How do you manage your data science learning progress?

Start simple. Here a few very practical hints to avoid getting overwhelmed. 1. Set intermediate milestones. Of course the final goal is to have a machine learning model working with 99% accuracy. In the meantime though, you need to build the data pipeline, control the data quality, train the first basic model. These milestones will prevent being demotivated and will give you some intermediate results to present. 2. Start humble and small.I know that complex models like deep learning network will likely solve your problem. However, it is also possible that a simpler model will work as well. So, start from the simplest model. There is always time to use and train more complex models.

As the Manager of Data Science at Priceline, managing my learning progress is a meticulous and ongoing process. I establish clear, measurable objectives aligned with emerging industry trends and organizational goals. Regularly engaging with reputable data science publications, forums, and online platforms keeps my knowledge up-to-date. Participating in webinars, workshops, and conferences allows for continuous learning and networking. Furthermore, practical application and collaboration in our team projects refine and enhance my skills. This blended approach ensures a continual learning curve, allowing me to steer our team effectively towards innovative, impactful data science solutions at Priceline.

SMART goal setting plays pivotal role. Each project demand clarity: Specific: Goals like "enhance customer segmentation" were refined to "improve segmentation accuracy by 15%." Measurable: Metrics like accuracy, precision, and AUC tracked progress tangibly, ensuring goals were met. Achievable: Understanding available data, resources, and team capabilities ensured realistic goals. Relevant: Aligning goals with larger objectives, like boosting sales through accurate predictions, fueled purpose. Time-bound: Setting deadlines – say, completing a sentiment analysis model in 2 months – infused urgency. Implementing SMART goals fostered focused, attainable, and timely outcomes, enhancing project success.

Focus on solving real-world problems daily. There are a lot of real-world data science problems that are trying to be solved by thousands of researchers and engineers worldwide. Select a problem of your interest and start your research in the problem statement. Suppose, one of the real-world problems that many researchers are trying to solve is "How to generate the best efficient SQL queries from Large Language Models given a natural language query". Then you start to research the problem by doing initial Google searches. Read some research papers related to the problem. Try open-source implementations to solve the problem. After knowing about the problem, another crucial step is data. Learn every day by solving new problems.

Just as in any other domain, setting Specific, Measurable, Achievable, Relevant, and Time-bound (SMART) goals is essential. Define precisely what you want to achieve in your data science journey. For instance, a SMART goal could be to "Complete a deep learning specialization on Coursera within six months with a final project demonstrating image classification accuracy above 90%." SMART goals provide clear direction and motivation for your learning path.

Research: Look for reputable sources. e.g., "Select courses from established platforms like Coursera or edX." Recommendations: Seek advice from experienced individuals. e.g., "Ask data scientists in your network about their preferred learning materials." Reviews: Check feedback and ratings. e.g., "Ensure a book or course has positive reviews before diving in." Relevance: Ensure the content aligns with current industry standards. e.g., "Opt for resources that teach the latest data science tools and techniques." Selecting quality resources, like well-reviewed courses or recommended books, ensures a robust and updated foundation in data science.

Quality but also fun resources is the most important tip. Quality, since it's hard to follow more than a few resources, so the ones we pick should cover exactly what we need. And fun, since otherwise it'll be hard to make it a habit. Personally, I'm following a Facebook group called MDLI - Machine and Deep Learning Israel. It's of very high quality since it's the largest group of this sort in my country, I've already gained useful insights from it for my models, researchers post their new papers there, and the moderaters are rather strict (I know from personal experience ??). And It's also fun, or more precisely, a much better use of the large time I'm already spending on Facebook.

When working on sensitive project, selecting data science resources was vital. I verified authors' expertise, checking credentials, and reputation. Reviews gauged others' experiences, aiding my choices. Content relevance, format alignment with my learning style, and a balanced challenge level were key. Balancing resource duration with project timelines was crucial. Lastly, considering costs against benefits ensured wise investments. This approach led me to invaluable, credible resources, advancing my expertise efficiently.

he quality of your learning resources significantly impacts your progress. Select reputable platforms, courses, books, and online communities that align with your goals. Platforms like DataCamp(personal favorite), Coursera, edX, and Udacity offer a wide array of data science courses, while books like "Introduction to Statistical Learning" and "Python for Data Analysis" provide in-depth knowledge. Engaging with the data science community through platforms like Kaggle and Stack Overflow can also be invaluable for learning and networking.

This can't be said enough. Every alternate click on Youtube redirects to a so called Data Science Expert. Resources that stem from Top Tier Universities are often the best - case in point the Harvard CS Course which is the most well regarded starting point for anyone new to coding and data science.

Got this from InternetMadeCoder channel. I keep it simple. I just put in the hours. Assuming I've got a whole day, my schedule looks like this: ? 0500-0700: Wake up. Quick workout. Quick breakfast. Get to a café. ? 0700-0900: Deep Work Block #1. No distractions. Shut off my phone. Work on a project. Work on a course. Doesn't matter. Just git gud. ? 0900-1000: Break #1. Do whatever I want. Go for a walk. Doom scroll social media. Just relax. ? 1000-1200: Deep Work Block #2 ? 1200-1300: Break #2. ? 1300-1500: Deep Work Block #3. ? Victory. I can do whatever I want for the rest of the day, knowing that I put in six solid focused hours towards mastering my craft. I'm not ALWAYS this disciplined, but this simple framework is working for me.

?? Planning your schedule is pivotal for successful data science learning. Tailor it to your availability, goals, and learning style: Consistency: Decide on daily, weekly, or monthly learning sessions based on your routine. Variation: Mix session lengths and intensities for balance and retention. Tools: Utilize a calendar or planner app to schedule, set reminders, and monitor progress. Tracking: Keep tabs on your accomplishments to stay motivated. By customizing your schedule, you'll maximize your learning potential and ensure steady progress on your data science journey. ??????

If you find it difficult to focus on certain occasions, consider using the Pomodoro technique. This technique involves breaking your work into focused intervals, typically 25 minutes long, followed by short breaks. After completing four of these intervals, you can take a longer break of around 15-30 minutes to recharge and maintain productivity. This technique works like magic on those days when you just don't feel motivated to learn!

Effective time management is paramount in managing your data science learning progress. Develop a study schedule that accounts for your daily responsibilities and commitments. Allocate dedicated time slots for learning, and stick to them consistently. Consider leveraging time management tools or techniques like the Pomodoro technique to enhance your focus and productivity.

It's a marathon and not a sprint. Dedicating a short amount of time every day or every other day has better long term impact on your data science journey. Plan to watch videos and be hands on coding, visualizing, processing, etc. at least 1 hour every day

Taking data science from theory to action demands active application. Real-world value is born through hands-on problem-solving. Seek projects individually or collaboratively, a cornerstone for skill refinement. Platforms like Kaggle, GitHub, and DataCamp Projects offer diverse challenges to tackle. Personal projects fueled by curiosity or industry-related puzzles can also be transformative. Application hones your abilities, revealing gaps needing attention. It's a litmus test for comprehension, yielding insights into practical nuances. Additionally, it's a canvas to showcase proficiency via a portfolio. My journey in data science emphasized that only through practice does theory evolve into mastery, yielding confidence and expertise.

Teaching is the first way of applying your skills directly while planning how they are to be communicated. The second way is helping and collaborating the projects and problems of other people. This way you can improve your skill during the interactions with other experts. Third way is working with your own pet project which may have or have no financial intention. This will keep bringing your energy to learn and apply the knowledge in long term due to the self motivation.

Effective progress in data science learning encompasses various strategies. It begins with a clear understanding of your journey in the data field. Recognize areas where expertise or proficiency is essential, like programming languages and the application of AI and ML in specific scenarios. Each aspect demands dedicated learning and adaptation. It's crucial to remember that persistence is key in this field, which translates to continuous learning and regular updating of your skills. Embrace this journey with a focus on growth and adaptability in the ever-evolving world of data science

Data Science is a practical skill. It is multi-disciplinary field that requires application of theory while understanding the needs of business and the problem statement. 1. Practice on as many real world problems/datasets as you can. 2. Then take the next steps forward, creating a clean dataset and processing it for modelling is more than half of the work of a data scientist. 3. One also needs to build skills on presenting/visualizing the information coming out of your models. You will be required to convert the data science output into terms that a business stakeholder can understand and utilize.

Learn by doing projects. The workflow is very simple. When I started learning Data Science, I was very speculative whether I'm in the right direction or not. I followed these 4 steps to do a project. 1. Searched for an interesting dataset from Kaggle 2. Came up with 10 questions that I want an answer for from the data 3. Tried to answer those 10 questions by analyzing the data I took 4. Finally, and most critical part is I wrote a blog about the insights that I got, so that others can understand it easily. This helped me to enhance my skills, as well as contribute to the community

Write blogs, articles and post your work on kaggle communities. Seek feedback on your approach and use others' perspective to identify what you missed. Learning from others can accelerate your learning rate and make you a master of Data Science

In my previous experiences, seeking feedback played a pivotal role. Collaborating within interdisciplinary teams and interacting with clients demanded constant learning. Active participation in industry-specific forums, like Stack Overflow and industry conferences, such as PyData, broadened perspectives. Engaging with clients fostered valuable insights into their domain expertise and unique challenges. Feedback loops guided project refinement, ensuring alignment with client expectations. Critiques from colleagues and stakeholders, coupled with my experience, catalyzed innovative solutions. The feedback-driven iterative process was essential for delivering impactful, tailored solutions while expanding my own expertise.

Constructive feedback is a catalyst for growth. Engage with peers, mentors, or online communities to seek feedback on your projects and solutions. Participate in code reviews and discussions to gain insights into different approaches and best practices. Don't hesitate to ask questions when you encounter challenges, as the data science community is generally supportive and eager to help.

Feedback is extremely important in a data science work. Despite out best efforts, if there are certain wrong assumptions made during the early stages of any work, it can easily trickle down and lead to uncertain conclusions towards the end. Public resources like StackOverflow or Reddit can help regarding specific challenges, but not the holistic feedback for a project work. Reaching out to data science professionals on LinkedIn who might be willing to help or interacting with attendees at conferences like PyData could be useful.

In my data science journey, I quickly realized that growth thrives on feedback. The fifth step? Seeking feedback from the vibrant data science community. ???? ?? The true power of seeking feedback lay in its ability to illuminate my path. With each exchange, I gained clarity on my strengths and identified areas that needed improvement. It was like having a compass that guided me through the data science landscape. ???? ?? And let's not forget the incredible sense of community. Data science isn't just a field; it's a network of like-minded individuals eager to help each other succeed. ????

You achieve success when you do things consistently. Cause lot many stop in between. The same goes here. There is no era like today, where consistently learning and keeping up with the trends is as important as your career. In this age and its important to stay relevant and hence focusing new developments and learning nuances will keep you ahead.

Learning is a continuous journey for any professional today, and even more so for a data scientist. A data scientist needs to be learning about multiple technical aspects: new data prep tools, data synthesizers, new modeling techniques, productization best practices, tools for post-deployment maintenance etc. Then there are non-technical skills to learn which are also equally important if not more to make a data scientists successful: reading between the lines to tease out the precise unmet need for the user, understanding the customer's culture / people context and devising a solution that fits in that context, the art of storytelling to convey in a non-tech language how your solution will benefit, educating and setting expectations etc.

In Data Science, of course keeping learning is essential. That's why collaboration between industry and research institution is so important and should be more developped. Research institutions will help you to feed your models with solid theoretical backgrounds, and avoid fashionable and trendy methods (so much noise these days). Application in Industry will consolidate and demonstrate the business value of the Data science models.

Data science is an evolving field, so staying current is essential. Regularly read research papers, blogs, and articles to keep up with the latest advancements and trends. Follow thought leaders and experts in the field on social media and consider attending conferences or webinars to broaden your knowledge. Continuous learning and adaptability are key to remaining competitive in the data science landscape.

The journey does not stop when you are hired as a data analyst; the field is growing really fast and you need to stay on top of your game by constantly learning and really enjoying the process of expanding your knowledge. Thankfully, most companies appreciate that and reward you for it!

In my data science journey, I've learned that success extends beyond algorithms. Understanding domain context enriches solutions; failures foster growth. Projects and mentors prove pivotal. Embrace self-driven projects for practical learning. Engaging in the data science community fuels inspiration. Data science isn't just coding; it's weaving insights from diverse threads.

Like in many other technical careers, at some point data scientist would have to go beyond just working with data and start communicating. These are communications with team members, broader circle of colleagues, and, most importantly, clients. The toughest part of our work is to understand the business problem. Once that's done, finding technical solutions is rather stratightforward.

?? In the fast-paced world of data science, remember: your journey is a marathon, not a sprint. Avoiding burnout isn't just about self-care; it's a strategic necessity. Clear minds fuel innovation, accurate analysis, and lasting success. Prioritize your well-being to excel in the long run. ?? Tip: Schedule regular breaks and set clear boundaries for work hours. Allocate time for hobbies, exercise, and quality time with loved ones. Balance is the key to sustainable excellence. ??

Learning the algorithms is obviously of high importance, but my data science learning journey has also revealed the power of being able to apply your models and demonstrate their value. Whether it be public or private sector, models must produce results. Being able to express the value of what your algorithm can provide or what problem it can solve is arguably as important as the algorithm itself when it comes to putting your learning to use. Showing consideration of these applications can help develop a more robust data science portfolio and help demonstrate forward thinking about how you will use what you learned to accomplish a tangible goal and bring quantifiable value.

In the learning process you need to understand data science concepts, expectations, task approach,what to look for in the data, how to study data, how to re-structure it for it to communicate,the underlining factors that may affect the outcome i.e missing data and how to tackle them effectively to extract valuable non bias information. Self assessment like, what I am supposed to do, how I am supposed to do it, where does it lead me, does it stay within my scope, what is affecting it not to or to, what techniques do I need to deploy, how to deploy them, what are the best practices i.e using mode in visualization may prove valuable in terms of data distribution than average, how do I fair on this techniques, how do I polish my techniques.