Best Data Science Courses on Udemy

The Data Science Course: Complete Data Science Bootcamp

Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture 

Data science is a multidisciplinary field. It encompasses a wide range of topics.   

• Understanding of the data science field and the type of analysis carried out  
• Mathematics  
• Statistics   
• Python   
• Applying advanced statistical techniques in Python   
• Data Visualization  
• Machine Learning  
• Deep Learning  

Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is.   

The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs.

• The course provides the entire toolbox you need to become a data scientist
• Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
• Impress interviewers by showing an understanding of the data science field
• Learn how to pre-process data
• Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
• Start coding in Python and learn how to use it for statistical analysis
• Perform linear and logistic regressions in Python
• Carry out cluster and factor analysis
• Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
• Apply your skills to real-life business cases
• Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
• Unfold the power of deep neural networks
• Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
• Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations

Rating: 4.6 | Course Duration: 31.5hrs | Coding Exercises: 132 | Articles: 92 | Downloadable Resources: 541

Data Science A-Z: Hands-On Exercises & ChatGPT Prize

In this course you WILL experience firsthand all of the PAIN a Data Scientist goes through on a daily basis. Corrupt data, anomalies, irregularities – you name it!

This course will give you a full overview of the Data Science journey. Upon completing this course you will know:

• How to clean and prepare your data for analysis
• How to perform basic visualisation of your data
• How to model your data
• How to curve-fit your data
• And finally, how to present your findings and wow the audience

This course will give you so much practical exercises that real world will seem like a piece of cake when you graduate this class. This course has homework exercises that are so thought provoking and challenging that you will want to cry… But you won’t give up! You will crush it. In this course you will develop a good understanding of the following tools:

• SQL
• SSIS
• Tableau
• Gretl

This course has pre-planned pathways. Using these pathways you can navigate the course and combine sections into YOUR OWN journey that will get you the skills that YOU need.

• Successfully perform all steps in a complex Data Science project
• Create Basic Tableau Visualisations
• Perform Data Mining in Tableau
• Understand how to apply the Chi-Squared statistical test
• Apply Ordinary Least Squares method to Create Linear Regressions
• Assess R-Squared for all types of models
• Assess the Adjusted R-Squared for all types of models
• Create a Simple Linear Regression (SLR)
• Create a Multiple Linear Regression (MLR)
• Create Dummy Variables
• Interpret coefficients of an MLR
• Read statistical software output for created models
• Use Backward Elimination, Forward Selection, and Bidirectional Elimination methods to create statistical models
• Create a Logistic Regression
• Intuitively understand a Logistic Regression
• Operate with False Positives and False Negatives and know the difference
• Read a Confusion Matrix
• Create a Robust Geodemographic Segmentation Model
• Transform independent variables for modelling purposes
• Derive new independent variables for modelling purposes
• Check for multicollinearity using VIF and the correlation matrix
• Understand the intuition of multicollinearity
• Apply the Cumulative Accuracy Profile (CAP) to assess models
• Build the CAP curve in Excel
• Use Training and Test data to build robust models
• Derive insights from the CAP curve
• Understand the Odds Ratio
• Derive business insights from the coefficients of a logistic regression
• Understand what model deterioration actually looks like
• Apply three levels of model maintenance to prevent model deterioration
• Install and navigate SQL Server
• Install and navigate Microsoft Visual Studio Shell
• Clean data and look for anomalies
• Use SQL Server Integration Services (SSIS) to upload data into a database
• Create Conditional Splits in SSIS
• Deal with Text Qualifier errors in RAW data
• Create Scripts in SQL
• Apply SQL to Data Science projects
• Create stored procedures in SQL
• Present Data Science projects to stakeholders

Complete Data Science, Machine Learning, DL, NLP Bootcamp

Are you looking to master Data Science,Machine Learning (ML), Deep Learning(DL) and Natural Language Processing (NLP) from the ground up? This comprehensive course is designed to take you on a journey from understanding the basics to mastering advanced concepts, all while providing practical insights and hands-on experience.

This course is suitable for anyone interested in learning machine learning and natural language processing, from beginners to advanced learners. Whether you’re a student, a professional looking to upskill, or someone looking to switch careers, this course will provide you with the knowledge and skills you need to succeed in the field of ML and NLP.

• Foundational Concepts: Start with the basics of ML and NLP, including algorithms, models, and techniques used in these fields. Understand the core principles that drive machine learning and natural language processing.
• Advanced Topics: Dive deeper into advanced topics such as deep learning, reinforcement learning, and transformer models. Learn how to apply these concepts to build more complex and powerful models.
• Practical Applications: Gain practical experience by working on real-world projects and case studies. Apply your knowledge to solve problems in various domains, including healthcare, finance, and e-commerce.
• Mathematical Foundations: Develop a strong mathematical foundation by learning the math behind ML and NLP algorithms. Understand concepts such as linear algebra, calculus, and probability theory.
• Industry-standard Tools: Familiarize yourself with industry-standard tools and libraries used in ML and NLP, including TensorFlow, PyTorch, and scikit-learn. Learn how to use these tools to build and deploy models.
• Optimization Techniques: Learn how to optimize ML and NLP models for better performance and efficiency. Understand techniques such as hyperparameter tuning, model selection, and model evaluation.

By the end of this course, you’ll have a comprehensive understanding of machine learning and natural language processing, from the basics to advanced concepts. You’ll be able to apply your knowledge to build real-world projects, and you’ll have the skills needed to pursue a career in ML and NLP.

Course Rating:4.6 | Course Duration: 91.5hrs | Coding Exercises: 20 | Articles: 19 | Downloadable Resources: 73

Complete A.I. & Machine Learning, Data Science Bootcamp

Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know).

This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want.

The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know programming, you can dive right in and skip the section where we teach you Python from scratch. If you are completely new, we take you from the very beginning and actually teach you Python and how to use it in the real world for our projects. Don’t worry, once we go through the basics like Machine Learning 101 and Python, we then get going into advanced topics like Neural Networks, Deep Learning and Transfer Learning so you can get real life practice and be ready for the real world.

By the end of this course, you will be a complete Data Scientist that can get hired at large companies. We are going to use everything we learn in the course to build professional real world projects like Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and many more. By the end, you will have a stack of projects you have built that you can show off to others.

• Become a Data Scientist and get hired
• Master Machine Learning and use it on the job
• Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
• Use modern tools that big tech companies like Google, Apple, Amazon and Meta use
• Present Data Science projects to management and stakeholders
• Learn which Machine Learning model to choose for each type of problem
• Real life case studies and projects to understand how things are done in the real world
• Learn best practices when it comes to Data Science Workflow
• Implement Machine Learning algorithms
• Learn how to program in Python using the latest Python 3
• How to improve your Machine Learning Models
• Learn to pre process data, clean data, and analyze large data.
• Build a portfolio of work to have on your resume
• Developer Environment setup for Data Science and Machine Learning
• Supervised and Unsupervised Learning
• Machine Learning on Time Series data
• Explore large datasets using data visualization tools like Matplotlib and Seaborn
• Explore large datasets and wrangle data using Pandas
• Learn NumPy and how it is used in Machine Learning
• A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided
• Learn to use the popular library Scikit-learn in your projects
• Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry
• Learn to perform Classification and Regression modelling

Course Rating: 4.6 | Course Duration: 43.5hrs | Coding Exercises:1 | Articles: 60 | Downloadable Resources: 14

Mathematics-Basics to Advanced for Data Science And GenAI

This course is designed to bridge the gap between your current math skills and the level required to understand and implement data science algorithms effectively. Whether you are a beginner or an experienced professional looking to strengthen your mathematical understanding, this course will equip you with the tools you need to succeed.

This course stands out by focusing on the clarity and practical application of mathematical concepts in data science. Each topic is broken down into simple, easy-to-understand modules that build on one another. You will not only learn the theory but also see exactly how these mathematical tools are used in real data science scenarios.

Throughout the course, you’ll engage with interactive quizzes, assignments, and hands-on projects designed to reinforce your understanding. By applying what you learn in real-world projects, you’ll gain practical experience and build a portfolio that showcases your newly acquired skills.

1. Calculus for Data Science:
   • Understand the fundamentals of calculus, including derivatives, integrals, and limits.
   • Learn how these concepts are applied in optimizing machine learning algorithms, such as gradient descent, and in understanding complex data transformations.
2. Linear Algebra Essentials:
   • Gain a deep understanding of vectors, matrices, eigenvalues, and eigenvectors.
   • Discover how these linear algebra concepts are crucial for data manipulation, dimensionality reduction (like PCA), and building advanced machine learning models.
3. Probability Theory and Its Applications:
   • Dive into the world of probability, including concepts like random variables, distributions, and Bayes’ Theorem.
   • Explore how probability forms the backbone of predictive modeling, classification algorithms, and risk assessment in data science.
4. Statistics for Data Analysis:
   • Master key statistical techniques such as hypothesis testing, regression analysis, and statistical inference.
   • Learn to make data-driven decisions by understanding and applying statistical methods to real-world datasets.

Python for Data Science and Machine Learning Bootcamp

This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!

This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!

We’ll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning:

• Programming with Python
• NumPy with Python
• Using pandas Data Frames to solve complex tasks
• Use pandas to handle Excel Files
• Web scraping with python
• Connect Python to SQL
• Use matplotlib and seaborn for data visualizations
• Use plotly for interactive visualizations
• Machine Learning with SciKit Learn, including:
• Linear Regression
• K Nearest Neighbors
• K Means Clustering
• Decision Trees
• Random Forests
• Natural Language Processing
• Neural Nets and Deep Learning
• Support Vector Machines

Data Science & Machine Learning(Theory+Projects)A-Z 90 HOURS

This course is designed to provide both theoretical foundations and practical, hands-on experience. By the end of the course, you will be equipped with the knowledge to excel as a data science professional, fully prepared to apply AI and ML concepts to real-world challenges.

The course is structured into several interrelated sections, each of which builds upon the previous one. While you may initially view each section as an independent unit, they are carefully arranged to offer a cohesive and sequential learning experience. This allows you to master foundational skills and gradually tackle more complex topics as you progress.

The “Data Science & Machine Learning Full Course in 90 HOURS” is crafted to equip you with the most in-demand skills in today’s fast-paced world. The course focuses on helping you gain a deep understanding of the principles, tools, and techniques of data science and machine learning, with a particular emphasis on the Python programming language.

Key Features:

• Comprehensive and methodical pacing that ensures all learners—beginners and advanced—can follow along and absorb the material.
• Hands-on learning with live coding, practical exercises, and real-world projects to solidify understanding.
• Exposure to the latest advancements in AI and ML, as well as the most cutting-edge models and algorithms.
• A balanced mix of theoretical learning and practical application, allowing you to immediately implement what you learn.

The course includes over 700 HD video tutorials, detailed code notebooks, and assessment tasks that challenge you to apply your knowledge after every section. Our instructors, passionate about teaching, are available to provide support and clarify any doubts you may have along your learning journey.

1. Python for Data Science and Data Analysis:
   • Introduction to problem-solving, leading up to complex indexing and data visualization with Matplotlib.
   • No prior knowledge of programming is required.
   • Master data science packages such as NumPy, Pandas, and Matplotlib.
   • After completing this section, you will have the skills necessary to work with Python and data science packages, providing a solid foundation for transitioning to other programming languages.
2. Data Understanding and Visualization with Python:
   • Delve into advanced data manipulation and visualization techniques.
   • Explore widely used packages, including Seaborn, Plotly, and Folium, for creating 2D/3D visualizations and interactive maps.
   • Gain the ability to handle complex datasets, reducing your dependency on core Python language and enhancing your proficiency with data science tools.
3. Mastering Probability and Statistics in Python:
   • Learn the theoretical foundation of data science by mastering Probability and Statistics.
   • Understand critical concepts like conditional probability, statistical inference, and estimations—key pillars for ML techniques.
   • Explore practical applications and derive important relationships through Python code.
4. Machine Learning Crash Course:
   • A thorough walkthrough of the theoretical and practical aspects of machine learning.
   • Build machine learning pipelines using Sklearn.
   • Dive into more advanced ML concepts and applications, preparing you for deeper exploration in subsequent sections.
5. Feature Engineering and Dimensionality Reduction:
   • Understand the importance of data preparation for improving model performance.
   • Learn techniques for selecting and transforming features, handling missing data, and enhancing model accuracy and efficiency.
   • The section includes real-world case studies and coding examples in Python.
6. Artificial Neural Networks (ANNs) with Python:
   • ANNs have revolutionized machine learning with their ability to process large amounts of data and identify intricate patterns.
   • Learn the workings of TensorFlow, Google’s deep learning framework, and apply ANN models to real-world problems.
7. Convolutional Neural Networks (CNNs) with Python:
   • Gain a deep understanding of CNNs, which have revolutionized computer vision and many other fields, including audio processing and reinforcement learning.
   • Build and train CNNs using TensorFlow for various applications, from facial recognition to neural style transfer.

By the End of This Course, You Will Be Able To:

• Understand key principles and theories in Data Science and Machine Learning.
• Implement Python-based machine learning models using real-world datasets.
• Apply advanced data science techniques to solve complex problems.
• Take on challenging roles in data science and machine learning with confidence.

Python for Machine Learning & Data Science Masterclass

This course is designed for the student who already knows some Python and is ready to dive deeper into using those Python skills for Data Science and Machine Learning. The typical starting salary for a data scientists can be over $150,000 dollars, and we’ve created this course to help guide students to learning a set of skills to make them extremely hirable in today’s workplace environment.

This course is balanced between practical real world case studies and mathematical theory behind the machine learning algorithms.

We cover advanced machine learning algorithms that most other courses don’t! Including advanced regularization methods and state of the art unsupervised learning methods, such as DBSCAN.

This comprehensive course is designed to be on par with Bootcamps that usually cost thousands of dollars and includes the following topics:

• Programming with Python
• NumPy with Python
• Deep dive into Pandas for Data Analysis
• Full understanding of Matplotlib Programming Library
• Deep dive into seaborn for data visualizations
• Machine Learning with SciKit Learn, including:
  • Linear Regression
  • Regularization
  • Lasso Regression
  • Ridge Regression
  • Elastic Net
  • K Nearest Neighbors
  • K Means Clustering
  • Decision Trees
  • Random Forests
  • Natural Language Processing
  • Support Vector Machines
  • Hierarchal Clustering
  • DBSCAN
  • PCA
  • Model Deployment

Data Science : Complete Data Science & Machine Learning

Data Science and Machine Learning are the hottest skills in demand but challenging to learn. Did you wish that there was one course for Data Science and Machine Learning that covers everything from Math for Machine Learning, Advance Statistics for Data Science, Data Processing, Machine Learning A-Z, Deep learning and more?

Well, you have come to the right place. This Data Science and Machine Learning course has 11 projects, 250+ lectures, more than 25+ hours of content, one Kaggle competition project with top 1 percentile score, code templates and various quizzes.

We are going to execute following real-life projects,

• Kaggle Bike Demand Prediction from Kaggle competition
• Automation of the Loan Approval process
• The famous IRIS Classification
• Adult Income Predictions from US Census Dataset
• Bank Telemarketing Predictions
• Breast Cancer Predictions
• Predict Diabetes using Prima Indians Diabetes Dataset

Today Data Science and Machine Learning is used in almost all the industries, including automobile, banking, healthcare, media, telecom and others.

As the Data Science and Machine Learning practioner, you will have to research and look beyond normal problems, you may need to do extensive data processing. experiment with the data using advance tools and build amazing solutions for business. However, where and how are you going to learn these skills required for Data Science and Machine Learning?

Data Science and Machine Learning require in-depth knowledge of various topics. Data Science is not just about knowing certain packages/libraries and learning how to apply them. Data Science and Machine Learning require an indepth understanding of the following skills,

• Understanding of the overall landscape of Data Science and Machine Learning
• Different types of Data Analytics, Data Architecture, Deployment characteristics of Data Science and Machine Learning projects
• Python Programming skills which is the most popular language for Data Science and Machine Learning
• Mathematics for Machine Learning including Linear Algebra, Calculus and how it is applied in Machine Learning Algorithms as well as Data Science
• Statistics and Statistical Analysis for Data Science
• Data Visualization for Data Science
• Data processing and manipulation before applying Machine Learning
• Machine Learning
• Ridge (L2), Lasso (L1) and Elasticnet Regression/ Regularization for Machine Learning
• Feature Selection and Dimensionality Reduction for Machine Learning models
• Machine Learning Model Selection using Cross Validation and Hyperparameter Tuning
• Cluster Analysis for unsupervised Machine Learning
• Deep Learning using most popular tools and technologies of today.

This Data Science and Machine Learning course has been designed considering all of the above aspects, the true Data Science and Machine Learning A-Z Course. In many Data Science and Machine Learning courses, algorithms are taught without teaching Python or such programming language. However, it is very important to understand the construct of the language in order to implement any discipline including Data Science and Machine Learning.