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In today’s era, data science is the most evolving sector changing the technological landscape. Whether you are a student or a tech professional looking to get into the field of data science, there are abundant data science courses available today to enhance an individual’s skills. Here, we have curated a list of best data science courses available under US$50:
The Data Science Specialization by Johns Hopkins University on Coursera is a comprehensive series of ten courses designed to cover the full spectrum of data science topics. These courses include:
The Data Scientist’s Toolbox
R Programming
Getting and Cleaning Data
Exploratory Data Analysis
Reproducible Research
Statistical Inference
Regression Models
Practical Machine Learning
Developing Data Products
Data Science Capstone
This specialization is particularly valuable because it teaches data science using R, one of the most popular programming languages in the field. The courses are well-structured, offering both theoretical knowledge and practical experience through hands-on projects.
This Udemy course, taught by Jose Portilla, covers essential data science and machine learning concepts using Python. The course includes:
Python Basics
NumPy for Numerical Data
Pandas for Data Analysis
Matplotlib and Seaborn for Data Visualization
Plotly and Cufflinks for Interactive Visualizations
SciKit-Learn for Machine Learning
K-Means Clustering, Decision Trees, and Random Forests
With over 25 hours of video lectures, this course is suitable for beginners and intermediates looking to enhance their skills in Python and its applications in data science.
This Udemy course, created by Kirill Eremenko and the SuperDataScience Team, provides a hands-on approach to learning data science. Key topics include:
Data Mining
Statistics and Probability
Data Visualization
Machine Learning
Deep Learning
The course is structured around real-life data science projects, allowing students to apply theoretical knowledge to practical scenarios. It’s an excellent choice for those who prefer learning by practising.
In this Udemy course, Andrei Neagoie covers both data science and machine learning extensively. The course includes:
Python Programming
Numpy and Pandas
Data Visualization with Matplotlib and Seaborn
Statistics
Linear Regression
Logistic Regression
Decision Trees and Random Forests
Support Vector Machines
With over 40 hours of content, this course is thorough and well-suited for those who want an in-depth understanding of data science and machine learning using Python.
This Udemy course, taught by Jose Portilla, focuses on data science and machine learning using R. The course covers:
R Programming Basics
Data Analysis with R
Data Visualization with GGPlot2
Machine Learning Algorithms
The course is designed to be comprehensive and practical, with real-world examples and exercises to help solidify the concepts learned.
DataCamp’s ‘Introduction to Data Science’ is one of the best and easy to understand Data Science courses available for learners which is based on Python programming. The course includes:
Python Basics
Data Manipulation with Pandas
Data Visualization with Matplotlib
Introduction to Machine Learning
One of the key advantages of the DataCamp platform is that a student can easily learn and/or practice by coding right within the browser environment, the approach perfect for starters.
The Data Science Essentials course by Microsoft, offered on edX which helps to lay a good foundation. The course covers:
Data Analysis and Visualization
Probability and Statistics
Introduction to Machine Learning
Data Science Process
This course is part of Microsoft’s Professional Program in Data Science and is an excellent starting point for those looking to get into the field without spending much.
This professional certificate by IBM on Coursera comprises a sequence of nine courses aimed at building the learner’s proficiency in data science. The courses include:
What is Data Science?
Free software utilities for use in Data analysis
Data Science Methodology
Signals and Systems for Bioengineering, Information Technology, and Energy
Topics on databases and SQL for data science
Data Analysis with Python
Data Visualization with Python
Machine Learning with Python
Applied Data Science Capstone
This program is very extensive, and it combines both the theories and real life.
This Udemy course, created by Kirill Eremenko and Hadelin de Ponteves, covers machine learning concepts in both Python and R. The course created by Kirill Eremenko and Hadelin de Ponteves, covers machine learning concepts in both Python and R. The course includes:
Data Preprocessing
The algorithms range from Regression, Classification, and Clustering.
Association Rule Learning
Reinforcement Learning
Natural Language Processing
The essence of the course is that it teaches data science from two different angles of analysis and computation, namely Python and R.
The wide availability of low-cost online learning programs has made data science education more accessible to everyone with a web connection. All of the mentioned programs cost less than US$50, and they offer high-quality material from famous schools and experienced tutors. For individuals that have no idea of how to get into the business, these programs are a good starting point because apart from learning new concepts, they can be useful for masters who want to improve on already existing knowledge. When employed, then you will have the required capacities and knowledge to triumph in the rapidly growing discipline of data science.
What are the essential features to look for in a data science course under $50?
When selecting a data science course under $50, it’s crucial to consider several key features to ensure you’re getting quality education. Firstly, check the course content and curriculum. The course should cover fundamental topics such as programming languages (Python or R), data analysis, machine learning, statistics, and data visualization. Look for courses that include practical projects and real-life case studies, as hands-on experience is essential in data science.
Additionally, consider the credentials of the instructors and the reputation of the platform offering the course.
How can I ensure that the data science course I choose is suitable for beginners?
To ensure that a data science course is suitable for beginners, look for several indicators in the course description and structure. Firstly, check if the course includes introductory modules that cover the basics of data science, such as an overview of the field, basic statistics, and introductory programming in Python or R. These foundational topics are crucial for beginners to understand before moving on to more advanced concepts.
Read through the course syllabus to see if the content progresses logically from basic to more complex topics. Additionally, beginner-friendly courses often include plenty of hands-on exercises, guided tutorials, and practical examples that help reinforce learning.
What platforms offer the best data science courses under $50?
Several online learning platforms offer high-quality data science courses for under US$50. Udemy is a popular choice, known for its frequent sales that bring course prices down significantly. Courses on Udemy often feature comprehensive content, practical exercises, and experienced instructors. Another excellent platform is Coursera, which offers courses from renowned universities and institutions. While the monthly subscription fee is typically around $49, many courses can be audited for free, and financial aid is available for those who qualify.
DataCamp is another valuable resource, offering interactive courses focused on data science and programming. Their monthly subscription starts at approximately US$25, making it an affordable option.
Are the certificates from affordable data science courses recognized by employers?
Certificates from affordable data science courses, particularly those offered by reputable platforms like Coursera, Udemy, edX, and DataCamp, are generally recognized by employers. While these certificates may not carry the same weight as a formal degree, they demonstrate your commitment to learning and your ability to acquire relevant skills. Employers often appreciate candidates who take the initiative to upskill through online courses, especially if these courses cover practical and in-demand skills.
When listing these certificates on your resume or LinkedIn profile, it’s beneficial to highlight the specific skills and knowledge you gained, as well as any projects or hands-on experiences included in the course.
How can I effectively combine multiple affordable data science courses to create a comprehensive learning plan?
Creating a comprehensive learning plan by combining multiple affordable data science courses involves careful planning and a clear understanding of your learning objectives. Start by identifying 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 might take a Python programming course on Udemy, followed by a machine learning course on Coursera, and a data visualization course on DataCamp.
It’s essential to sequence the courses logically, beginning with foundational topics before progressing to more advanced subjects. Allocate time for hands-on practice and projects between courses to reinforce what you’ve learned. Additionally, keep track of your progress and periodically review and consolidate your knowledge.
