New Year is not just replacing your table calendar with a new one or waking up next morning rubbing your eyes. It’s celebrating the joy of a new beginning. It gives a perfect reason to inculcate new habits. It is the arrival of new ‘Hope’.
If you are reading this, I’m sure data science excites you! You want 2016 to be a game changing year for you. Don’t you? You can make it possible, if you commit to these resolutions today. You must understand that becoming a data scientist is a process, not an event. It’s not an overnight success. Hence, you must patiently work towards your goal.
I’ve shared a list of resolutions every data scientist should make depending on where he / she is in their journey. This is of course a generic list, you should adopt it for your needs. I am also provided a checklist below which can be downloaded to track these goals.
Note: These are generic resolutions meant for an aspiring / experienced data scientist. This article might not be useful for people from domain other than analytics.
New Year Resolutions for a Data Scientist
I’ve categorized these resolutions according to three levels in the life of a data scientist. You decide which suits you the best and work accordingly. You can move to the next level, once you have satisfactorily completed your level. I’ve also listed the best course available on the topic. For optimum benefit, I’d suggest you to take these courses one by one. If you still find them hard, discuss with me, I may have an alternative for you. For your convenience, I’ve also shared a checklist below which can be downloaded.
Who’s a Beginner? – If you are completely new to analytics and data science. You have no idea how this industry operates. And yet, curious to pursue your career in this field, you are a beginner. These should be your resolutions:
1. Start with a programming language. Either R or Python
I’ve seen students trying hands on both R and Python. Eventually, they end up with nothing. This is a deadly approach. You must promise yourself to learn R or Python in depth. Both are open source tools hence widely used in companies too. Python is widely recognized as the easiest programming language. R still remains the favorite statistical tool. Choice is upto yours. Both are equally good.
2. Learn Statistics & Mathematics
Statistics is all about assumptions and progressions. But, you can’t progress in this industry without statistics and mathematics. It lies in the heart of a data scientist. If you are weak at mathematics, it’s time change that equation. Get comfortable with powerful statistical techniques, algebra and probability, The are many awesome courses available on statistics by Khan Academy, Udacity etc. You can get started right now if you install these apps.
3. Enroll in one MOOC at a time (Most Difficult)
Massive Online Open Courses a.k.a MOOCs are free to access and study. But, this one is the most difficult promise you can make to yourself. Student often tend to enroll and study multiple courses at a time and complete none. Hence, you must focus on one course and finish it before proceeding to the next. You can check coursera, edX, Udacity to undertake any course.
4. Engage, Discover and Socialize in Industry
You need to know what’s happening in the industry. We live in a dynamic world. Things change overnight. May be a technology prevalent today might become obsolete tomorrow. You must talk with experienced professionals, industry experts and meet your future self. So start participating in discussions, meet-ups, follow blogs, join groups and read books. To check all these, you can follow us on Facebook for latest updates on this part.
Who’s an intermediate level of data scientist ? – If you have finished the previous level, and you’ve experimented with basics of machine learning, you have gained knowledge to build predictive models, then you possess an intermediate level. Completing this level need huge determination and hours of practice. Are you ready for this challenge ?
1. Understand and Build your Machine Learning Skills
Machine Learning is the future of data science and technology. All the major companies have heavily invested in hiring candidates with this skill. No doubt, it’s in huge demand these days. And, this is a chance for you to get the best out of this situation. This year, you should dig deeper in machine learning. Master Regression, Clustering, CART in depth. Here you’ll find free resources on machine learning.
Courses to Do: Complete Machine Learning by Andrew Ng.
2. Focus on Ensemble and Boosting Algorithms
Once you feel confident about machine learning, get to the next models. Using boosting and ensemble, you can could achieve model accuracy much higher than other algorithms. This topic would be covered in the free resources shared above. But, promise yourself to conquer this topic with great understanding.
3. Explore Spark, NoSQL and other Big Data Tools
This year, you can start your journey in big data. Considering the fact that demand of big data professionals is surging, you must learn Spark. It has recently gained popularity. The future of big data lies in Spark. It is widely used tool to handle and manipulate big data. Along with spark, you can extend your expertise to NoSQL , Hadoop as well.
Courses to Do: Take your first step with Spark.
4. Educate Community Members
What could be better than sharing knowledge! This year, you should start sharing your knowledge with people who are struggling to learn data science. You can join active data science forums, answer their doubts and educate them with useful tips and hacks. You could also lead meet-ups happening in nearest circles.
Things to Do: Follow us on Facebook.
5. Participate in Data Science Competitions
Time to test your knowledge. This year, you must participate in competitions. It would introduce you to your weak and strong areas. Moreover, you’ll become confident of the knowledge you’ve acquired. I’d want you to rank in Top 500 data scientist on Kaggle. For now, you should aim to become the Last Man Standing.
Addition: Competitions can be bit difficult at times. You can also check out these practice problems to check your skills and knowledge. They aren’t difficult but surely FUN!
I don’t need to define the people falling in this category. These people know of data science what most people are afraid to even try! They’ve reached a level where life is cozy and easy going. But still, they love challenges. They are experienced professionals. Here are some resolutions:
1.Build a Deep Learning Model
This year, you have to set an example for the people aspiring to become a data scientist. You must promise yourself to try build model on deep learning this year. People around the world and already using it for making predictions. It’s an advanced level of machine learning. The accuracy is obviously better than normal machine learning models.
Courses to Do: Complete this Deep Learning Tutorial.
2. Give Back to Community
I believe knowledge is meant to be shared not stored. The more you share, the more you’ll learn. It’s being said, ‘if you learn a new concept, explain it to 2 friends of yours. You are more likely to remember that concept for long’. This year you must take a resolution of helping people in analytics community with your knowledge and experience. This will allow many struggling people to find a shore in this domain.
Things to Do: Share your knowledge on Discuss.
3. Explore Reinforcement Learning
Reinforcement Learning is the most powerful yet less-discovered aspect of machine learning. This year, promise yourself to research in this field. It will surely be challenging but worth trying. Self driving cars, spy drones are results of reinforcement learning. Once you start with this, you’ll automatically get into artificial intelligence.
Courses to Do: Complete tutorial by Andrew Moore.
4. Rank in Top 50 on Kaggle
This year, you must promise yourself to uphold ‘master’ status on Kaggle. Precisely, secure a rank in top 50 data scientist on Kaggle. Participate in competitions which suits best to your knowledge. Team up with other kagglers. At this level of competition, you’ll end up learning concepts which you wouldn’t have learnt otherwise.
To Do: Participate on Kaggle.
Track your Progress. New Year Resolutions 2016 Checklist: Download
I understand, these resolutions can be challenging for you, but still worth trying. You are free to take up a resolution according to your current situation. I’ve simply enlisted the most important ones which an aspiring data scientist must take up.
Last week, I realized that people aren’t confident enough in deciding a new year resolution. This was a concern for me. Hence, this lead me write this article. I hope, before 2016 ends, you would finish beginner level (assuming you are a fresher).
This article would have cleared your confusions on making new year resolutions. As an aspiring data scientist, I’ve already put a lot of things on your plate to eat. Chew one by one and proceed. If you find difficulty in successful completion of your resolutions, feel free to share your thoughts with me in the comments section below.