In today's era, data science is the most evolving field changing the technology landscape. For students and technology professionals who want to go into the field of data science, there is an abundance of data science courses available today to enhance an individual's skills. Here, we have compiled a list of the best data science courses that you can take under $50.
1. Coursera Data Science Specialization (Johns Hopkins University)
The Data Science Specialization from Johns Hopkins University on Coursera is a comprehensive series of 10 courses designed to cover every topic in data science. These courses include:
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The data scientist's toolbox
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R Programming
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Acquiring and cleaning data
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Exploratory Data Analysis
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Reproducible research
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Statistical Inference
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Regression Model
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Practical Machine Learning
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Data product development
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Data Science Capstone
This specialization is particularly valuable in that it teaches data science using one of the most popular programming languages in the field, R. The courses are well-structured and provide both theoretical knowledge and practical experience through hands-on projects.
2. Python Bootcamp for Data Science and Machine Learning by Udemy
In this Udemy course taught by Jose Portilla, you'll learn basic data science and machine learning concepts using Python. The course covers:
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Python Basics
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NumPy for Numerical Data
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Pandas for Data Analysis
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Matplotlib and Seaborn for Data Visualization
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Plotly and Cufflinks for interactive visualization
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SciKit-Learn for Machine Learning
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K-means clustering, decision trees, random forests
With over 25 hours of video lectures, this course is suitable for beginners and intermediate learners who want to improve their Python and .
3. Data Science AZ™: Practical Data Science Exercises by Udemy
Created by Kirill Eremenko and the SuperDataScience team, this Udemy course offers a hands-on approach to learning data science. Key topics include:
This course is based around real life and allows students to apply their theoretical knowledge to real-life scenarios – perfect for those who want to learn by doing.
4. Complete Machine Learning and Data Science Bootcamp by Udemy 2023
In this Udemy course, Andrei Neagoie covers both Data Science and Machine Learning extensively. The course covers:
With over 40 hours of content, this course is thorough and perfect for anyone wanting to gain a deeper understanding of data science and machine learning using Python.
5. Data Science and Machine Learning Bootcamp with R by Udemy
This Udemy course, taught by Jose Portilla, focuses on data science and machine learning using R. The course covers:
The course is designed to be comprehensive and practical, using real-world examples and exercises to solidify the concepts learned.
6. DataCamp Introduction to Data Science
DataCamp's Introduction to Data Science is one of the most learner-friendly data science courses based on Python programming. The course covers:
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Python Basics
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Data manipulation with Pandas
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Visualizing Data with Matplotlib
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Introduction to Machine Learning
One of the main advantages of the DataCamp platform is that students can easily learn and/or practice by coding within a browser environment, making it an ideal approach for beginners.
7. Microsoft Data Science Fundamentals (edX)
Microsoft's Data Science Fundamentals course on edX will help you build a strong foundation. The course covers:
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Data Analysis and Visualization
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Probability and Statistics
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Introduction to Machine Learning
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The Data Science Process
This course is part of Microsoft's Data Science Professional program and is a great starting point for those who want to get into the field without spending too much.
8. IBM Data Science Professional Certification by Coursera
This professional certificate from IBM on Coursera is made up of nine courses designed to build learners' skills. The courses include:
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What is Data Science?
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Free Software Utilities for Data Analysis
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Data Science Methodology
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Signals and Systems for Bioengineering, Information Technology, and Energy
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Database and SQL Topics for Data Science
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Data Analysis with Python
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Data Visualization with Python
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Machine Learning with Python
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Applied Data Science Capstone
The programme is very comprehensive and combines both theory and real-life experience.
9. Machine Learning AZ™: Practical Python and R for Data Science (Udemy)
Created by Kirill Eremenko and Hadelin de Ponteves, this course covers machine learning concepts in both Python and R. Created by Kirill Eremenko and Hadelin de Ponteves, this course covers machine learning concepts in both Python and R. The course contents include:
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Data Preprocessing
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Algorithms include regression, classification, and clustering.
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Association Rule Learning
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Reinforcement learning
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Natural Language Processing
The essence of this course is to teach data science from two different analytical and computational angles: Python and R.
With the widespread availability of low-cost online learning programs, data science education is now within reach of anyone with a web connection. All of the programs featured here are under $50 and offer high-quality material from well-known schools and experienced instructors. For those unsure of how to break into the industry, these programs are a good starting point, as they are useful not only for learning new concepts but also for masters looking to improve their existing knowledge. If hired, you will gain the competencies and knowledge you need to succeed in the rapidly growing field of data science.
FAQ
What are the important features to look for in a data science course under $50?
When choosing a data science course under $50, it's important to consider a few key features to ensure you receive a quality education: First, look at the course content and curriculum. The course should cover fundamental topics such as programming language (Python or R), data analysis, machine learning, statistics, and data visualization. Hands-on experience is essential in data science, so look for courses that include hands-on projects and real-life case studies.
Additionally, consider the qualifications of the instructor and the reputation of the platform offering the course.
How can you be sure that the data science course you choose is suitable for beginners?
To determine if a data science course is suitable for beginners, look for several indicators in the course description and structure. First, see if the course includes introductory modules that cover the fundamentals of data science, such as an overview of the field, basic statistics, and introductory programming in Python or R. These foundational topics are important for beginners to understand before moving on to more advanced concepts.
Read the course syllabus to see if the content progresses logically from basic to more complex topics. Additionally, beginner-friendly courses often include lots of hands-on exercises, guided tutorials, and practical examples to help you reinforce your learning.
Which platform offers the best data science courses under $50?
Several online learning platforms offer high-quality data science courses for under $50. Udemy is a popular choice known for frequently hosting sales and drastically lowering course prices. Udemy courses feature comprehensive content, practical exercises, and experienced instructors. Another great platform is Coursera, which offers courses from well-known universities and institutions. Monthly subscription fees are usually around $49, but many courses are free to take and financial aid is available if you qualify.
DataCamp is another valuable resource that offers interactive courses focused on data science and programming, with affordable pricing starting at around $25 per month.
Are affordable data science course certificates recognised by employers?
Certificates from affordable data science courses, especially those offered by reputable platforms such as Coursera, Udemy, edX, and DataCamp, are commonly recognized by employers. While these certificates may not carry the same weight as a formal degree, they demonstrate a passion for learning and the ability to master relevant skills. Employers often value candidates who actively upskill through online courses, especially if these courses cover practical, in-demand skills.
When listing these certificates on your resume or LinkedIn profile, it’s a good idea to highlight the specific skills and knowledge you gained, as well as the projects and practical experience that were included in the courses.
How can you effectively combine multiple affordable data science courses to create a comprehensive study plan?
Combining multiple affordable data science courses to create a comprehensive study plan requires careful planning and a clear understanding of your learning objectives. First, identify the core skills and knowledge areas you need to cover, such as programming, statistics, machine learning, data visualization, and domain-specific applications. Then, select courses that specialize in each of these areas. For example, you could take a Python programming course on Udemy, followed by a machine learning course on Coursera and a data visualization course on DataCamp.
It's important to sequence your courses logically, starting with foundational topics and progressing to more advanced themes. Allocate time between courses for hands-on practice and projects to reinforce what you've learned. Additionally, track your progress and regularly review and reinforce your knowledge.
