How to Learn Data Science from Scratch?

 How to Learn Data Science from Scratch?

Data Science has been termed as the ‘sexiest’ job of the century by none other than Harvard Business Review. No doubt, it enjoyed the cult status till 2019 and while its popularity dipped a bit in 2020, it was still at No 3 position. According to Glassdoor, it is still the best of the jobs for aspiring professionals. 

But there have been rumors of it becoming obsolete. Yes, they were just that – rumors. Data Science won’t become redundant – at least not till organizations are producing data at a tremendous speed.

Let that sink in! 

Data Science professionals would always stay in demand; however, the nature and the required skills would undergo changes. Reason: There will be lots of evolutions in the basic data science skills and profile.

So how do you learn data science from a scratch? 

While there are many theories saying that you need to be an expert at coding the truth is you don’t need necessarily need coding skills to learn data science. Yes, mathematics and statistics form the core of data science, the fact remains – you can still become a data science professional even without experience in either of them. 

It doesn’t matter if don’t have a swanky degree, or a background. You can still learn data science.

Yes, you read it right.

The question: What are the challenges in learning data science from scratch?

The major challenge in learning data science is to figure out ‘whence to learn data science.’ 

There are numerous resources online from where one can learn data science. The confusion arises when you don’t know how to separate the wheat from the chaff. Having said that, as per the experts, there are generally three ways you can learn data science.

  1. You earn either earn a bachelor’s or a master’s degree in data science
  2. You can also join a Bootcamp course
  3. Or you can learn it on your own

How you choose to start learning depends largely on your academic as well as a professional journey so far. 

And whatever path you choose to start your journey with, there are two major skills that you would need to master – technical and non-technical skills – every skill that you would require to become a good data scientist would fall under these two categories. 

Technical and Non-Technical Skills You Need to Learn

Aspiring data scientists need to master both technical skills as well as non-technical skills. As mentioned earlier, while programming is required, it is not imperative to have an in-depth knowledge of the same. Without further ado, let’s look at the technical and non-technical skills you would need. 

  1. Machine Learning
  2. Statistics
  3. Mathematics
  4. Deep Learning
  5. Data Wrangling &visualization
  6. Programming
  7. Large data sets processing
  8. Big Data

The above mentioned are the core technical skills but as aspiring data scientists, you would also other technical skills like the knowledge of Python, R, C/C++, Java, and SQL. Python has become one of the most used languages for a data science professional.

Note: You would be able to organize the unstructured data with the help of various programming languages.  

The choice of programming language depends (4) on the following things

  1. What type of data science jobs you would perform? 
  2. What are your career interests in relation to the data scientists? 
  3. Which languages you know already before you decided to jump into data science career? 
  4. Difficulty levels you are ready to tackle
  5. Last but not the least your educational ambitions

Once you have considered all the points, you can go for the languages that are more in demand and are bit easy to grasp. Reason: In-demand languages will put you in a good stead in the jobs market.

The non-technical skills include

  1. Excellent communication skills
  2. A sound business acumen
  3. Exceptional data intuition

The non-technical skills are equally important for you to succeed in the field of data science.

Reason: 

Excellent communication skills would help you present the data analysis in simple and easy language; with sound business acumen, you would be able to analyze the data to find both the problem as well as solutions related to the business. 

1. Data intuition skills are one of the most important skills and a good data scientist knows when to scratch the surface and find the data that could offer valuable insights. You could develop data intuition skills by working on data – both large and small sets of data.Now that you know what skills are required to become a data scientist, let’s explore the ways you could learn data science. One of the best ways to learn data science is by doing data science. Implying you need to work on data science projects. You could do that by joining internships, through data science boot camps, Kaggle. Practice is the way forward in data science. You may have read all the machine learning algorithms, however, if you don’t practice whatever you have read you would forget it. Result: You would be at a loss when you are presented with real problems in the corporate world. 

2. Understand and get a hold of your basics in data science. The core knowledge in data science concepts will help you in understanding and grasping the advanced concepts of data science and machine learning. It is advisable to first understand the various data science concepts and components before you hop onto learning the numerous programs. Remember: The basics are crucial to understand and grasp if you wish to succeed in this ever-evolving field. Some of the important fundamentals of Mathematics as well as programming are mentioned below—

First programming fundamentals that you would need to master include –
  1. Loops
  2. Knowledge of numerous data science packages as well as libraries. 
  3. If you intend to learn data science with Python, you should be aware of the packages like –
  • Pandas
  • NumPy
  • Scikit
  • Sklearn
  • TensorFlow

3. You could browse for data science programs that don’t charge anything.  There are numerous data science programs that are free and easily available online. In case there is a concept that you don’t understand you could search that online from other programs available. Also, remember that every individual is different and each one of us has a different learning speed and technique. So, what might work for other data science professionals might not work for you. 

These steps are ongoing. Implying: You must keep learning, upskilling, and reskilling to stay updated with the latest developments in the field of data science. As mentioned earlier, there is no dearth of resources to learn, however, the key is to practice, practice, and more practice.

Networking is another way to keep learning from experts and improve your skills. Kaggle – the world’s biggest community for data science professionals – is considered as one of the best platforms to learn data science from scratch. The data science hackathons at Kaggle would help you understand different algorithms of machine learning.

In a nutshell, to learn data science from scratch, you would need a learning mindset and be ready to upgrade and upskill your knowledge and skills.
About Swati Gupta:

A Disruptive Technology writer and editor with almost three years of experience in the field. A journalist transitioned into a lifestyle content writer and consultant with almost 18+ years of experience. Contact me for writing, editing, and proofreading here or at swatithewriter (at) gmail (dot) com

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