Despite the fact that every one of us is living a different life, there are a few common experiences that tie most of us together. A depressing yet true example would be the sequence of occasions that characterise a few decades of our lives: school -> college -> work. The majority of these continue to be the same, but there has been a growing drive to rethink education systems as we currently have them, in order to create one that protects students from the negative effects of new technologies in the workplace while also reskilling them to prepare for new cross-functional roles.
The data science community encourages both conventional and informal education, each with its own advantages and disadvantages. A self-study plan is not a foreign concept though, given that there is no “correct method” to study for or pursue a profession in data science. So let’s delve into a comprehensive and curated list of online resources for data science self-study that can help you loads.
- MASSIVE OPEN ONLINE COURSES (MOOCS)
Massive open online courses are proliferating as a result of the growing need for data science professionals (MOOCs). While some MOOCs are completely free, some do require you to pay a subscription cost, which varies between platforms and can be anywhere from $50 to $200 per course or more. Remember that it takes a significant amount of time and effort to become an expert in any field. Make sure you are prepared to finish the entire course, including all assignments and homework challenges if you decide to enrol in a course. There will be some difficult tests and homework tasks. However, you won’t be able to advance your knowledge and abilities if you don’t push yourself.
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Compared to online courses, learning from a textbook offers a more specialized and in-depth understanding. Books can also help you develop a comprehensive understanding of data science as it encompasses a wide range of subjects in addition to computing, such as mathematics, probability, statistics, programming, machine learning, and much more. There are many top-notch data science textbooks available in the market that can help you in your data science self-study. Wes McKinney’s “Python for Data Analysis,” Kuhn & Johnson’s “Applied Predictive Modeling,” and Ian H. Witten, Eibe Frank, and Mark A. Hall’s “Data Mining: Practical Machine Learning Tools and Techniques” are a few that can come in handy.
Kaggle is a crowd-sourced platform that draws in, develops, educates, and challenges data scientists from across the world to tackle problems in data science, machine learning, and predictive analytics. Kaggle offers a forum for data science enthusiasts to participate in solving real-world challenges. Your knowledge about how to identify new big data solutions will be greatly aided by the experience you gain on Kaggle. This data science community offers you excellent tools and resources to assist you in achieving your data science ambitions. Users can discover and share datasets, explore and develop models in a web-based data science environment, collaborate with other data scientists and machine learning experts, and participate in competitions to address data science challenges on this platform. Through this site, you can get access to a wide range of datasets, classes, notes, and competitions.
KDnuggets is one of the leading websites on AI, Analytics, Big Data, Data Mining, Data Science, and Machine Learning. On this website, you can find crucial data science instructional resources, tools, and resources for professional growth such as blogs, courses, datasets etc.
On GitHub, you can find numerous projects and lessons related to data science and machine learning. In addition to being a great tool for data science education, GitHub is a great platform for managing projects and developing a portfolio. In a cooperative and collaborative environment, GitHub provides a hub for more than 73 million programmers and developers to host and share their programs. It is the most well-known source code server worldwide with over 28 million public repositories, and it offers numerous features including access control, version control, and continuous integration for every project.
Whether you want to start a career in data science, switch to the field, or just apply these cutting-edge, highly applicable skills to another area of expertise, a mix of non-traditional education with the appropriate experience and skills can help you get far. Data science is an applied discipline. Get software to start learning your first programming language. Review the mathematics that underlies data science. Play around with data visualization with free software. You will discover how to become a data scientist more effortlessly and within no time you are one step closer to your data scientist dreams. However, there is no harm in getting help. Confused about where to look for help? Praxis has got you covered. Praxis has developed a versatile PGP in Data Science that will provide you with an unmatched and adequate platform to build the skills and proficiencies you need to jumpstart your career.
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