What are the best ways to improve your data science skills?

One effective way to improve data science skills is through online courses, especially those offering project-based learning. For instance, DataCamp provides interactive projects that mimic real-world scenarios. In one project, you might analyze a dataset from a tech company to forecast user engagement trends using Python. This hands-on approach not only solidifies your understanding of key concepts but also prepares you for similar challenges in the industry. Such practical experiences are invaluable in bridging the gap between theoretical knowledge and real-world application.

Online courses are effective to build data science skills. However we need to keep few things in mind when we are going ahead with online courses. it's important to check ratings of courses and the reviews. Also we can check the LinkedIn/GitHub profile of the speaker to understand his /her experience in the subject and coaching. If it's a live session, then it's important to ask questions when we have queries. Unlike an in-person session, it's difficult for the speaker to make out from people's expressions whether they are following in an online session. Besides online platforms like LinkedIn Learning, DataCamp, Udemy, Coursera etc, I also look for online courses provided by MIT, Harvard and other premier institutes.

Indeed, although hard to believe at first, online courses can help you launch your career in data science. Just make sure that you follow up what you learn online with practice and reading more detailed resources, which are often linked to within the courses.

Online course take case need hands on course. Online course is free course and paid course. beginner's case, free course is first chose. course is join the project is for person have no work experience very important.

Online learning is very useful, but ultimately what is going to make it useful is going in with a use case in mind and working on the use case hands on. This will both cement the skill learned as well as help in finding what is next, what are conditions where this works, what are pros/cons?

Most important ingredient for building data science solutions is data. Hence it's important to practice on building data science applications on real world data. The real world data includes business trends, relationships, patterns and also data intricacies (biased, unclean, missing data). Our objective is to clean this data and make it ready for modeling. Then we have to uncover trends and relationships hidden in this data using exploratory data analysis. Also we have identify model class which are best suited to capture trends and relationships of this data. You can see that it's all about the data and knowledge hidden in it which we want to capture using data science. Kaggle, Google dataset etc. all have real datasets.

To enhance data science prowess, dive into real-world datasets. They're the gym for your analytical muscles, providing varied weights of messy, complex data akin to real-life problems. Training on them builds robust, versatile skills that textbooks can't match.

Doing data analysis and exploration and training a few machine learning models on publicly available datasets will confront you with some of the challenges you'll face when working on the industry. It is a great opportunity to gain some experience if you are new to data science or coming from another field, and it will equip you with the experience you need to answer many questions you might be asked during an interview.

The best way to gain experience as an aspiring data scientist, is working on personal projects. Start by finding a topic that interests you, then download a dataset from Kaggle or Google Dataset Search, and work on your project! It is even better to create your own dataset by scraping a website, as this is necessary in some real-world projects. All that may sound daunting to a beginner, but there’s no other way to become skillful and valuable to employers. Working through the examples of online courses and books is simply not enough, as you need to stand out from the rest.

Build a strong foundation in mathematics and statistics: Data science heavily relies on mathematical and statistical concepts. Strengthen your foundation in these areas by studying topics like linear algebra, calculus, probability, and statistics.

Data science community is the best way to engage with data scientists, learn from each other and get guidance and support. A data science community which has dedicated and quality data scientists, the members of this community will learn significantly form each other. There are multiple data science communities like GitHub, Reddit etc. which can be leveraged. Also in India, Analytics India Magazine, Analytics Vidhya etc. have created strong data science communities which cane be leveraged.

1. Kaggle: Kaggle hosts a vibrant community where data scientists share notebooks and datasets. 2. GitHub: A hub for sharing data science projects and collaborating with others. 3. Reddit: Subreddits like r/datascience and r/MachineLearning are good for discussions and advice. 4. LinkedIn Groups: Various data science groups exist for networking and knowledge sharing. 5 Towards Data Science: A Medium publication with a community of writers and readers focused on data science. 5.Learn the Prompts for chatGPT

Blogs are useful but you can't be stuck on them. As you grow your knowledge on many data science topics, books and papers are the one that will stay as real sources of learning.

Blogs are a good place to start if you are a complete beginner. You can find new ideas, techniques or methods that you were not aware of. It is time consuming to find good quality blogs though. Eventually, I recommend reading from books, or even better, scientific articles like those from ArXiv.

Search and find out on the internet. And read blogs and articles. Also read, learn and think about social media articles. By posting, discussing, and joining the community, you can deepen your knowledge and increase the amount of knowledge you have.

After working on your personal projects, sharing them with others is the next logical step. Github is a free service that lets you create repositories for your projects, that can either be public or private. Creating a strong portfolio of data science projects is the best way to promote yourself to employers, as it showcases your practical skills and accomplishments. Furthermore, you should avoid using toy datasets like Iris/Titanic Passengers, and try finding new datasets in a domain that interests you. This will motivate you to focus on the project and get a better result! Also, make sure to document each project and write an executive summary with your insights. Communication is a key skill for data scientists, so don’t underestimate it.

Register your portfolio on Github. Github is a database. You can post images, etc. By posting them all together, Github members can see them. Also, when job hunting, use GitHub to appeal to hiring managers.

Building a portfolio not only helps you in showcasing your skills but also acts as a reference when you feel like refreshing your skills. A portfolio also acts as a repository of the boilerplate code that you can lift and shift into your future projects.

The easy way of learning new platform/software is to apply techniques to a familiar portfoglio and get the same results. Anyone can create their oun portfoglio even without using projects participation data due to data ounership issues. There is a lot of free public data on internet. My advice is also not to use already established public database, but to create one by consistently colecting data in areas of interest over the time. Along with creating such databases, one learns the sources of such data, how to structure the data for analysis, clean and verify it, even how to present both the data and the analysis on it in an uderstandable manner.

Learn data science effectively by focusing on two key aspects: 1. Stay Updated: Cultivate curiosity and embrace continuous learning to stay current with industry trends by actively seeking knowledge. 2. Skill Enhancement: Sharpen your existing skills by seizing opportunities to apply them. Engage in projects, participate in hackathons, and practice on platforms like LeetCode to enhance programming and problem-solving abilities. Consistent practice in these areas ensures long-term progress. For added motivation, tune into podcasts, watch TED Talks and videos on YouTube, and read relevant blogs. This not only keeps you ahead but also broadens your thinking, enabling you to tackle real-life problems with innovative solutions.

Hi there! I'd like to share key learnings from my data science path, hoping it helps you too. It's all about practical engagement: Real Projects: Immersing in actual projects has been my best teacher, applying theory to real-world problems. Tool Exploration: Using platforms like Google Colab for hands-on method trials is invaluable. Industry Insights: Studying data science applications in various sectors has sharpened my problem-solving. Continuous Reflection: After projects, I reflect on learnings for continuous improvement. Remember, the real growth in data science lies in applying what you learn. That's been my tenet for success in this field. Hope this helps!

My experience was doing python code using Jupiter Notes on IBM Cloud. And data science requires data visualization etc. I was making use of the library. You can experience the joy of learning by making good use of social media such as Linkedin to gather information. You should also take advantage of free online courses and communities. Although I haven't experienced it yet, participating in a hackathon is also a way to improve your skills. There are people there who have a high level of skill.

If you understand supervised learning models, every learner should first learn about linear regression and logistic regression from every single perspective, and with minute details. This will help you compare the model's simplicity or complexity with the basic models. Every complex model is built up from the basic fundamental models.

Showing up to a place where like minded individuals are is a great way to learn about resources, methods and opportunities. I found job opportunities through community slack channels, good learning resources that were recommended on LinkedIn by people like you and me, or new Python libraries at meetups like Pydata.

Some ways to getting started with Data Science are as follows, - Use networking options to form a set of like minded individuals to co-learn & collaborate (platform for initiation & growth) - Engage with a mentor, identify someone with expertise & use Linkedin, YouTube, etc to follow insights, updates & ultimately become a mentee with one to one touchpoints (this will be an avenue for guided skill building) - Look out for events, community gatherings, etc to have an additional venue for learning (this will be a way for continuous skill growth)