So you’ve taken the plunge. You want to become a data scientist. But where to begin? There are far too many resources out there. How do you decide the starting point? Did you miss out on topics you should have studied? Which are the best resources to learn?
Don’t worry, we have you covered!
Analytics Vidhya’s learning path for 2016 saw 250,000+ views. In 2017, we went even further and saw an incredible 500,000+ views! So this year, we have made the learning path more interactive than ever before and we can’t wait for you to experience it yourself.
What changes have we made in the learning path?
This year, the learning path has been designed on a completely new LMS portal. This portal allows you to track your progress as your data science journey continues. We have designed questions and exercises after each module to test your understanding. You will also be able to access the related hackathons / practice problems from the same place.
We even have a discussion portal within the learning path where you can share your doubts and queries and even post the awesome projects you’re working on!
Take a sneak peek below of how the progress tracking looks like:
Here it is then – the ultimate learning path to becoming a Data Scientist in 2018!
Just a few things to note before you experience our new LMS portal:
- We have published the resources for January and February to get you started on your path. We will publish the remaining links in the next couple of weeks.
- The majority of this learning path is based on learning through Python. Why is this so – because we are increasingly getting convinced that Python is the way to go for complete beginners (at least for 2018).
Below is a summary of the learning path – an overview what you should follow throughout the year. Let’s get cracking
Getting Started with Data Science and Python
By the end of January, you’ll know what role data science plays in the industry. You’ll also be able to answer the burning question – why use Python and how is it useful?
Statistics, Data Exploration and Basic Data Visualization
Before this month is over, you should have a firm grasp over the basics of statistics. You should also be proficient at exploring the dataset given to you and know the role data visualization plays in this. The budding data scientist is slowly coming out!
Probability and Machine Learning Basics (Part I)
Time to get into machine learning!
By the end of the month, you should have a firm command on the basic machine learning topics like linear and logistic regression, among others. To test what you’ve learnt so far, we will provide you with two projects to apply your newly acquired data science skills!
Machine Learning Basics (Part II) and Feature Engineering
Continue learning the ML basics and by the end of April, you should know enough to take part in hackathons are secure a decent rank. Also, go in depth into feature engineering – one of the MOST important things in data science.
Build Your Data Science Persona
Building models is not enough. The real test of a data scientist comes in explaining the power of the model you’ve created to non-technical people. By the end of May, you should have structured your thinking and personality as a data scientist to be able to do this.
This is a very critical month in your progress. Attempt to get a high ranking in hackathons and competitions all the while learning how to make an impactful presentation of your work. Also, start looking for an internship; you should have enough knowledge by now to secure one.
Advanced Machine Learning and Time Series Modeling
Deep dive into advanced machine learning. With half the year behind you, you should be ready to tackle advanced ML algorithms and time series models.
Dealing with Unstructured Data
Unfortunately, most real-world data comes in an unstructured format. This month you should get a deeper understanding of how to deal with unstructured data in business scenarios including learning the Natural Language Processing field. At the end of the month, you will be given a few projects to apply your newly learned skills.
Introduction to Deep Learning
Here comes one of the hottest data science subjects around – Deep Learning! By the end of August, you should be able to deal with basic neural network problems. As usual, we will provide you with a couple of projects to test your mettle.
September – October
Practice is the name of the data science game. Keep checking your progress by taking part in competitions.
By the end of October, you should also be familiar with topics like recommendation methods, and reinforced learning. This is also when you should start taking up a language like SQL to interact with databases (a truly important skill for a data scientist).
November – December
Apply for jobs and further enhance your portfolio
If you have seriously followed this plan, you should be able to deal with interview questions. Continue to acquire new skills, delve into Big Data and make sure you stick to your plan!
A few things to keep in mind:
A few pointers to make this learning path (and 2018) super successful for you:
- Follow and experience it on our new LMS. It will need a one time sign up, but once you are done (or you sign in with your existing Analytics Vidhya id), you can track your progress and assignment at a single place. It sounds like I am selling you our new LMS – but it is free and makes life super easy.
- Learn, Engage, Compete and Get Hired! That is exactly what you need to do. Make sure you follow the activities mentioned. Data Science is all about learning through practice and showing curiosity through data. Follow these and you will be hired before the year finishes.
- Remember – depth matters more than the breadth – So, whatever you do, do it well. While we have laid out things on a time line – you should be doing this on your own pace and time. If you need more time to grasp statistics – you take that. You are not missing a bus by grasping the fundamentals
Let’s get started
2018 – here we come! I hope this year our path gets 1,000,000+ visits! We have made sure that we put all our wisdom and experience in creating it. Having said that – Is there anything you feel we should have included?
Or if you had taken our learning path last year – what was your experience and how do you like the current changes? Let us know your thoughts in the comments section below!