Udemy features more than 2,400 data science courses that are taught by data scientists or professionals who have worked for Google, IBM, Netflix, Facebook, etc. The majority of Udemy data science courses are beginner-friendly as they require no prior experience or paid software. They help beginners who are not familiar with Machine Learning, Python, R, Data Analysis, Business Analysis, Deep Learning, etc. They learn these concepts through live video lessons and real-world projects.
Most of the Udemy data science courses cost INR 3,499. Usually, the Udemy Data Science courses are offered at 90% off, however, students can enroll now and get discounts up to 90% on the listed price by clicking the Join Now button below. There are also more than 100 free data science courses on Udemy for those who want to check out basic data science courses without any certification.
As per student ratings & reviews ‘Machine Learning A-Z: Hands-on Python & R in Data Science’ is the best data science course on Udemy. The course has 9.7 lakh student enrollments, a 4.5/5 rating, and more than 1.7 lakh student reviews. ‘Data Science and Machine Learning Bootcamp with R’ is the highest-rated data science course on Udemy. The course has a 4.7/5 rating.
Data Science and Machine Learning Bootcamp with R
‘Data Science and Machine Learning Bootcamp with R’ is a popular Udemy data science course. The course content is designed in such a way that it will benefit both complete beginners with no programming experience as well as data scientists looking for career progression. The course provides foundational knowledge on R Programming; the topics covered in this course are fundamentals of R programming, data visualization, and how Machine Learning works with R.
- Course Rating: 4.7/5
- Duration: 18 hours
- Fees: INR 2,000 – INR 4,000 (click on the join now link to get 90% discount)
- Benefits: 128 video lectures, 9 articles, 3 downloadable resources, full lifetime access on mobile and TV, Udemy data science certification.
Join Now: Data Science and Machine Learning Bootcamp with R
Learning Outcomes
| R programming | Applications of R programming in data analysis |
| Data visualization | Learn the uses of R for handling CSV, Excel, SQL files, or web scraping. |
| Learn how to use R for data manipulation | Learn the use of R in Machine Learning Algorithms |
Python for Data Science and Machine Learning Bootcamp
In this course, students will learn how to program with Python, how to create data visualizations, and how to use Machine Learning with Python. The topics covered in this course are Python programming, NumPy with Python, web scraping with Python, linear regression, random forests, and more. Students need to have some prior experience in programming in Python to pursue this course.
- Course Rating: 4.6/5
- Duration: 25 hours
- Fees: INR 2,000 – INR 4,000 (click on the join now link to get 90% discount)
- Benefits: 165 lectures, 13 articles, 5 downloadable resources, full lifetime access on mobile and TV, and Udemy data science certification.
Join Now: Python for Data Science and Machine Learning Bootcamp
Learning Outcomes
| Learn to use Python for Data Science and Machine Learning | Learn the uses of Spark for Big Data Analysis |
| Learn to Implement Machine Learning Algorithms | Learn about the uses of NumPy in Numerical Data |
| Learn how to use Pandas for Data Analysis and Matplotlib for Python Plotting | Learn about the uses of Seaborn for statistical plots |
| Learn about the uses of Plotly in interactive dynamic visualizations | Learn the ways of using SciKit-Learn for Machine Learning Tasks |
| Understand K-Means Clustering, Logistic Regression, and Linear Regression | Learn about Random Forest and Decision Trees |
| Learn about Natural Language Processing and Spam Filters | Learn Neural Networks and Support Vector Machines |
Machine Learning A – Z: Hands-on Python & R in Data Science
‘Machine Learning A-Z: Hands-on Python & R in Data Science course’ is designed to help learn complex theories, algorithms, and coding libraries in a simpler way. The topics covered in this course are data reprocessing, regression, classification, clustering, NLP, deep learning, and more. The course contains exercises and projects to practice making models.
- Course Rating: 4.5/5
- Duration: 44.5 hours
- Fees: INR 2,000 – INR 4,000 (click on the join now link to get 90% discount)
- Benefits: 320 lectures, 73 articles, 38 downloadable resources, full lifetime access on mobile and TV, and Udemy data science certification
Join Now: Machine Learning A-Z: Hands-on Python & R in Data Science
Learning Outcomes
| Learn machine learning in Python and R | Practice making machine learning models |
| Learn how to use machine learning for personal purposes | Learn about reinforcement learning, NLP, and deep learning |
| Learn ways of handling advanced techniques like dimensionality reduction | Understanding which machine learning model to choose depending on the type of problem |
The Data Science Course 2023: Complete Data Science Bootcamp
‘The Data Science Course 2023: Complete Data Science Bootcamp’ is created by 365 Career and instructed by the 365 Career Team. It is created as a result of an effort in creating the most effective, time-efficient, and well-structured data science training available online and is suitable for beginners too as it starts with the fundamentals to gradually develop the skills.
- Course Rating: 4.6/5
- Duration: 30 hours
- Fees: INR 2,000 – INR 4,000 (click on the join now link to get 90% discount)
- Benefits: 488 lectures, 90 articles, 501 downloadable resources, full lifetime access on mobile and TV, and a Udemy data science certification
Join Now: The Data Science Course 2023: Complete Data Science Bootcamp
Learning Outcomes
| The course consists of all the toolboxes that are needed to become a data scientist | Learn in-demand data science skills such as Statistical analysis, and Python programming with NumPy, pandas, matplotlib, Seaborn, and others. |
| Learn about Advanced statistical analysis, Tableau, Machine Learning, and Deep learning | Gain an understanding of the field of data science |
| Learn preprocessing of data | Learn the mathematics behind Machine Learning |
| Learn to start coding in Python and how they are used for statistical analysis | Learn to perform linear as well as logistic regressions in Python |
| Learn how to carry out the cluster and factor analysis | Learn how to create Machine Learning algorithms in Python, using NumPy, stats models and scikit-learn |
| Understanding the power of deep neural networks | Learn the ways of improving Machine Learning algorithms by studying underfitting, overfitting, training, validation and various other practices |
R Programming A-Z: R for Data Science with Real Exercises!
‘R Programming A-Z: R for Data Science with Real Exercises course’ is created by Kirill Eremenko, Ligency I Team. This Udemy data science course is designed to teach Programming in R and R Studio along with Data Analytics, Data Science, Statistical Analysis, and more with the main focus on R programming.
- Course Rating: 4.6/5 (Bestseller)
- Duration: 10.5 hours
- Fees: INR 2,000 – INR 4,000 (click on the join now link to get 90% discount)
- Benefits: 82 lectures, 6 articles, full lifetime access on mobile and TV, and Udemy data science certification
Join Now: R Programming A-Z: R for Data Science with Real Exercises
Learning Outcomes
| Learn R programming and how to use it in R studio | Understanding the core principles of programming |
| Learn about creating vectors in R and variables | Understanding integer, double, logical, character and other types in R |
| Learn about creating while () and for () loop in R | Learn about building and using matrices in R Learn about matrix () function, rbind (), and cbind () |
| Learn to install packages in R and customize R studio | Learn the Law of Large Numbers and Normal distribution |
| Practice working with statistical, financial, and sports data in R | – |
Statistics for Data Science and Business Analysis
‘Statistics for Data Science and Business Analysis course’ is created by 365 Careers and is instructed by the 365 Career Team. It focuses on topics like Statistics, Inferential statistics, Hypothesis Testing, and Regression analysis. This Udemy data science course is suitable for aspiring people who want to learn about statistics, data science, business intelligence, business analysis, business executives, and others.
- Course Rating: 4.6/5 (Bestseller)
- Duration: 5 hours
- Fees: INR 2,000 – INR 4,000 (click on the join now link to get 90% discount)
- Benefits: 92 lectures, 28 articles, 98 downloadable resources, full lifetime access on mobile and TV, and Udemy data science certification
Join Now: Statistics for Data Science and Business Analysis
Learning Outcomes
| Learn about the fundamentals of statistics | Understanding working with different types of data |
| Learn about plotting different types of data | Learn to calculate the measures of central tendency, asymmetry, and variability |
| Learn to calculate correlation and covariance | Learn about distinguishing and working with different types of distributions |
| Learn how to estimate confidence intervals | Understand and perform hypothesis testing |
| Learn about making data-driven decisions | Learn about the mechanics of regression analysis |
| Learn how to carry out regression analysis | Understanding the uses of dummy variables |
| Understanding the concepts which are required for data science with Python and R | – |
Data Science A-Z: Real-Life Data Science Exercises Included
‘Data Science A-Z: Real-Life Data Science Exercises Included course’ is created by Kirill Eremenko, Ligency I Team. This Udemy data science course is designed to provide step-by-step learning of data science through real analytics examples, data mining, modeling, tableau visualization, and more. It will give a full overview of data science and experience the pain and difficulties a data scientist faces daily.
- Course Rating: 4.5/5
- Duration: 21 hours
- Fees: INR 2,000 – INR 4,000 (click on the join now link to get 90% discount)
- Benefits: 217 lectures, 7 articles, full lifetime access on mobile and TV, and a Udemy data science certification
Join Now: Data Science A-Z: Real-Life Data Science Exercises Included
Learning Outcomes
| Learn to perform all steps in a complex Data Science project successfully | Learn to create Basic Tableau Visualisations |
| Learn to perform Data Mining in Tableau | Learn the application of the Chi-Squared statistical test |
| Application of Ordinary Least Squares method for Creating Linear Regressions | Learn to create a Simple Linear Regression (SLR) and Multiple Linear Regression (MLR) |
| Learn to read statistical software output for created models | Understanding the uses of Backward Elimination, Forward Selection, and Bidirectional Elimination methods in creating statistical models |
| Learn to create a Robust Geodemographic Segmentation Model | Learn to read a Confusion Matrix |
| Learn to apply the Cumulative Accuracy Profile (CAP) for assessing models | Learn to build the CAP curve in Excel |
| Learn installation and navigation of SQL Server | Learn to clean data and look for anomalies |
| Learn to use SQL Server Integration Services (SSIS) for uploading data into a database Create Conditional Splits in SSIS | Learn how to deal with Text Qualifier errors in RAW data |
| Learn to create Scripts in SQL and apply SQL to Data Science projects | – |
Python A-Z: Python for Data Science with Real Exercises!
‘Python A-Z: Python for Data Science with Real Exercises course’ is designed for teaching Python programming for Data Analytics and Data Science along with tutorials on Statistical Analysis, Data Mining, and Visualization. It tries to provide step-by-step learning and is full of real-life analytical challenges, which one will be taught to solve them.
- Course Rating: 4.6/5
- Duration: 11 hours
- Fees: INR 2,000 – INR 4,000 (click on the join now link to get 90% discount)
- Benefits: 75 lectures, 6 articles, full lifetime access on mobile and TV, and Udemy data science certification
Join Now: Python A-Z: Python for Data Science with Real Exercises
Learning Outcomes
| Learn about programming with Python | Learn about coding in Jupyter Notebooks |
| Understanding of core principles of programming | Learn about creating variables |
| Understanding of integer, float, logical, string and other types in Python | Learn the process of creating while() and for() loop in Python |
| Learn the process of installing packages in Python | Learn about the Law of Large Numbers |
Complete Machine Learning & Data Science Bootcamp 2023
‘Complete Machine Learning & Data Science Bootcamp 2023’ is a beginner-friendly course on data science and machine learning. The course is taught by instructors who have years of experience in data science and machine learning and have worked with top companies like IBM, Tesla, Amazon, Apple, and more. The course is recently updated and uses the latest version of Python, Tensorflow 2.0, and other libraries. The course introduces data scientists’ skills and offers a chance to work on real-world projects. Some of the topics covered in this Udemy data science course are data exploration and visualizations, neural networks, deep learning, Python 3, and more.
- Course Rating: 4.6/5
- Duration: 43.5 hours
- Fees: INR 2,000 – INR 4,000 (click on the join now link to get 90% discount)
- Benefits: 377 lectures, 56 articles, 13 downloadable resources, 1 coding exercise, full lifetime access on mobile and TV, and a Udemy data science certification
Join Now: Complete Machine Learning & Data Science Bootcamp 2023
Learning Outcomes
| Learn about Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0 | Learn to master Machine Learning and use it |
| Learn the use of modern tools that big tech companies use | Learn to present Data Science projects to management and stakeholders |
| Learn about what Machine Learning model to choose for different types of problem | Learn about the best practices of Data Science Workflow |
| Learn to implement Machine Learning algorithms | Learn about programming in Python using the latest Python 3 |
| Learn about pre-processing, cleaning, and analyzing large data | Learn the ways of improving Machine Learning Models |
| Learn about NumPy and its uses in Machine Learning | Learn about Data Engineering and the use of different tools like Hadoop, Spark and Kafka |
| Learn about performing Classification and Regression modeling | Learn about applying Transfer Learning |
Machine Learning, Data Science and Generative AI with Python
This course is aimed at those who are familiar with basic coding or scripting. This comprehensive course covers a variety of topics including deep learning, generative AI, data visualization, and more, with a focus on practical applications. It is specifically designed for software developers and data analysts. If you are a fresher, it is recommended to watch basic Python tutorials before enrolling in this course. Also, it requires a PC capable of running Anaconda 3 or newer.
- Course Rating: 4.5/5
- Duration: 18.5 hours
- Price: Join now and get up to 90% off the original price
- Benefits: 1 downloadable resource, 6 articles, access on mobile and TV, certificate of completion
Join Now: Machine Learning, Data Science and Generative AI with Python
Learning Outcomes
| Build artificial neural networks with Tensorflow and Keras | Implement machine learning at a massive scale with Apache Spark’s MLLib |
| Classify images, data, and sentiments using deep learning | Make predictions using linear regression, polynomial regression, and multivariate regression |
| Data Visualization with Matplotlib and Seaborn | Understand reinforcement learning – and how to build a Pac-Man bot |
| Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA | Use train/test and K-Fold cross-validation to choose and tune your models |
| Build a movie recommender system using item-based and user-based collaborative filtering | Clean your input data to remove outliers |
| Design and evaluate A/B tests using T-Tests and P-Values | – |
Introduction to Machine Learning for Data Science
This is an introductory course on Machine Learning and Data Science, making it accessible to everyone. It covers fundamental concepts such as computer science, data, big data, artificial intelligence, and more. It helps students to understand and apply Machine Learning in Data Science effectively. It also includes a bonus course that delves into practical examples and essential data science tools.
- Course Rating: 4.6/5
- Duration: 5.5 hours
- Price: Join now and get up to 90% off the original price
- Benefits: 11 downloadable resources, 1 article, access on mobile and TV, certificate of completion
Join Now: Introduction to Machine Learning for Data Science
Learning Outcomes
| Learn about Computer Science, Algorithms, Programming, Data, Big Data, Artificial Intelligence, Machine Learning, and Data Science | Understand how these different domains fit together, how they are different, and how to avoid the marketing fluff. |
| To really understand computer technology has changed the world, with an appreciation of scale. | To know what problems Machine Learning can solve, and how the Machine Learning process works. |
| How to avoid problems with Machine Learning, to successfully implement it without losing your mind! | – |
Python for Machine Learning & Data Science Masterclass
This comprehensive course on Python, Data Science, and Machine Learning is designed for students with basic Python knowledge. The course covers a wide range of topics, including Python programming, data analysis with libraries like NumPy and Pandas, data visualization with Matplotlib and Seaborn, and advanced machine learning algorithms such as Linear Regression, K Nearest Neighbors, Decision Trees, Natural Language Processing, Support Vector Machines, and more. It’s suitable for beginner Python developers who are curious about entering the world of Machine Learning and Data Science with Python. ;
- Course Rating: 4.7/5
- Duration: 44 hours
- Price: Join now and get up to 90% off the original price
- Benefits: 33 downloadable resources, 6 articles, 4 coding exercises, access on mobile and TV, certificate of completion
Join Now: Python for Machine Learning & Data Science Masterclass
Learning Outcomes
| You will learn how to use data science and machine learning with Python | You will create data pipeline workflows to analyze, visualize, and gain insights from data. |
| You will build a portfolio of data science projects with real-world data. | You will be able to analyze your own data sets and gain insights through data science. |
| Master critical data science skills. | Understand Machine Learning from top to bottom. |
| Replicate real-world situations and data reports. | Learn NumPy for numerical processing with Python. |
| Conduct feature engineering on real-world case studies. | Learn Pandas for data manipulation with Python. |
| Create supervised machine learning algorithms to predict classes. | Learn Matplotlib to create fully customized data visualizations with Python. |
| Create regression machine learning algorithms for predicting continuous values. | Learn Seaborn to create beautiful statistical plots with Python. |
| Construct a modern portfolio of data science and machine learning resume projects. | Learn how to use Scikit-learn to apply powerful machine learning algorithms. |
| Get set up quickly with the Anaconda data science stack environment. | Learn best practices for real-world data sets. |
| Understand the full product workflow for the machine learning lifecycle. | Explore how to deploy your machine-learning models as interactive APIs. |
Data Science: Natural Language Processing (NLP) in Python
The course focuses on practical NLP applications. It covers topics like cypher decryption, spam detection, sentiment analysis, and more. It’s suitable for Python developers who want to learn more about NLP and machine learning. However, it’s not for absolute beginners. To take this course, students need to install Python and should be able to write basic Python code. They should also know how to install numerical libraries like Numpy, Scipy, Scikit-learn, Matplotlib, and BeautifulSoup.
- Course Rating: 4.6/5
- Duration: 12 hours
- Price: Join now and get up to 90% off the original price
- Benefits: Access on mobile and TV, certificate of completion
Join Now: Data Science: Natural Language Processing (NLP) in Python
Learning Outcomes
| Write your own cipher decryption algorithm using genetic algorithms and language modeling with Markov models | Write your own spam detection code in Python |
| Write your own sentiment analysis code in Python | Perform latent semantic analysis or latent semantic indexing in Python |
| Have an idea of how to write your own article spinner in Python | – |
Statistics for Business Analytics and Data Science A-Z™
This hands-on course focuses on statistical concepts in a practical and engaging manner, focusing on real-world examples. The topics covered in this course include distributions, the z-test, the Central Limit Theorem, hypothesis testing, confidence intervals, and statistical significance. It’s suitable for anyone looking to master statistics for business analytics, learn statistics from scratch, or gain hands-on experience with statistics.
- Course Rating: 4.5/5
- Duration: 6 hours
- Price: Join now and get up to 90% off the original price
- Benefits: 3 articles, access on mobile and TV, certificate of completion
Join Now: Statistics for Business Analytics and Data Science A-Z™
Learning Outcomes
| Understand what a Normal Distribution is | Understand standard deviations |
| Explain the difference between continuous and discrete variables | Understand what a sampling distribution is |
| Understand the Central Limit Theorem | Apply the Central Limit Theorem in practice |
| Apply Hypothesis Testing for Means | Apply Hypothesis Testing for Proportions |
| Use the Z-Score and Z-Tables | Use the t-Score and t-Tables |
| Understand the difference between a normal distribution and a t-distribution | Understand and apply statistical significance |
| Create confidence intervals | Understand the potential pitfalls of overusing p-Values |
Artificial Intelligence: Reinforcement Learning in Python
The course focuses on reinforcement learning, a branch of artificial intelligence where machines learn to make decisions through interaction with their environment. The topics covered in this course include multi-armed bandit problems, Markov Decision Processes, dynamic programming, Monte Carlo methods, Temporal Difference Learning, and approximation methods using deep neural networks. The course is suitable for those interested in learning AI beyond traditional supervised and unsupervised machine learning.
- Course Rating: 4.8/5
- Duration: 15 hours
- Price: Join now and get up to 90% off the original price
- Benefits: Access on mobile and TV, certificate of completion
Join Now: Artificial Intelligence: Reinforcement Learning in Python
Learning Outcomes
| Apply gradient-based supervised machine learning methods to reinforcement learning | Understand reinforcement learning on a technical level |
| Understand the relationship between reinforcement learning and psychology | Implement 17 different reinforcement learning algorithms |
Data Science: Deep Learning and Neural Networks in Python
The course covers the extension of binary classification models to multiple classes using the softmax function and delves into backpropagation. The course is suitable for students eager to learn more about machine learning and looking to incorporate neural networks into their data science pipeline. This hands-on course also includes practical examples and a course project focusing on predicting user actions on a website and facial expression recognition using deep learning. ;
- Course Rating: 4.6/5
- Duration: 12 hours
- Price: Join now and get up to 90% off the original price
- Benefits: Access on mobile and TV, certificate of completion
Join Now: Data Science: Deep Learning and Neural Networks in Python
Learning Outcomes
| Learn how Deep Learning really works (not just some diagrams and magical black box code) | Learn how a neural network is built from basic building blocks (the neuron) |
| Code a neural network from scratch in Python and NumPy | Code a neural network using Google’s TensorFlow |
| Describe different types of neural networks and the different types of problems they are used for | Derive the backpropagation rule from first principles |
| Create a neural network with an output that has K > 2 classes using softmax | Describe the various terms related to neural networks, such as “activation”, “backpropagation” and “feedforward” |
R Programming: Advanced Analytics in R for Data Science
This course is designed for individuals with basic R programming knowledge who want to advance their skills in data science and analytics with R. It covers various topics such as data preparation, median imputation, working with date-times, using lists, and applying functions like apply(), lapply(), and sapply(). The course includes real-life case studies, providing practical experience in financial analysis, and data analysis. This course is not suitable for complete beginners in R.
- Course Rating: 4.7/5
- Duration: 6 hours
- Price: Join now and get up to 90% off the original price
- Benefits: 6 articles, access on mobile and TV, certificate of completion
Join Now: R Programming: Advanced Analytics in R for Data Science
Learning Outcomes
| Perform Data Preparation in R | Identify missing records in dataframes |
| Locate missing data in your dataframes | Apply the Median Imputation method to replace missing records |
| Apply the Factual Analysis method to replace missing records | Understand how to use the which() function |
| Know how to reset the dataframe index | Work with the gsub() and sub() functions for replacing strings |
| Explain why NA is a third type of logical constant | Deal with date-times in R |
| Convert date-times into POSIXct time format | Create, use, append, modify, rename, access and subset lists in R |
| Understand when to use [] and when to use [[]] or the $ sign when working with lists | Create a timeseries plot in R |
| Understand how the apply family of functions works | Recreate an apply statement with a for() loop |
| Use apply() when working with matrices | Use lapply() and sapply() when working with lists and vectors |
| Add your own functions into apply statements | Nest apply(), lapply() and sapply() functions within each other |
| Use the which.max() and which.min() functions | – |
The Complete Machine Learning Course with Python
This comprehensive course focuses on Machine Learning and Python programming. It is suitable for students with basic Python programming knowledge and a good understanding of linear algebra. The course covers various topics, including deep learning, computer vision, and binary/multi-class classifications with updated content for Python 3.6 and 3.7. By completing this course, students will have a portfolio of 12 machine learning projects and the skills to tackle real-world problems using machine learning algorithms.
- Course Rating: 4.4/5
- Duration: 17.5 hours
- Price: Join now and get up to 90% off the original price
- Benefits: 3 articles, 2 downloadable resources, access on mobile and TV, certificate of completion
Join Now: The Complete Machine Learning Course with Python
Learning Outcomes
| Solve any problem in your business, job or personal life with powerful Machine Learning models | Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells & more |
| Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, unsupervised Machine Learning, etc. | – |
Probability and Statistics for Business and Data Science
This Probability and Statistics course is designed for individuals interested in applying probability and statistics to business or data science. It covers essential topics, including measurements of data, probability, common statistical distributions, hypothesis testing, regression analysis, and more. The course provides clear explanations, high-quality animations, and real-world case studies to help students grasp the concepts.
- Course Rating: 4.7/5
- Duration: 5 hours
- Price: Join now and get up to 90% off the original price
- Benefits: 10 articles, 35 downloadable resources, access on mobile and TV, certificate of completion
Join Now: Probability and Statistics for Business and Data Science
Learning Outcomes
| Understand the basics of probability | Be able to implement basic statistics |
| Understand how to use various statistical distributions | Apply statistical methods and hypothesis testing to business problems |
| Understand how regression models work | Implement one-way and two-way ANOVA |
| Understand Chi-Squared Tests | Be able to understand different types of data |
Intro to Data Science: Your Step-by-Step Guide To Starting
This Data Science course offers a structured path to master Data Science within 6 weeks. The course covers topics such as statistics, data visualization, machine learning with Python, and cloud computing. Students will use tools like SQL, Tableau, and Python to solve real-world problems and work on different Data Science projects. ;
- Course Rating: 4.5/5
- Duration: 5 hours
- Price: Join now and get up to 90% off the original price
- Benefits: 6 articles, 1 downloadable resource, access on mobile and TV, certificate of completion
Join Now: Intro to Data Science: Your Step-by-Step Guide To Starting
Learning Outcomes
| Cloud concepts & application in Data Science | Database concepts |
| Statistics fundamentals as needed in Data Science | Visualizations for data mining and presentation |
| Basics of Statistical Learning | Fundamentals of Machine Learning |
| Advanced Python concepts | – |
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Best Data Science Courses on Udemy: FAQs
Ques. Which is the best data science course on Udemy?
Ans. As per total student enrollments, Machine Learning A-Z: Hands-on Python & R In Data Science is the best-selling Udemy data science course with over 8.54 lakh student enrollments, 1.56 lakh student reviews, and a 4.5-star rating out of 5. Data Science and Machine Learning Bootcamp with R is the highest-rated Udemy data science course with a 4.7-star rating out of 5.
Ques. Are Udemy data science courses worth it?
Ans. Yes, Udemy data science courses are some of the best online data science courses to consider in 2023. Udemy data science courses stand out from other online platforms because of their fees which are less than INR 500 due to seasonal discounts back and forth and additional perks such as lifetime access, study material, and 24/7 instructors’ support. Moreover, Udemy is a globally recognized platform by top companies such as Google, Amazon, Netflix, and alike. Udemy data science courses have simple, to-the-point course content and ease of learning at your own pace compared to other websites like Coursera or Futurelearn that have a set duration to complete the course.
Ques. Is the Udemy course enough for data science?
Ans. Yes, Udemy Course is enough for Data Science. There are more than 480 Data Science Courses that can be pursued either by paying or for free. Each of the courses provides various knowledge and skills that are crucial in the field of Data Science while giving you the freedom to learn anytime you want. Among the different courses, you can check the reviews and course contents and choose which course to pursue wisely to not waste time and money.
Ques. Which course is best for data science in Udemy as per student reviews on Quora?
Ans. Udemy offers various high-rated Data Science Courses. Given below are some of the most popular and highly rated Courses.
- Data Science and Machine Learning Bootcamp with R
- Python for Data Science and Machine Learning Bootcamp
- Machine Learning A-Z™: Hands-On Python & R In Data Science
- The Data Science Course 2023: Complete Data Science Bootcamp
- R Programming A-Z™: R For Data Science With Real Exercises!
Ques. Is Udemy certificate valid?
Ans. No, Udemy Certificate is not valid. Udemy is not an accredited institution but rather a platform where course providers and learners are brought together. The certification does not in itself provide much value, but serves as a perk to your educational qualifications and helps you stand out from others.
Ques. Is it worth buying a course on Udemy?
Ans. If you want to learn about Data Science from the basics or looking for career advancement in the field, it’s worth buying a Course. Udemy offers numerous Courses which provide various skills and knowledge from fundamentals to advanced levels. Moreover, adding a Certificate to your resume helps you stand out from others.
Ques. Can I put Udemy courses on my resume?
Ans. Yes, you can put Udemy Certifications on your resume. They do not have much importance as educational qualifications, however, they act as an additional perk and help you stand above others when you list them in personal advancement in your CV.
Ques. Is Udemy data science Bootcamp good?
Ans. The Udemy Data Science Bootcamp has a rating of 4.6 on Udemy and more than 4,42,000 students enrolled for the course. And is one of the bestselling Data Science Courses in Udemy. They are suitable for all levels of learners i.e beginners, intermediate, and advanced. It will benefit most for Beginners level learners. You can check out the course content and reviews and see it for yourself if it provides the skills you are looking for.
Ques: Is Data Science hard for beginners?
Ans. Data Science can be a little tough for beginners due to its multidisciplinary nature, involving programming, statistics, and domain knowledge. However, with dedication, practice, and the right resources, beginners can gradually build the necessary skills and master data science.
Ques: Does data science require coding?
Ans. Yes, data science typically requires coding. Proficiency in programming languages like Python or R is essential for tasks such as data manipulation, analysis, and machine learning model development, which are fundamental to data science.
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