Data Science

Data Science comprises multiple fields including mathematics, statistics, coding, analytics, Artificial Intelligence, and Machine Learning. Simply put, the process involves dissecting vast data sets to get useful insights. It is projected that the global Data Science sector will be valued above USD 95 billion in 2024, recording a remarkable compound annual growth rate (CAGR) of 27.6% in the subsequent periods. 

Data Science Course

As per data from the US Bureau of Labor Statistics, the standard yearly income for a Data Scientist is around USD 103,500. So, if you are a beginner you can start from simple summary videos or from free platforms that include minimal real projects or practice problems. While online e-learning platforms provide different levels of skills, and teach topics like analyzing data, Deep Learning, and coding.

Online courses help students learn how to work with Big Data, which is a key part of almost all data studies today.

Why pursue a Data Science Course on Udemy?  

Udemy stands out as a leading online learning platform, providing affordable and accessible education. With a vast library of courses, Udemy offers multiple Data Science courses for different skill levels. The platform’s intuitive interface and the ability to learn at your own pace make it an ideal choice for anyone eager to master Data Science techniques. 

1. Data Science: Master Machine Learning without Coding  

This course will teach you the fundamentals of Data Science and Machine Learning if you do not know how to code. The course uses RapidMiner to teach Data Science skills, and neither knowledge nor coding experience is required. I was able to create predictive models using machine learning algorithms without having to write any code. However, this course requires high school-level math knowledge.

Who would benefit most from taking this course? 

This course is perfect for those who have no coding knowledge. It uses a tool called RapidMiner to solve real Data Science problems like predicting house prices using a Regression Algorithm. This makes it great for those who deal with big data for statistical purposes but can’t become coding experts immediately.

What’s good about the course? 

  • The course starts by introducing the tools and concepts that will be used later on. 
  • It starts with the basics but also includes problem-solving exercises and quizzes for practical application. 
  • The lectures are not too long and keep the students engaged. Also, the fact that it doesn’t include coding makes it less scary for beginners in Data Science.

What could be improved? 

  • The course could go into more detail about how RapidMiner works, rather than just touching the surface. 
  • It could also include more practical examples like forecasting. 
  • The explanations in the lectures could be longer and clearer, they seem a bit too brief and vague.

2. Intro to Data Science: QuickStart Guide + AI & ChatGPT Prize 

This course serves as an intro to Data Science and covers all the important aspects of the field. It discusses Data Visualization, Databases, Python, and other Data Science tools that can turn you into a Data Scientist in just 6 weeks. The course proves that Data Science isn’t as tough as it seems, as it spends ample time explaining everything from Python to Cloud with Data Science. So, this course offers a clear pathway to master Data Science skills.

Who should take this course? 

Anyone truly interested in Data Science will find this course beneficial. It’s perfect for individuals aiming to move into the Data Science field, students planning a career in Data Science, or current Data Scientists looking for career progression. Since the average yearly salary of a Data Scientist is approximately USD 103,500, this makes it a very appealing career to start or switch to.

What’s good about the course? 

  • The course touches on major areas connected to Data Science like Tableau, Cloud, and Python. 
  • It includes practical exercises for those wishing to put their knowledge into action. 
  • It’s a well-structured course that takes the time to define key concepts, beneficial to both beginners and experts.

What could be improved? 

  • The course could focus more on detailed explanations rather than application. 
  • Some sections lack video lectures, which would have helped in clarifying concepts better. 
  • As an introductory course, it’s a bit lengthy and might take more time than one would expect. 

3. The Data Science Course: Complete Data Science Bootcamp 2024 

This course is a thorough guide to Data Science that covers an array of topics ranging from Mathematics, Statistics, and Python to Machine & Deep Learning. You’ll learn how to get data ready for analysis and apply these skills to real-life business challenges, which essentially makes you job-ready. This course is a full kit of Data Science essentials and can help you add an array of Data Science techniques to your resume. It offers an enriching experience with its coding tasks, real-world examples, and detailed explanations.

Who should take this course? 

Since this course is so wide-ranging, it caters to everyone, whether they’re a beginner or an expert. It offers an assortment of case studies, tools, appendices, and coding tasks within the sphere of Data Science. If you’re looking for a one-stop resource to answer all your questions or if you want one course that fulfils all your learning needs, then this course is perfect for you.

What’s good about this course? 

  • It’s truly comprehensive as it addresses every facet of Data Science. 
  • It forms a one-course-fits-all solution to learn about Data Science using case studies and coding exercises. 
  • It’s as useful for beginners as it is for experts.

What could be improved? 

  • Some coding exercises may be difficult for beginners because they need more information than what’s provided. 
  • It doesn’t supply PDFs for the complex sections, making it hard to review those chapters. 
  • There are instances when the course might feel a bit hectic and rushed.

4. Data Science: Supervised Machine Learning in Python  

This course focuses on teaching Machine Learning Algorithms using Python with Scikit-Learn. I learned about various tools like K Nearest Neighbours (KNN), Bayes Classifiers, and Perceptron. Through this course, you’ll be able to put machine learning web services into action and figure out the difference between traditional machine learning and deep learning. Just keep in mind that you should have some experience with Python, NumPy, Pandas, and Gaussian distribution, along with some coding skills.

Who should take this course? 

If you are keen on deepening your understanding of Machine Learning, this course is a good fit for you. It also serves as a stepping stone to Artificial Intelligence. Regardless of whether you’re a student or a working professional, if you’re dealing with a data set that needs machine learning, this course has got you covered.

What’s good about this course? 

  • The course lays out all the machine learning algorithms systematically, providing a detailed understanding. 
  • It simplifies basics using examples and tackles complex concepts like decision trees in an easily digestible way.

What could be improved? 

  • The course assumes that students can navigate all the setup procedures independently. 
  • It doesn’t completely cover all the minor topics related to a major subject. 
  • Also, some of the techniques taught in this course take a while to get a grasp of.

5. Statistics & Mathematics for Data Science & Data Analytics 

The core of this course lies in Statistics and Mathematics for Data Science & Business Analytics, which are key to finding valuable insights in a messy dataset. The course helped me brush up on my Statistics and Mathematical foundations and how to apply them in Data Science. Topics like hypothesis testing, probability distribution, Regressions, etc. are covered, and they all find use in Business Analytics, Data Manipulation, Organization, and solving problems.

Who should take this course?

The course centers around Statistics and Mathematics in Data Science, so it’ll be beneficial if you’re looking to grasp the relationship between Statistics and Data Analysis. I believe it’s especially useful if you’re from a Statistical background aiming to use that expertise in Data Science. So, if you’re a student or a professional who regularly deals with large datasets and aims to draw meaningful conclusions that can assist businesses or broadly for data gathering, then this course will benefit you.

What’s good about the course?

  • What I liked was the simplicity of the language and the directness of the examples. 
  • The lecture lengths are suitable – brief enough to keep attention whilst packing in plenty of details about each topic. 
  • The course is well organized and sets up a solid basis for more complex concepts.

What could be improved?

  • The course could do with more downloadable resources as this means students have to jot down formulas from the video lecture. 
  • More quizzes and practice exercises would be beneficial. 
  • Also, I would have appreciated the inclusion of more advanced topics like ANOVA analysis.

6. Python for Machine Learning & Data Science Masterclass

This course is all about using Python in Machine Learning & Data Science, exploring Python Libraries like NumPy, Pandas, Matplotlib, and Scikit-Learn. I was able to dive into how Python connects with Data Science and grasp Machine Learning concepts. It offers an optional quick Python course and teaches how to simulate real-world scenarios and data reports. The course also explains the usage purposes of Data Science and the Mathematical theory behind Machine Learning algorithms.

Who should take this course?

Since the average salary of a Python Developer is around USD 106,611, Python coding is a sought-after skill, especially in the Data Science field. This course is great for those looking to shift from Python to Data Science or those who want to enhance their Data Science know-how with Python. If you’re a Python Developer wanting to venture into Data Science and Machine Learning, this course fits the bill.

What’s good about the course? 

  • The exercises in the course are thorough, requiring no extra studying or Googling for missing pieces of information. 
  • The course packs in excellent data, covering the basics of Python tools.

What could be improved?

  • Some sections seemed to get more attention than others. 
  • More practice exercises could have been beneficial. 
  • The course could do with additional content like oversampling, sampling, and some elements of neural networks.

7. Data Science and Machine Learning Bootcamp with R

This course gives a good dive into programming with R, deals with its advanced features, and showcases practical applications like web scraping. The machine learning section is well-rounded, ranging from basics to neural networks. It also dedicates a section to data visualization using tools like ggplot2 and plot and goes in-depth into machine learning, covering everything from linear regression to neural networks. If you’re seeking an affordable, comprehensive learning experience in data science, this is it! I’d highly recommend this to anyone looking to become a data scientist.

Who should take this course? 

The course is a great fit for both beginners and experienced data science developers. It addresses the basics of programming with R, explores its advanced features, and teaches practical applications like web scraping and R to SQL. If you’ve got an interest in venturing into data science and want to become a data scientist without breaking the bank, this is the course to go for.

What’s good about the course? 

  • The best part about the course for me was its hands-on approach – plenty of exercises, projects, and real-world examples that allowed me to immediately put into action what I learned and check my understanding. 
  • Plus, the teaching style is straightforward, making it beginner-friendly and easy to grasp the basics of R programming.

What could be improved?

  • While it attempts to shed light on machine learning concepts, I felt they should have been dealt with in more detail. 
  • Certain students, including me, felt there was more focus on applying formulas in R than providing a comprehensive understanding and interpretation of machine learning results. 
  • Additionally, it missed covering some important libraries such as Data tables, which are key for exploratory data analysis (EDA).

8. Data Science A-Z: Hands-on Exercises & ChatGPT Prize [2024] 

This course is a step-by-step guide to Data Science, offered by Kirill Eremenko. What stood out for me were the real-life examples used to clarify concepts like Data Mining, Tableau Visualization, and modeling. The course taught me to successfully execute all stages of a complex Data Science project, and how to use tools such as SQL, SSIS, Tableau, and Gretl. One unique aspect is the course provides what they refer to as ‘pre-planned pathways’, allowing you to tailor your learning journey to fit your specific needs.

Who should take this course? 

If you’re someone curious about Data Science, this course has got you covered, as it spans the entire field from start to end. Particularly, if you’re someone wishing to master Data Science within a single course without investing too much of your time, this is a great pick. Those who stand to benefit the most are those seeking to enhance their skills in Data Mining, Statistical Modelling, Data Preparation, and overall, Data Science.

What’s good about the course? 

  • The course was enlightening and well-assembled for me. 
  • It struck a good balance of theory and practical exercises. 
  • Moreover, it covers a wealth of information, and the time required to complete the course seemed reasonable.

What could be improved?

  • The course seems to promise coverage of more topics than it delivers. 
  • Also, some sections could have used more context and structure to ensure clear understanding. 
  • Lastly, the parts discussing the ETL phase seem a bit outdated.

9. Data Science: Deep Learning and Neural Networks in Python 

This course is an intermediate-level guide that dives into the theory of neural networks for machine learning. It utilizes Python and TensorFlow extensively. During the course, I learned how deep learning operates and coded a neural network using Google’s TensorFlow. The intriguing part is that the course goes beyond basic regression exercises and displays a learning system all on its own, similar to the mechanism that powers AI technologies like Open AI, ChatGPT, DALL-E Midjourney, etc. This course is mainly about building and understanding concepts rather than just learning and using them.

Who should take this course? 

Since it’s an intermediate-level course on neural networks in Python, it’ll fit you well if you’re a student interested in Machine Learning. It provides you with handy tools and techniques for working with neural networks. Additionally, professionals wanting to integrate neural networks into Machine Learning and Data Science pipelines, or aiming to use more advanced models, will find the course beneficial.

What’s good about the course? 

  • The clarity of the explanations stood out, along with the use of plenty of examples. 
  • The course was comprehensive, giving a good run-through of the basic and crucial elements of deep learning and backpropagation.

What should be improved?

  • The course assumes that you’re aware of several equations beforehand. 
  • Some might find the course to be a tad too theoretical. 
  • Moreover, it uses an older version of TensorFlow, which might create a slight learning curve.

10. Advanced Data Science Techniques in SPSS     

This is a pretty advanced Data Science course that will level up your SPSS skills as it grapples with some high-level data analysis methods. But to get the most out of it, you’d be better off with a basic understanding of SPSS and a decent grasp of Statistics. Trust me, it was in this course that I learned about Regression analysis, Cluster analysis, Survival analysis, neural networks, and decision trees.

Who should take this course? 

Despite its advanced level, students and researchers can find this course beneficial. Especially if you love Data Science, then this is your pick.

What’s good about the course? 

  • It blends theory and practical examples brilliantly, making learning a joyful journey. 
  • Plus, the lecturer’s approach simplifies the content and makes it super easy to understand.

What could be improved?

  • Considering this is an advanced course, it’s surprising that it doesn’t offer more examples and exercises. 
  • Plus, it doesn’t really go into explaining topics tackled in other courses by the same lecturer.
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By Nikita Joshi

A creative advocate of multi-disciplinary learning ideology, Nikita believes that anything can be learned given proper interest and efforts. She completed her formal education in BSc Microbiology from the University of Delhi. Now proficiently dealing with content ideation and strategy, she's been a part of Coursevise since August 2023 working as a content writer Having worked with several other things during these two years, her primary fields of focus have been SEO, Google Analytics, Website Traffic, Copywriting, and PR Writing. Apart from all that work, Nikita likes to doodle and pen down her rhymes when she feels free.

4.9 /5
Based on 13 ratings

Reviewed by 13 users

    • 2 months ago

    What is Cluster analysis in data science?

      • 2 months ago

      Cluster analysis in data science is like grouping similar things. It helps find patterns in data by sorting them into clusters based on similarities. It’s handy for organizing customers by their buying habits or grouping images with similar features. So, cluster analysis makes data easier to understand by putting similar things into groups.

    • 2 months ago

    Is learning R important for Data Science?

      • 2 months ago

      Learning R for data science can be helpful because it’s a powerful tool for statistical analysis and data visualization. Many companies and researchers use R for data analysis for academia and healthcare. However, there are also similar tools and languages like Python. So, while learning R can be beneficial, it’s not mandatory.

    • 2 months ago

    How Statistics and Mathematics are related to Data Science?

      • 2 months ago

      Statistics and math are crucial for data science because they help you make sense of data. Statistics spots patterns, while math lets you play around with numbers. Together, they’re like the building blocks of data science, helping you find patterns in raw data and make predictions.

    • 2 months ago

    What are the benefits of learning Machine Learning Algorithms using Python?

      • 2 months ago

      Learning machine learning algorithms using Python is great because Python is easy to understand, especially for beginners. Plus, Python has libraries (like TensorFlow, PyTorch, and scikit-learn), which provide pre-built algorithms and tools for machine learning tasks. With Python, you can find plenty of help and tutorials online, making learning easier.

    • 2 months ago

    Can I learn Data Science without coding?

      • 2 months ago

      Learning data science without coding is possible to some extent, but coding is a crucial part of it. Coding helps you analyze and work with data effectively using tools like Python or R. While there are beginner-friendly tools that require less coding, having coding skills boosts your abilities and job opportunities in data science.

    • 3 months ago

    Is it hard to learn data science?

      • 2 months ago

      This is a subjective question. For example, a student interested in Data Science can find it interesting to learn. Also, students with experience in programming looking to excel or switch careers would like the courses on Udemy as well.

    • 3 months ago

    Is Udemy free to join?

      • 2 months ago

      There are multiple free courses in Udemy that learners can pursue. Hence, students can upskill themselves, and advance in their careers. However, it is advisable to pursue courses that can meet the industry requirements and skill levels of an individual.

    • 3 months ago

    Does Udemy charge monthly?

      • 2 months ago

      Yes. Students can enroll in Udemy with a monthly charge of $29.99 (plus applicable transaction taxes). Furthermore, this will start after your 7-day free trial; students can cancel this at any time.

    • 3 months ago

    Can I get a job by learning data science in Udemy?

      • 2 months ago

      Students after learning Data Science in Udemy can show their skills to the recruiter, which can provide job opportunities. However, for a job, students need to gain a comprehensive knowledge of Data Science along with formal education.

    • 3 months ago

    Is Udemy good for learning data science?

      • 2 months ago

      Udemy is a great platform for learning data science. This is because, Udemy has multiple courses, and students can choose any one of those, depending on their interest and expertise. These courses can provide the skills required to build a portfolio and learners can use the certificates to get employment opportunities.

    • 3 months ago

    Which data science course is best in Udemy?

      • 2 months ago

      Most data science courses in Udemy are top-rated. Among all the courses a few of the best courses include “Data Science: Master Machine Learning without Coding ”, “The Data Science Course: Complete Data Science Bootcamp 2024”, “Advanced Data Science Techniques in SPSS ”, etc.

    • 3 months ago

    Does data science have a future?

      • 3 months ago

      Yes, data science definitely has a bright future!

      Data science involves using data to solve problems and make decisions. As technology continues to advance, the amount of data being generated is increasing rapidly. This means there is a growing need for people who can analyze and make sense of all this data.

    • 3 months ago

    Does data science require coding?

      • 3 months ago

      yes, data science does require coding, but it’s not as daunting as it might seem.

      Coding is an essential part of data science because it allows you to manipulate, analyze, and visualize data. However, you don’t necessarily need to be a software developer or have extensive coding experience to start learning data science.

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