What are the best ways to learn data mining skills without returning to school?

The best way to start is through online courses like the ones Coursera, Udemy, LinkedIn, and Rapidminer Academy provide. I highly recommend reading "Pattern Recognition and Machine Learning" by Bishop and "Machine Learning" by Mitchell. Mentors play a crucial role in honing skills, yet finding the right ones is essential.

Get skilled for any courses is very best approach to enrolled online platforms such as Coursera, Udemy, Linkedin and many more. Just need to identify your goal and requirements of your role in your job, research before get enrolled on any platform and get view by asking in your network for authentication, end of the learning is your practice and use in real time will boost your skills.

In the dynamic world of technology, one of the swiftest routes to knowledge enrichment lies in the realm of boot camps, Udemy, & YouTube. In my experience, have led me to discover these resources for continuous learning. Additionally, major players like Microsoft & SAP offer Partner learnings. As individuals grasp & appreciate the intricacies of cutting-edge tools, they become influential advocates. This influence can significantly impact leadership , potentially steering them towards strategic investments. While these often come with a cost, forward-thinking firms recognize the value proposition. They may extend free memberships to their IT teams, fostering an environment where continuous learning is not just encouraged but supported.

Enrolling on online courses on platforms like Udemy, Datastax, Coursera, etc can help in self-learning of these technologies. You can attend some live streaming related to data mining which might help. Traditional methods like books can also be a good option to learn. Keep doing hands on live projects from sites kaggle, projectspro, etc.

There are several excellent ways to learn data mining skills without returning to school, and online courses are just one option. . Platforms: A variety of platforms offer data mining courses, including Coursera, edX, Udemy, Udacity and DataCamp. Choose courses that align with your experience level and learning style. Look for courses with projects and practical exercises to solidify your understanding. Specializations: Consider enrolling in a data mining specialization, which typically consists of several interconnected courses covering key data mining concepts and tools. . Free options: Start with free introductory courses on platforms like Khan Academy, YouTube channels like StatQuest or MOOCs from universities.

Books contribution are always great in any learning, weather learn online or offline, from reading books you will get details and many important concepts which explained with examples and case studies, Also can learn techniques and concepts of Data Mining and get every details what you need to really learn for this skill, Blogs can give you experience from an individuals for their articles and case studies, it can add more value in your learning.

There are numerous resources available that cover a wide range of topics in data mining, machine learning, and data analysis. Books authored by experts in the field provide in-depth knowledge and insights into various data mining techniques, algorithms, and applications. Additionally, blogs written by data scientists and researchers like Data science central, SmartData collective, insideBIGDATA, Datafloq and even medium offer practical tips, tutorials, and case studies that can help you enhance your data mining skills. By reading books and following reputable blogs, you can gain valuable insights and stay updated on the latest developments and trends in data mining.

Finding core books and blogs or commentaries would be one of the first steps to learn anything including data mining or data skills. Personally if there is a book in my area in for dummies series, I definitely would consider having it.

If you want to enhance your journey in data mining, building projects are the best way. As it gives you hands on experience as well as builds your resume. Start doing mini projects and move to big and collaborative projects.

Project will explore your learning, and give you real time experience in data mining, by doing projects you will learn practically what you learned theoretically, and will know how to apply all tools and concepts for data mining. Must go through a projects as its next level of learning data mining.

Remember, the key is to be consistent, be curious, and don't be afraid to experiment and make mistakes. Here are some strategies to get you started: Finding the right projects and competitions. Choose your level: Are you a beginner, intermediate, or advanced learner? Platforms like Kaggle offer difficulties ranging from introductory to expert. Explore variety: Try different types of projects, like text mining, web scraping, or social media analysis, to gain diverse experience. Platforms and resources. Kaggle: The go-to platform for data science competitions with datasets, forums, and kernels (code notebooks) you can learn from and adapt. UCI Machine Learning Repository: Open-source datasets for experimentation and practice.

Always best way to Learn something is using it. In my experience as a teacher and speaker of data science, best way to include students is to bring a subject that would be practical in their everyday lives.

To execute data mining you must have skilled to operating to tools which exactly used for data mining, their are many tools which helps, great way to learn it go with market demands or your clients requirements, Mostly in demands are M.S. Excel, Power BI, SQL, Tableau. But still most important point is rather than getting all be a master of any one or two, and rest should add as per need.

First step for a cook is to know his or her tools. Knife and pan of a data scientist is sql, power BI, tableau, excel and others. You wouldn’t consider yourself a data scientist if you would not know majority of these tools well enough

Communities and networks are way that we could learn any thing, and its really play important role in our social life, hence for learning data mining skill one should join its related communities and networks where you can get updated with latest and also can ask and share your points easly. use your network to grow yourself and help to grow other as well. Communities such as Reddit, Quora, Meetup, and Slack are best platform for Data mining professionals.

Pleasure of each atmosphere is being among people with common interests and common issues. Knowing the core communities of your field has a lot of benefits, least of them you can learn from the questions your colleagues would ask and you would learn just by being amongst friends and experts.

Mentors and coaches can be invaluable assets in your data mining journey, offering personalized guidance, feedback, and support. . Identify your needs: Be clear about your goals, what you want to learn, and your preferred learning style. Do you need help with specific techniques, career advice, project guidance, or accountability? . Platforms: Explore platforms like MentorCruise, Superpeer, Clarity, Codementor, or reach out to professionals on LinkedIn with relevant experience. Consider university alumni networks or professional data mining organizations. . Interview potential mentors: Ask about their experience, expertise, and mentorship style to ensure a good fit.

Understanding the business context and objectives is crucial for data mining professionals. Being able to align data mining initiatives with business goals, communicate the impact of insights, and identify growth opportunities are important skills for success in data mining. Strong business acumen can help translate insights into action and drive business outcomes.

I start my career by read through the whole SPSS documentation. It still the best learning material I have ever read. SAS technical papers are also very helpful when it comes to individual projects.