How can you learn data science in your free time?

Stanford Computer Science has some fantastic youtube content on deep learning. A lot of universities use those courses as blueprints for their own programs, but it's all available for free online. If you can stick with self-driven learning, this is a great way to get into the more technical side of data science.

Here are some resources that have helped me improve my knowledge on tools/Statistics: 1. DataCamp and codeschool (now part of Pluralsight) for R. 2. Pluralsight is also good for Python. I had to learn specific topics. In such scenarios, specific learning courses in LinkedIn Learning helped. 3. SQLZoo for SQL. This allows testing queries on sample datasets, very good learning experience. 4. Business Analytics by U Dinesh Kumar - Nice book on Statistics needed for Analytics. There are good resources for visualization tools like PowerBI and Tableau. Since I learnt from multiple sources, not listing anything specific. Optional: End-to-end ML platforms like Dataiku - Dataiku Academy is good.

To learn data science in your free time, consider these steps: Online Courses: Join courses on Coursera, edX, or Udemy for structured learning. Reading: Dive into books and e-books on data science basics, statistics, and programming (Python or R). Hands-On Projects: Practice skills with real-world projects using datasets from platforms like Kaggle or GitHub. Community Engagement: Participate in data science forums and communities for peer support and industry updates. Podcasts and Blogs: Follow relevant podcasts and blogs for insights and trends. Tutorials/Webinars: Attend free online tutorials and webinars by experts. Balance theoretical learning with practical application for effective skill acquisition.

Defining the future prospects of your data science career, Creating a passion for learning and then developing love for the what you do and why you do it. They exist a numerous online platforms with rich resources you can leverage on. 1. Youtube contents 2. Coursera 3. Udemy 4. Towards data science 5. ML masterly Etc Complementing self-study, consider the invaluable support of a mentor who can guide you through challenges on your learning journey. Finally, actively engage in data science communities, where you can network, share experiences, and stay abreast of industry developments. This holistic approach ensures a structured and rewarding progression in the ever-evolving field of data science.

Always think what do you want to do in your life and whatever you are currently doing is it align to your life goals or not. Once you find the answer to this question things get easier. Now you can set your goal according to your current situation. Find what makes you happy and start working on it. If you are full of empathy, compassion and feels responsible towards society then start thinking like how can you help society to make this world as better place. My life goal is to help people and want to save many lives, So i use my Machine Learning knowledge to prototype products which would save many lives on the road. I have found my life goal hope you find yours soon. Love Ash.

Personally, I believe you should learn Python, especially if you intend to deploy your code. R is not great when it comes to putting your code into production. For starting out, download Anaconda as it contains most of what you will need (think Python plus Jupyter Notebook). When you get frustrated at this step, just know that it’s configuration problems, not data science problems. Do your best not to get discouraged. Definitely use Kaggle or DataCamp for an all in one environment.

I might be a little biased when it comes to tools and how to go about learning them. I would recommend picking up Python, setting up Jupyter Notebook and go through scikit-learn and Scipy docs when you have the free time. Python, because you can do much more than Data science when you get a hang of it, like Rest APIs. Scikit-learn and Scipy, because of their extensive and welcoming docs.

Some ways to get started in Data Science while keeping the interest intact, one can refer to the plethora of YouTube videos as a first step. Then you can choose a tool or skill that you want to learn and focus on that specific aspect. Python is great to get started with. After you know what’s the tool or skill you want to go ahead with, you could enrol in an online course. Also be on the lookout for @LinkedIn articles and posts by fellow Data Scientists.

In my view, the tools you learn should reflect your goals. Are you learning data science for personal interest? In that case, you have a lot of freedom to choose, although Python is a great place to start. If the aim is to build a career in data science, I would suggest getting started with 3 basic tools: Python, SQL, and a cloud platform (AWS, Google, Azure). Once you have a basis in those 3, start looking at a wider set, e.g. more languages (R being an obvious one but I've also seen DS job ads calling for Java or C++), more types of tools (e.g. dashboard tools), etc. Try to strike a balance between breadth of tools and depth of knowledge in the core tools.

I would prefer to learn Power BI. I like to visualize datasets using PowerBI in this way, you will able to understand the details of Data. Presenting Data in visualization is also an important topic in the Data Science field.

Too often, aspiring data scientists jump into the details of programming without learning the basics of statistics, probability, program evaluation, and other key concepts that underlie the machine learning methods. These concepts can be learned from online resources, textbooks or in-person instruction...different people learn different ways so try to identify what method works best for you. Make sure you try solving problems - just watching videos or reading textbooks without seeing if you can apply the information gives people a false sense of confidence.

Medium is a goldmine for data scientists! The articles strike a perfect balance — the content is approachable, making complex concepts more digestible, ensuring professionals stay updated on the ever-evolving trends in the dynamic field of data science. It is a great resource for beginners as well.

I personally found StatQuest videos (StatQuest is a YouTube channel) to be pretty good. Machine learning algorithms are explained in a very lucid manner. Even the maths related to the algorithms is also explained very well.

I recommend starting with 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville for a solid theoretical and practical foundation in data science. Their book, coupled with the Python notebook examples available on their website, offers an excellent blend of deep learning concepts and hands-on practice. However, remember that data science extends beyond these foundations. You'll also need to delve into areas like Docker for containerization, cloud computing for scalable resources, security to safeguard data, and storytelling to effectively communicate your findings. Balancing deep theoretical knowledge with these diverse, practical skills is key to becoming proficient in the multifaceted field of data science.

Since Data Science is an umbrella term- encompassing various domains like Identifying Patterns, Data Wrangling (~imputation), Statistics, Model Building, Validation, Production Readiness Monitoring -it's helpful to focus the fundamentals & way up: ??Getting a holistic gist of Data Science ??Grasping basics of statistics (quantiles, hypothesis etc) ??Learning concepts of Supervised & Unsupervised models (regression, clustering etc) ??Building Projects that focus on Data Visualisation - using PowerBi/Tableau etc & Machine Learning - using Python/Polars/R ??Writing blogs to keep a track of the lessons throughout the journey - serves a great reminder of your progress as well as a way to track things we'd otherwise miss out on after a while

Pick a real life project. Something which touches your day to day life. Could be a simple dashboard analyzing your bank statement or number of steps data or screen time etc. This kind of passion projects would help you figure out data collection, apis, connecting dots, building and deploying etc.

To excel in data science, it's crucial to apply your skills to real-world problems and datasets while also taking advantage of online courses, joining data science communities, building a strong project portfolio, staying updated with the latest industry trends, seeking feedback, and collaborating with peers. This multifaceted approach ensures you gain practical experience, connect with the data science community, and continuously evolve your skills in this ever-changing field.

Real world project matter. That means that it is fine to start with data sets that are well-used in Kaggle (Iris, Titanic anyone...) I strongly recommend moving on to focus on specific real-world projects and the data associated with those projects. Ideally you can start with raw data so you experience the challenges and learnings from data cleaning, wrangling...before moving on to phases like descriptive statistics, transformations, feature engineering and other critical early steps in machine learning. Learn the key data insights before moving on to advanced modeling techniques - advanced machine learning is exciting but exploratory data analysis is a critical step that should not be skipped. Remember to have fun and keep learning!

You can build data products like websites, mobile applications etc. that'll have Data Science, Machine Learning and Deep Learning integrated as part of the product or add these to existing ones to apply your data science skills. As a beginner you can contribute to open source projects in Github where data science can be useful. Another path is doing research and publishing research papers utilizing Data Science, Machine Learning and Deep Learning etc.

Applying what you learned is one of the most critical steps to really hone your skills when picking up a new skill. Whenever you learn a new library or tool, I recommend finding a project that you can apply the skill to. Even if it doesn't have a direct application in your current role, find an online dataset that interests you and build a project! By building analytics in the project, you'll build new skills and refine old ones.

Create your own git repository and start pushing your projects on it. Create videos on the concepts which your have learnt recently or do podcast. Write blogs on different platform. Write appreciations posts on LinkedIn.

- Portfolio building: Create a platform, be it a blog, website, GitHub repository, or LinkedIn profile, to showcase your data science projects, code, and results. - Networking and feedback: Participate in online communities, forums, or events to network with other data science enthusiasts. - Learning from peers: Interacting with others allows you to learn from their experiences, exchange ideas, and receive feedback and suggestions.

Build a portfolio on GitHub or your own website. Showing off your skills is great! I have a few tips for this: - When you upload your code, make sure to consider how others could run it so they can demo it - Write posts on LinkedIn about your learnings and an explanation of the project - Keep building

Establish your personal Git repository and initiate the process of uploading your projects onto it. That is the best way for a programmer to show his personal projects.

Indeed, showcasing your data science work is a pivotal step in the learning process. It not only enables you to build a compelling portfolio but also provides opportunities for feedback and collaboration. To exhibit your work effectively, consider creating a blog, website, GitHub repository, or a LinkedIn profile where you can feature your projects, code, and outcomes. Engaging in online communities, forums, or events further facilitates networking with fellow data science enthusiasts, fostering a rich exchange of experiences, insights, and tips.

Data Science is highly dynamic, with noticeable trends evolving rapidly. Just 7-8 months ago, the focus was on creating multiple models for various NLP tasks such as sentiment analysis, classification, summarization, and Q&A. However, the current trend involves addressing all these tasks with a single model, often without the need for extensive fine-tuning. And you don't need to be ML expert to implement these models effectively for your business needs. If you're embarking on a career in Data Science, start with the fundamentals, then consider specializing in areas like NLP, computer vision, or reinforcement learning. Additionally, staying attuned to the latest trends is essential for staying relevant and effective in this dynamic domain.

Remember that learning Data Science (and any other domain) is a marathon, not a sprint. It may take time to become proficient, so be patient and persistent in your learning journey. It's okay to explore and experiment in the early stages of your data science journey. Your goals may become clearer as you gain more experience and exposure to different aspects of the field.

let me share my experience working on a Python-based predictive model project that explored the relationship between weather patterns and motorbike sales in Australia. I embarked on this project with a clear objective in mind: to help a local motorcycle dealership optimize their inventory management and marketing strategies based on weather conditions. The dealership had noticed fluctuations in sales throughout the year and suspected that weather might be a significant factor. By leveraging data and machine learning techniques, we were able to help the dealership optimize their operations, ultimately improving their sales and customer satisfaction. This experience taught me the importance of thorough data exploration, feature engineering

Data science is a dynamic and ever-evolving field, necessitating continuous learning and adaptation. To stay at the forefront, it's imperative to remain vigilant about the latest trends, innovations, challenges, and opportunities in data science. To facilitate ongoing learning, consider following data science experts, influencers, and publications on social media platforms, subscribing to newsletters or podcasts, attending webinars or workshops, and enrolling in relevant online courses or certifications. Furthermore, challenge yourself by tackling new projects, acquiring proficiency in emerging tools, and exploring different domains.

In contrast to other domains, it's vital to acknowledge that the AI field is rapidly evolving. Staying abreast of novel methodologies, emerging research, new libraries, and platforms can not only streamline your work but also keep you at the forefront of your field. Stay calm and keep learning please

All the contributions to this article are very helpful. The only thing I'd add is - its going to be difficult at times but keep at it. Its okay if everyone is talking about LLMs while you've just started with Linear regression. That's where we all started. Good luck for your journey! PS - I'd be happy to help if I can.

Data Science is a vast subject and keep on adding new tech . Read articles to update you knowledge. While updating your resume add your project and success rate.

Ok , just think of learning data science as embarking on a thrilling adventure, much like learning to play a new instrument. It's all about exploring, discovering, and having a great time along the way !

You can learn data science in your free time through online courses on platforms like Coursera and edX, YouTube tutorials dedicated to data science topics, engaging in practice projects available on GitHub, and following data science blogs for insights and updates. These resources offer a flexible and accessible way to enhance your data science skills and knowledge outside of formal education or work hours.

When it comes to learning data science, understanding your needs is key. It’s not just about mastering tools, although I have a bias towards Python and Jupyter notebooks for their incredible utility. If you’re serious about a career in data science, the real journey starts with delving into the underlying mathematics and structures behind the models. There are many comprehensive courses you can follow online. This deep understanding empowers you to explain not just what a model does, but also its strengths and limitations. It’s about developing an analytical mindset, honing those problem-solving skills, and embracing research. It’s the blend of these factors that makes a great data scientist.