You're starting a new data science project. How can you ensure you have the right tools?

Some ways to get started: 1) Literature review: A thorough literature review will help you provide better insights into current research topics and its methodologies. It helps you in identifying research gaps in the existing knowledge. This way you can get ideas or solidify your raw ideas, and find several paths to get started with the project. 2) Reproduce and Implement: Now why not reproduce some of the studies that you've liked?? This way you can learn the skills required more effectively. Once you're done with reproducing start your own project. Ask yourself what different you can contribute and how significant you think it can be. If you ever get stuck somewhere, ask the developers directly, most often they're happy to help you out.

Systems engineering distinguishes between requirements (what) and solution design (how). Avoid talking about technologies (solutions) when thinking about requirements. Many data science projects crashed due to the lack of requirements. Make your requirements as clear as possible.

Asegúrate de realizar las preguntas correctas: 1. ?Cómo es el proceso actualmente y con el modelo cómo se espera que sea? 2. ?Qué dolor piensas solucionar con esta solución analítica? 3. ?Cuál es tu población objetivo? 4. ?Qué datos dispones?, ?tiene suficiente historia? ?Cumple con los parámetros de calidad de datos? 5. ?Cómo piensas accionar a partir de los resultados? 6. ?En qué plataforma se piensa usar??Dónde se desea implementar? 7. ?Qué KPI de negocio piensas impactar con esta solución? ?En cuánto? 8. ?Tienes en el equipo a alguien con los skills para construir estas soluciones? o lo piensas tercerizar.

Choosing Tools for Data Science Projects -Objectives: Define your project goals. -Data Assessment: Understand size, type, and complexity. -Tool Research: Opt for tools suited to your data and goals. Python and R are versatile; SQL manages databases; TensorFlow excels in machine learning. -Integration: Ensure tool compatibility with your system. -Community and Support: Look for active user communities. -Scalability: Tools should accommodate project growth. -Experiment: Test different tools to find the best match. Success hinges on aligning tools with project objectives, data needs, and scalability. Experimentation is key to finding the perfect fit.

Everything should come down to “what is the business problem that I am solving and what is the true minimum viable product to solve it?” By using this approach, you can decipher between what is needed and what is nice to have, allowing you to iterate more in the future.

Some ways to get started: 1. Understand the problem statement 2. Identify the data sources 3. Create a data dictionary consisting of all the data points required and the sources they are coming from. 4. Ask your peers if they have extracted the data from the same source before. This could help in reutilizing the already created data pipelines. 5. If there isn't any existing data pipelines then check any tool that performs ETL within the organization and check for its connectivity with the source system. 6. Download a small set of data and perform analysis. 7. Share your results with stakeholders and post their confirmations and changes, productionize the same.

Always document and understand where your data is coming from; databases, APIs, files, etc. Make a list of all potential data sources. Examine your data. Look for missing values, duplicates, outliers, and inconsistencies. Use summary statistics, such as mean, median, and std. deviation, to identify potential issues. Do exploratory data analysis to gain insights into your data. Choose appropriate data visualization tools and libraries to create visual representations of your data. Based on your assessment, select the right data preprocessing and cleaning tools/technologies. Data exploration and understanding is an iterative process. As you progress, revisit and update your understanding of the data based on new insights and requirements.

Exploratory analysis is crucial to understand your data, what trends it shows, and how usable it is. It's good practice to document what assumptions are being made and how this can impact analysis later on, while also reflecting on whether the assumption is based on sound judgment. For instance, if you are evaluating a program from a third-party view, the input of those running the program will be invaluable. Also, understanding the underlying entity relationships can help point toward where relevant patterns lie, especially in healthcare where biomarkers often correlate with clinical outcomes (ex. blood pressure and heart failure).

For both academic and professional projects often we tend to see the data from a data science perspective while exploring it. However, looking at the data from the business or problem statement perspective will help explore data and derive insights which will answer some of the open questions related to the problem statement you have in hand. Another important point to consider is literature review. This is often overlooked in professional projects but holds critical value when done properly.

1.Define project goals and requirements. 2.Identify the tools that fullfill the requirements. 3.Evaluation of the selected tools based on the factors like features and capabilities of the tool,compatability and ease of use. 4.Based on the evaluation, select the best-fit tools for the project. 5.Test the selected tools on a small part of your project to see how they work. 6.Stay updated and switch the tools if needed.

There are various applications and specifications of Data science itself. Based on the specific use case, you may need to use different method and tools. For example if you want to classify the data, you will need to focus on clustering, if you want to predict the future data, you will need to apply various regression methods like logistic. If you want to make your model more accurate and precise, you will need to add more layers of deep learning. You may need to deploy neural network also. On the top of all these key ingredients, you can decide if you will need any existing advanced models like LLM, NLP, Computer vision etc It’s like cooking a dish. While cooking, you may need to tune the approach like you add salt to a dish.

Data exploration should always be one of the very first steps in a data science project. There are several easy tools to do a cursory analysis, excel being one of them. Based on the data and the problem statement, one can start considering possible approaches including traditional machine learning algorithms, neural nets, etc. Based on the approach, software packages and required hardware can be selected. Having said that, Python and R are two very common programming languages often used in model training and inference. Also, there are many open source packages and tools to choose from, to apply any of the selected methods.

Consider this as a grid of 4 quadrants where Y axis is type of Problem - Predictive or Descriptive and X Axis is Data type - Structured and Unstructured. There is one more element which is time if exists in your data consider it as Z axis. Once you have above info, map your scenario and perform methods based on data type and problem type. For ex - If data is structured and problem is predictive, then you could perform any supervised learning algorithm which would again may change based on your inferences and results from data exploration

One thing I learnt during my masters is that start with simpler methods. There is no need to jump to the complex and computationally heavy Deep Learning or LLMs just because they are new and exciting. Many times it's quite possible that even linear models could do well for your data. Another recommendation would be that we should focus on the art of modelling, we can get insights from the data but how to leverage those insights and find relevant features to be used in our models is very important. Again, these days many people just plugin their data directly to huge neural networks and crank up the computation to find some relevant results but there could be a much more efficient and elegant way of doing it if we think through the problem.

In choosing methods, I lean towards an iterative, experimental approach. It's like cooking; you start with the basics - your stats and ML - but don't shy away from adding a dash of the latest like LLM. This mindset will not only broadens our toolkit but also helps innovation, ensuring your solutions are both robust and the optimised.

Setting up the right environment is crucial for a smooth and efficient data science workflow. It is ideal to choose platforms or databases that suit your data type, size, and access needs. Some considerations in this regard are: -Consider IDEs like PyCharm or Jupyter for coding comfort and virtual environments for isolating project dependencies. -Use platforms like GitLab or GitHub for code sharing, Git to track changes, revert to previous versions, and collaborate effectively. -Consider frameworks like Unittest or pytest to ensure code quality and identify errors. -Using debuggers like pdb or Jupyter Notebook cell magic to fix code issues. - Use tools like Sphinx or Markdown to document your code, data, and methodology.

For starters, Jupyter notebook is recommended to write and test the python codes. If you advance and start a large project, you may need VCS, visual dashboards, dependency and library and package handler. There are IDEs these days which incorporate all of them. On top of all this, they can assist you write a code by features like auto completion and co-pilot. Most widely used languages for data science are Python and R. So based on your preference and specialisation, you can select a perfect IDE. To name some of then, you can use PyCharm, Spyder, VS Code, JupyterLab etc If you need to run extensive models, you may need to scale your hardware or look cloud options to run your code on heavy machines for examples Google cloud for tensorflow.

One valuable lesson I've learned involves leveraging environments, especially for large, long-term projects. Establishing a dedicated environment for your dependencies is crucial. In my experience, projects without designated environments often encounter exceptions and require adjustments over time due to changes in underlying techniques and functions from various dependencies. Creating a stable environment ensures smoother maintenance in the long run. ?????

Make sure that your environment is compliant with the laws and regulations in your industry and targeted geography. Collaborate with the legal team to ensure proper data handling (especially when it comes to PII). Verify that the data is located in an appropriate region. Work with your security team to define protocols for OSS frameworks onboarding.

Choose a development environment that aligns with your project and skills. Popular options include Jupyter Notebooks for rapid exploration and interactive coding, or PyCharm and RStudio for larger-scale projects. Consider factors like collaboration needs, project complexity, and personal preferences when making your choice.

In my opinion, the most important point is to connect deeply with all stakeholders and partners and understand the use case, the business requirement, capability constraints. This will help you define how you should structure your problem, align the right metrics and optimize performance on them as well as decide how complex your algorithms should or should not be. For instance, you may have a platform constraint basis which you can only implement a logistic regression based solution, so there is no use building a complex tree based algorithm.

Certainly! When starting a data science project, Python is a must-have tool. Use pandas for data manipulation, Scikit-learn for machine learning, and Jupyter Notebooks for collaboration. Leverage GitHub for version control, and consider Tableau for visualizations. Remember, collaboration drives success! connecting with Domain experts and performing client interactions can build up in the fresh outcomes of new trial ways of solutions . Focusing on Constant learning mindset , and feedback from senior Colleagues can help to build confidence around the real time data . and Models architectures . Happy learnings...!

In my data science journey, I've found that effective collaboration is not mere tool selection; it's about creating an ecosystem where ideas prosper. Utilising platforms for version control and collaboration. But the magic happens when we embed a practice of open dialogue and continuous feedback into our workflow. This approach simplifies the world of data for everyone involved, ensuring that our collective effort translates into actionable insights that is understood across the board (from coders to stakeholders).

When collaborating with others, a good thing to keep in mind is what level of detail your audience needs. Collaborating with fellow engineers requires very different detail and focus than collaborating with business stakeholders. Your tools and methods should reflect the points you need to get across to your collaborators.

The best way to validate your intermediate results with the team and your stakeholders is to present them in short cycles in an agile manner. By doing so, you foster open communication, encourage feedback, and enable quick adjustments to the project's direction, ultimately enhancing collaboration and increasing the likelihood of achieving the desired outcomes.

Continuous development is vital in the data science world, as technologies and methods move quickly. A few ways to stay on top new developments: - Reddit communities - Y Combinator Hacker News - Medium & Sub-stack blogs - Linkedin authors & voices It's worth striking a balance however, just because a technology is new and shiny, doesn't mean you need to know everything about it. Learn how to follow and learn what excites you, while quickly filtering other technologies down.

Continual learning and improvement are key in data science. Always be open to new tools and methods that can enhance your project. For instance, if a new machine learning algorithm becomes popular, consider learning it and evaluating its performance on your project. Seek feedback, evaluate your results, and iterate your project to optimize performance. Tools like TensorBoard can be used for monitoring and logging. Remember, the best data science project is one that evolves with the field and incorporates learning and improvement at every step.

Continuously learn about new tools, techniques and best practices through books, blogs, courses etc. Be ready to tweak your toolkit if better solutions emerge. Don't get stuck using outdated technology.

I find it valuable to self-reflect on tools even within projects. When a particular avenue, such as a chosen model architecture, does not prove fruitful (sufficiently general, explainable, &c.), it is important to not get too attached. Looking for different approaches leads to learning new tools, broadening one’s reach as a successful practitioner.

In the ever-evolving realm of data science, continuous learning is key. Embrace new tools, methods, and technologies to elevate your projects. Seek feedback, iterate for optimization, and track progress. Monitoring, logging, and evaluation tools play a crucial role in your journey. Stay curious, adapt, and thrive in the dynamic landscape of data science

Ensuring you have the right tools for a new Data Science project is crucial for its success. Here's how you can ensure you're equipped with the appropriate tools: Define Project Requirements, Identify Required Skills, Research Tools and Technologies, Evaluate Tool Suitability, Consider Data Management, Select Analytical Tools, Assess Deployment Options, Evaluate Cost and Licensing, Seek Feedback and Recommendations and Stay Updated. By following these steps, you can ensure you have the right tools to tackle your Data Science project efficiently and effectively.

Consider ease of use, scalability, speed, visualization capabilities, community support etc for long term success. Evaluate both technical capabilities and functional benefits when selecting tools.

When starting a new data science project, ensuring you have the right tools is crucial for success. Look for popular data science tools and technologies that are relevant to your project. Consider tools for data acquisition, cleaning, exploration, modeling, and visualization. Open-source tools for example are often widely used, have robust communities, and are regularly updated. They can provide a cost-effective and reliable solution for many data science tasks. Explore popular options like Python (with libraries like Pandas, NumPy, and scikit-learn) and R (with packages like tidyverse and caret), among others.

I would say widen your perspective and thought process. Apply design thinking principles and always think that your data science solution should see the light of commercializing and have the vision of making a product out of it for wider applications. If you approach with this mindset when you start a new data science project, for sure you will see success.

One thing that prove business value of models is to a/b test it in production with real users/customer. Using business metrics that matters enable to prove that the value created to the business was caused by the model.