15 learning paths for a novice to start his journey in Data Science

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Data Science has been hailed as “the sexiest job of the 21st Century” and with the right reason. More and more people wish to start their career as a data scientist and yet there remains a lack of supply to the ever-increasing demand for data scientists in companies. Why? Lack of skill has left the HRs of companies in a candidate deficit. People are rushing after the money this job can provide without paying heed to the necessary skills required. It is never too late to begin again, to unlearn and relearn. For this reason, we bring you 15 learning paths for a novice to start their journey in Data Science.

1. Know your “why.”

Data Science is a tough path to walk on and conditions the brain to a certain way of devotion that most people cannot bear. To know why you are going through this intense training is imperative as it will be the only thing which will keep you motivated throughout the journey.

2. Faculty to learn by yourself

Machine learning, artificial intelligence, and deep learning are all newborns in the data science industry. It is quite possible that you may not find solutions and guides and checkpoints to help you with your journey. You need to learn to help yourself in these situations. Whenever you can’t figure something out, simply Google it. Read about it. Ask the data science community. Read articles, blogs, answers and other people’s questions as well. All of it contributes to your learning. But if self learning is becoming a hindrance then get hold of a competent Data Scientist who agrees to mentor you.

3. Be wary of false information

It is equally likely to find contradictory information on the internet on account of data science being a newly formed industry. It becomes imperative to know the credibility of your source and not follow up on someone’s speculations, especially on the collaborative platforms. Being mindful of what content you are using to learn can help you propel your career as a data scientist.

4. Get the terminologies clear

Data Science, Artificial Intelligence, Deep Learning, Machine Learning…These are all similar-sounding domains and yet differ quite extensively. Although a subset to each other, they are not to be confused with one another as they as different methods of approach and applications. Read good resources and blogs like KD nuggets, Data Science insights and listen to recognized authors and speakers to help you with clarity and gain perspective as well.

5. Stack Overflow

This platform can be your new best friend. It is a collaborative platform where everyone is ready to offer help on any possible coding problems. Since you are going to be learning to code very soon, it becomes important to know where to look when you are stuck. Wikipedia, similarly, is going to prove to be extremely resourceful. Always take a few minutes out to browse through the page’s contents to gain insights.

6. Eat statistics for dinner

Maths does not taste well. And yet it is the necessary medicine to any data science problem. Statistical analysis and probability are key concepts when it comes to utilizing statistics for data science. Now, we do not ask of you to become a genius at it. All we recommend is a constant study of how algorithms work and the logic behind them. This will help you to understand the logic behind any problem instead of remembering it. Any book on statistical analysis is good enough to get your brain juices flowing.

7. Learn to code

It can sound a tedious and hard task initially. But when you realize how simple it is to represent real-world entities into variables and manipulate data sets into actual knowledge, you will soon start having fun with it. Python and R are the best languages we recommend for data science as they will assist in an easy interface as well as mathematical incorporation into code. There is a plethora of online courses and tutorials available that you can look up to.

8. Coding your way statistically

Since statistics is important, it becomes essential to combine the two aspects of data science. Without using statistics in python or R, you cannot hope to analyze or manipulate data according to your will. It becomes important to combine the best of both worlds. This is where it gets interesting. You can see the data interacting with more data. Some predictions can be made and knowledge that can be derived. Practice this fusion, and you will become good at processing data in no time.

9. Follow courses

From here on out, if you have trouble creating a path of your own, do not worry. There are loads of courses available in the market to help set up a checkpoint journey for you. Udemy, businesstoys, edx.com offer great data science courses to get you started in this field. The clarity offered there is equivalent to a private tutor itself. In any case, it is good to seek guidance for your data science path, especially since there can be quite a lot of contradictory knowledge online.

10. Get your Hands Dirty

Learn by applying. It is as simple as that. Kaggle is an open community for data science enthusiasts who learn from each other. There are challenges and learning tips there that are extremely useful as a budding data science enthusiast. Hackerrank is yet another useful competitive platform where you can complete challenges and apply your newly gained skills.

11. A heavier tool belt

You know how to code, you know how to solve a mathematical problem, and you know your way around data. Good. It’s now time to add more under your tool belt. There are a lot of specific technologies that you can use to become proficient in data science. The motive was to keep learning, and you shall have that. Database management, data structured, data visualization, algorithms, evaluation, interpretability & infrastructure security are good specialization areas.

12. Read, read and read all about Data Science

Books, blogs, YouTube captions, posts, answers & any insight you can feed your brain will help you gain better insights into the data science field. Here is the complete list you will need. Remember to not jump into all of them at once.

13. Take it slow

The goal is to be a proficient data scientist and not an insufferable know-it-all. Reading things is easy; learning from them takes time. And time is what you shall give. Think of this time as an investment in your future and yourself. Patience will help you go a long way.

14. Do not overload your PC

Most problems can be run easily on a pc, depending on your processor, ram and chipset. However, should you choose to overload your PC with an excess of data, it will most probably crash or halt. We learned it the hard way. There are a lot of companies like Microsoft, Google & Amazon that offer cloud services free for student IDs. That can solve your processor problems.

15. Use version control

Since you are going to be working on multiple versions of datasets, it is best to know what changes you made in your code and datasets. To keep track of these changes, you should use git. It is a GitHub owned software that will help you store and version control your datasets and code.