What do you do if your data mining internship is filled with obstacles?

Asking for help is a good quality that every employer seeks as it shows that an employee is serious about its job and wants to learn. During my internship I contacted a few of my colleagues that have done the same or some similar tasks. That help really saved my time in analysis. But don't always start searching for help whenever you are stuck with a problem try to solve it by yourself and if you are not getting any solution then seek for guidance or help.

My opinion over here will be always give your 100% before seeking guidance from your senior colleague. I want to suggest you to try to think over the problem for sometime but dont waste too much time doing that. This strategy will help you to atleast understand your problem in detail and might also help you get the solution or atleast a partial solution or even you might get atleast a flow for your solution.

Pasos: 👉🔍Identifico obstáculos: determino si son técnicos, interpersonales o estructurales 👉📚Busco recursos: tutoriales,libros y foros especializados 👉💬comunico: hablo con mi supervisor para obtener orientación y posibles ajustes en mis tareas 👉🎯metas claras: establezco y ajusto mis objetivos de aprendizaje con mi supervisor 👉📈Aprendo de errores: uso cada desafío como oportunidad para aprender 👉🔄Solicito feedback: pido retroalimentación regular para mejorar mis habilidades 👉😊Mantengo una actitud positiva:conservo una actitud abierta y proactiva ante los desafíos 👉🤝Cultivo redes de contactos:desarrollo relaciones que puedan ofrecer apoyo y consejos 👉🧘mi bienestar:aseguro de equilibrar el trabajo y el descanso.

So many aspects to explore: 1. What’s the job description? 2. Where is the internship? 3. What stage of the intern? 4. What is intern’s quantitative background? 5. What’s the purpose of data mining? 6. Who are the end users and collaborators? 7. What type of data? 8. How were data generated? 9. Which software package is used? 10. Do models converge?

If your data mining internship is filled with obstacles, there are several strategies you can employ to overcome them which are listed below: 1. Data Quality and Consistency. 2. Data Exploration 3. Prioritize Essential Analytics Tasks 4. Talent Acquisition and Retention 5. Choosing the Right Tools and Technologies 6. Data Wrangling 7. Data Mining Techniques 8. Data Security and Privacy 9. Process Maturity 10. Strategic Alignment 11. Data Quality Assurance 12. Generative AI Tools

Your manager or teams leader will want you to succeed. Speak with them and share your concerns. Internships are to find talent so they want you to succeed.

Absolutely, internships offer invaluable learning experiences, especially when faced with obstacles. Overcoming challenges is part of the learning journey, often requiring us to manage, adapt, or even unlearn certain approaches. Variables like frequency, skill level, and approaches may initially seem daunting, but they provide opportunities for growth. It's common to feel outmatched by experienced peers, but facing such situations head-on builds resilience and expertise over time. Embracing these challenges with a growth mindset fosters development and prepares us for future endeavors in the field.

With the assumption this relates to students, an internship is the first experience of a startup or corporate world and is very different from the safe and controlled environment of a university. Stay humble. You know some, but not everything. If you are a high-achieving student, there is still much to learn, as real-world data science differs from academic exercises. Your supervisor knows you are there to learn, so do not feel pressured to excel in everything and avoid the "Hero Syndrome" at all costs. If something is unclear or you don't know, ask! Asking is a positive soft skill that you need to develop. When you are part of a team, there is no competition, as the only objective is to help and receive help to move forward together.

Identify specific obstacles. Communicate with your supervisor. Seek additional resources. Collaborate with peers. Break tasks into manageable steps. Stay persistent and patient. Seek feedback. Take care of yourself. Learn from the experience.

Always try to find the root cause of your problem not only this will help you resolve your problems quickly but also it helps you plan the action plan to solve the problem. You can always break your solution into subtask for better solving aproach and better result.

Data mining is time-consuming, but breaking it into smaller steps helps: 1. Understand the data source format and how to access it (SQL / API querying, web scraping, etc.). 2. Define the target data structure before mining. Having a data scheme BEFORE starting is useful (probably a must-do if the project is large) 3. Develop prototypes to streamline the coding process, with clear documentation. The latter eases debugging. Proper planning and iterative execution can streamline data mining efforts.

The main goal of an internship is to learn, so make this objective the main and measurable outcome of your internship. First, sit down with your manager and write down what you would like to learn. If you don't ask, you won't get it. Even if it is impossible to fulfill all your goals, a good manager will add some extra activities (e.g., training, conferences, etc.) to meet your learning needs. Learn the business! Write "Learn the business" as your major goal. Data science is utterly useless without understanding the business. This is your #1 priority. This also allows you to network with the business side of the company. A planned internship plan is a good internship and an antidote to trouble.

View challenges as opportunities to expand your skills and knowledge rather than setbacks. Seek out diverse learning resources and actively engage with them to deepen your understanding. Remember, every obstacle you overcome is a stepping stone toward becoming a proficient data miner.

You can always learn from your mistakes and try to learn from every aproach you think to solve the problem. Continous learning and keeping update of every technology,tools you use while you work on Data Mining projects is the crucial thing to be successfull in the data jobs. Continous learning is the key in IT sector. Yoh always need to stay up to date with the new trends comming in the technology and upskilling the skill sets is also important.

Self learning is the best way if you come across obstacles. This helps you in two ways: 1. You develop your research skills and terminologies w.r.t the subject matter. Join communities on Reddit and quora. Follow groups that have people with extensive experience and knowledge in the domain. Also try to find out ways where you can mine data from the source without any tools. It allows you to standout with your skill set. 2. On self learning journey we commit mistakes and hit barriers constantly. This will help you prune down your error prone areas and streamline your thought process in approach, decision making and qualitative learning where it will become easy for you to spot errors since most of them you would already have come across.

Al analizar la información es importante descartar los valores aberrantes ( descarte de manera manual o por rangos) que me pueden causar desviaciones pronunciadas en la información consolidada dentro del dashboard. De ser posible tener varias versiones del dashboard final v.1, v.2, v.3. Con la finalidad de ver los resultados y su impacto acorde a la tendencia de lo que se quiere presentar.

Documentar el progreso con una bitacora o un registro de cambios, de ser posible si se ejecuta dentro de la misma plataforma tener el registro de información de logueo de usuarios con notas, para validar la gestión y el acompañamiento de la revisión constante sobre la mejora.

Es muy importante compartir ideas en Foros o grupos porque a veces la solución está mas cerca de lo esperado y en otros puntos hay personas que han pasado por lo mismo y la solución suele estar al alcance de la mano. Ahora esto sin descuidar la capacitación constante y la práctica de lo aprendido. Las redes sociales se puede dar un enfoque mas profesional buscando canales o chats donde haya participación constante y opiniones, recuerda que toda idea suma a la gestión.

I recommend my intern to own a personal high objective and build an unique individual story that will offer strong resillience for all outside obstacles and provide a agile mindset that will always focus on opportunities trigered by obstacles we experience in our work life.

No obstacles can be imagine..how come.? .it is not allowed to start the preprocessing unless we are having the ability to normalize the data in the first step to overcome any kind of obstacles could be eventually had happened.