Data Science for Beginners
Data Science for Beginners, available at $44.99, with 38 lectures.
You will learn about Data science Artificial intelligence Machine Learning Statistics and data analysis This course is ideal for individuals who are Beginners curious about data science It is particularly useful for Beginners curious about data science.
Enroll now: Data Science for Beginners
Summary
Title: Data Science for Beginners
Price: $44.99
Number of Lectures: 38
Number of Published Lectures: 38
Number of Curriculum Items: 38
Number of Published Curriculum Objects: 38
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will Learn
- Data science
- Artificial intelligence
- Machine Learning
- Statistics and data analysis
Who Should Attend
- Beginners curious about data science
Target Audiences
- Beginners curious about data science
Are you eager to dive into the world of data science and unleash the power of data-driven insights ? My Data Science Fundamentals course is designed to equip you with essential skills and techniques required to excel in this rapidly growing field.
Whether you’re a beginner looking to start a career in data science or a professional seeking to enhance your analytical abilities, this course is tailored to meet your learning needs.
Course Overview:
In this comprehensive course, you will:
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Explore Data Science Foundations: Understand the fundamental concepts of data science, including data manipulation, exploratory data analysis (EDA), and data visualization techniques.
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Learn Statistical Analysis: Acquire foundational knowledge in statistics, hypothesis testing, and probability theory to make data-driven decisions and draw meaningful insights from data.
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Learn Machine Learning: Discover the principles of supervised and unsupervised learning, regression, classification, clustering, and evaluation metrics to develop predictive models.
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Learn Machine Learning Algorithms: Discover the principles of supervised and unsupervised learning, regression, classification, clustering, and evaluation metrics to develop predictive models.
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Introduction to Deep Learning: Dive into the basics of neural networks, deep learning, computer vision and natural language processing.
Who Should Enroll:
This course is ideal for:
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Aspiring Data Scientists and Analysts
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Business and IT professionals seeking to leverage data for decision-making
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Students and researchers interested in exploring the field of data science
No prior experience in data science is required
By the end of this course, you will be equipped with the skills and confidence to tackle data science challenges, make informed decisions based on data analysis, and embark on a rewarding career in the dynamic field of data science.
Join me on this exciting data science journey and unlock the potential of data-driven innovation !
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Chapter 2: Foundations of Data science
Lecture 1: What is data science ?
Lecture 2: Role of a data scientist
Lecture 3: History and evolution of data science
Lecture 4: Data Preprocessing
Lecture 5: Overview of data storage
Lecture 6: Data Exploration and Visualization
Lecture 7: Descriptive Statistics
Lecture 8: Data Visualizations Techniques
Chapter 3: Statistical Foundations
Lecture 1: Importance of statistics
Lecture 2: Statistical Features Part 1
Lecture 3: Statistical Features Part 2
Lecture 4: Correlation
Lecture 5: Causation
Lecture 6: Probability Distribution
Lecture 7: Skewness
Lecture 8: Dimensionality Reduction
Lecture 9: Multicollinearity
Lecture 10: Outliers
Lecture 11: Bayesian Statistics
Lecture 12: Central Theorem Limit
Lecture 13: Sampling
Lecture 14: Confidence interval & Significance Level
Lecture 15: Features transformation
Lecture 16: Probability Theory
Lecture 17: Hypothesis Testing
Lecture 18: Regression Analysis
Lecture 19: Domain Knowledge
Chapter 4: Advanced Machine Learning
Lecture 1: Machine Learning concepts
Lecture 2: Machine Learning algorithms
Lecture 3: Supervised & Unsupervised Learning
Lecture 4: Cross Validation Techniques
Lecture 5: Metrics for Regression
Lecture 6: Metrics for Classification
Lecture 7: Deep Learning
Lecture 8: Introduction to Neural Network
Lecture 9: Artificial Intelligence & Robotics
Chapter 5: Congratulations
Lecture 1: Outro
Instructors
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Ndiaga Fall
Data Scientist
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Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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