Welcome To Data Science

Name: Welcome To Data Science
Rating: 5.0 (1 reviews)

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

Created byLubula Paul Chikwekwe

Last updated 8/2025

English

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

Course content

6 sections • 73 lectures • 7h 20m total length

Promotional Video1:47
Course Curriculum3:21
What is all the fuss about Data Science?5:18
Introduction to Data Science6:31
Explore how data science extracts knowledge from diverse data sets, blends statistics, computer science, maths, and domain knowledge, and drives data-driven discovery in the era of big data.
Introduction to Statistics and Machine learning in Data Science6:07
First Test, you ready?
Important Notice!1:21
Programming Languages, Domain Knowledge & Skills Required in Data Science6:30
Deep Dive into Machine Learning & Statistics in Data Science4:56
Machine Learning & Statistics in Data Science1:53
Impact of Data Science on Society & Business7:20
Introduction to Data Science
Explainable AI (XAI) & AI Ethics8:21
Section 1 Exam
Section Completed!0:12
Celebrate the completion of a section in the welcome to data science course, signaling progress in foundational data science concepts and readiness for the next topics.

Programming & Software engineering principles3:36
Learning Framework7:19
Probability and Statistics Essentials1:48
Calculus In Data Science1:28
Essential Concepts2:41
Probability, statistics, calculus in Sata science
Python Basics & Libraries2:20
Welcome to Pandas | NumPy | Matplotlib | Seaborn5:00
NumPy and Pandas for Data Manipulation2:56
Data Visualization with Matplotlib1:52
Data Visualization with Seaborn1:47
Introduction to Algorithms9:41
Bonus: Algorithm Cheat sheet2:35
Introduction to building an algorithm with Python (Basic)33:02
Intro to algorithms
Udemy Assignment: Complexity Analysis & Algorithm Building
Important Notice!1:13
Introduction to EDA0:53
Bonus: Introducion to EDA5:14
Can we go Deeper Into EDA?2:30
An Actual EDA33:05
The Future Of EDA with Machine Learning3:09
Udemy Course Assignment: Exploratory Data Analysis & Algorithm Building
Introduction to TensorFlow and Keras2:45
Introduction to PyTorch Fundamentals2:14
Introduction to Scikit-learn2:16
Python ML frameworks
Design, implement, and deploy an end-to-end AI solution3:24
Beginner End to End Tutorial Project33:29
Section 2 Exam
Section Completed!0:12

What is machine Learning?3:09
The History of Machine Learning3:47
Python in Machine learning2:09
Machine learning
Mathematics for Machine Learning8:42
Mathematics In ML/AI3:14
Algebra for Machine Learning2:53
Math's in ML
Introduction to Supervised learning4:08
Supervised learning5:49
Introduction to Unsupervised Learning4:56
Unsupervised Learning10:11
What is deep Learning?4:14
Deep learning6:47
Reinforcement Learning3:38
Machine Learning Quiz
Stil An Important Notice!3:11
Ensemble Methods (Random Forests, Gradient Boosting)4:10
Hyperparameter Optimization in AI/ML2:33
Introduction To Convolutional Neural Networks (CNNs)2:50
Introduction To Recurrent Neural Networks (RNNs) and LSTMs2:55
End to end Python walkthrough tutorial59:12
Udemy Course Assignment: Algorithm Design
Section Exam
Section Completed0:12

Introduction to LLMs and Foundation Models3:37
Transformers3:34
Recommendation Model demystified: Introduction to recommendation models7:10
Explore how recommendation models use implicit signals and embeddings to personalize streams across platforms, and how content-based, collaborative, and hybrid filtering tackle cold starts and shape discovery.
Fine-tuning Pre-trained Models3:33
Prompt Engineering and Few-shot Learning4:03
Ethical Considerations in AI3:09
Udemy Assignment: Understanding LLMs & Transformers
Section Completed!0:12
Complete this section in the welcome to data science course, signaling the end of a portion and preparing learners to move forward.

Major Roles in Data Science11:12
What is a Data Scientist?4:26
The Role of AI Engineers vs. Data Scientists4:42
What is an Artificial intelligence Engineer?4:39
Explore how AI engineers bridge data science and software engineering to design, train, and deploy robust, scalable AI solutions for NLP and computer vision.
What is a Machine Learning Engineer?5:34
Future of Roles In Machine Learning2:29
Bonus: Essential Roles to Build Artificial Intelligence0:26
What is a Data Engineer ?6:35
Welcome to Data Science Assignment: Data Science Career Blueprint Assignment

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 science
The four foundational pillars of data science
Essential tools and technologies used in the field
Real-world applications and impact of data science across industries
Comprehensive overview of data science career roles and responsibilities
Practical guidance for transitioning into data science careers
Hands-on exposure to data science thinking and problem-solving approaches
Educational Assignments, quizzes and tests
Impact & Ethics: How data science transforms businesses and society responsibly

Who this course is for:

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

Welcome To Data Science

What you'll learn

Explore related topics

Course content

What is all the fuss about Data Science?12 lectures • 54min

The Pillars of Data Science25 lectures • 2hr 46min

Machine learning, Deep learning & Artificial Intelligence in Data science20 lectures • 2hr 19min

Introduction To large language models and foundation models7 lectures • 25min

Roles in Data Science8 lectures • 40min

This is where we part way1 lecture • 16min

Requirements

Description

Who this course is for: