How can you learn data mining without prior experience?

To learn data mining without prior experience: - Begin by familiarising yourself with the fundamental concepts of data mining. Learn about key terms, processes, and the role of data mining in extracting insights from large datasets. - Use online resources, books, or introductory courses that are designed for beginners. - Data mining involves statistical techniques. Strengthen your foundation by learning basic statistics.

Start with CRISP-DM (Cross-Industry Standard Process for Data Mining) framework - 1. Understand the Business Objective 2. Understand & Explore the Data available 3. Data preparation - (cleaning, preprocessing) 4. Modeling - (identifying trends) 5. Evaluation - (testing on new data) 6. Deployment - (make predictions in real-time) The methodology also allows the flexibility of being a Waterfall approach or an Agile approach as processes can iterate quickly, and layer in other processes.

To learn data mining without prior experience, start by understanding the basics. Familiarize yourself with the data mining process, types of data, common tasks, and methods. Online courses like "Data Mining: Concepts and Techniques" by Han, Kamber, and Pei, or the "Data Mining Specialization" on Coursera, offer valuable insights. Build a foundation before exploring advanced topics and hands-on projects, gradually gaining practical experience through real-world datasets and challenges. Engage with the community, seek mentorship, and continuously apply and refine your skills to develop proficiency in data mining.

First, start learning data mining by selecting a tool or language tailored to your preferences and goals. Some of them like Excel, SQL, Python, R, SAS, or SPSS provide versatility for manipulating, analyzing, and visualizing data. Begin with basic commands and functions, gradually delving into advanced features and libraries. Leverage online platforms like DataCamp, Kaggle, or Codecademy, offering interactive exercises and projects to gain practical experience and enhance your skills.

Hands-on learning! it's always important when it comes to working with data. Grab real data (think health records, sports stats, even your Instagram!) and unleash your newfound skills. Open-source sets abound online or use your own. Apply techniques you learned, dig for insights, and share discoveries with others. Practice makes perfect (and insightful) data miners!

Data mining is a dynamic field that constantly introduces new challenges and opportunities. Stay updated on new algorithms, applications, and platforms by following blogs, podcasts, newsletters, or journals like "Analytics Vidhya," "Data Science Weekly," or "Journal of Data Mining Research." Engage with online communities, forums, or groups such as Reddit, Stack Overflow, or Quora, where data mining topics are discussed. Keeping abreast of the evolving landscape ensures you stay well-informed and equipped to navigate the dynamic world of data mining.

It's crucial to prepare for interviews as part of pursuing a career in the field. Familiarize yourself with common data mining questions and scenarios that employers often present, covering topics like problem definition, method selection, model evaluation, and solution presentation. Practice coding and communication skills, emphasizing creativity and critical thinking. Utilize online resources such as Interview Query, Data Interview Questions, or Glassdoor, which provide a wealth of data mining interview questions and answers. This preparation enhances your readiness for real-world applications in data mining.