varying levels of experience, including beginners. While there are no strict prerequisites, having a basic understanding of programming concepts and familiarity with Python would be beneficial,Students and Professionals,Beginners in Artificial Intelligence, Machine learning and Data Science,Self-Learners and Lifelong Learners,Professionals Seeking Career Advancement,Beginners in Programming