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8 Smart Ways To Become A Data Scientist

Data Science has been coined everywhere, and everybody wants to express their views and thoughts on this subject even though they are a novice or lack adequate knowledge about data science! The idea that everyone can become a data scientist only by studying a few technological advances and solving any complicated problems cripples the world today. 

Let us first understand the career perspectives of data science. Data science has three pillars:


  1. Business Expert - Data Analyst
  2. Technology Expert - Data Engineer
  3. Statistics & Algorithm - Data Scientist

First, make it clear whether you want to become a data analyst, a data engineer, or a data scientist.


Smart ways to become a data scientist

  • Education requirement

Education is known to be one of the primary sections of resumes and it is not going to change at all. Educational background serves a signal to the employers to better know about their future employees. When it comes to Data Science, you’ll find most of the professionals holding Ph.D. education. As per the data gathered by 365datascience, the typical data scientist in 2020 holds a Master’s degree (56%), a Bachelor (13%), or a Ph.D. (27%) as their highest academic qualification. The highest level of education achieved by data science professionals is a doctorate. Though, the considerable drop in being a data scientist is a bachelor-level degree only. The advanced levels are just to ensure a specialization in data science. We have discussed degrees! But what is the best degree to become a data scientist? To answer this, degree related to Computer Science or Statistics and Mathematics are inclined to data science proficiency. Data Science and Analysis graduates have made their room on the top of the research on the career path of becoming a data scientist.

Let me tell you a hack here! In reality, no single degree can prepare a person for a real job in data science. Even if you are post-graduate in data science, but you don’t have strong analytical and programming skills, you can’t be a data scientist.

  • Learning formats

Primarily, you can choose either of the three learning formats- Online training, Offline training, and Self-learning. You can avail of data science training from various platforms like Coursera, Deakin, Udemy, and many more. Offline training, on the other hand, can also be a great option if you have proper resources of data science learning available around you. If you are already in the profession and don’t have time to avail of both online and offline learning, then you have another option to explore, it is self-learning. What you need is to religiously follow various resources online and offline. You can subscribe to various YouTube channels providing data science training and watch the videos as per your time availability.

  • Hands-on learning

Solving real-world data science problems will only improve your practice in data science. But from where will you start? Working with the dataset with the classic Titanic data set with survival classification or clustering is likely to damage your portfolio, rather than help. Instead, consider taking ideas from shared Github ventures. Look at what others are creating based on the network that you acquired from LinkedIn through tech sessions and certifications. Feel free to use samples from Github projects on Udacity or Coursera. Then mix real datasets from Google Research, Kaggle, or search for an interesting dataset and start building real problem solutions. If you are involved in a particular field or business, consider looking for public datasets and developing a sample project. For example, if you're interested in fintech, try building a loan approval algorithm using the public loan data from Lending Club. The key takeaway to work with real datasets is that they are very messy and noisy compared to academic ones. You need to prepare the skillset by practising online datasets available. What all you need to do is:

  • Download and open the data in Excel or a related application 
  • See what trends would you find in the data by eyeballing them 
  • If you think the evidence-backed the article's conclusions? Whether or not? 
  • What more questions do you think you should answer with the data?

  • Programming skills

Starting up in programming can be very challenging, and there are a lot of myths out there that make people think programming is a skill they can never learn, or that landing a job as a data scientist is almost mission impossible. They couldn't be any worse. To become a Data Scientist, prepare yourself to master the following skills:

  • Statistics
  • R and Python programming language
  • Data wrangling
  • Mathematical skills like calculus and linear algebra
  • Data visualization
  • Visualization tools like a tableau, ggplot, plotly.
  • Predictive modelling
  • Machine learning

Research shows that the sector is continually changing and adapting to market needs as well as its rising importance in academia and around. Universities are meeting demand while Master's is defining itself as the traditional golden degree. Python keeps consuming away at R, but even SQL is on the rise!

  • Algorithmic approach

Your prime emphasis should be on a deep understanding of the algorithms. You should be able to answer certain questions as-

  • What are the input and outputs of the algorithm?
  • What are the assumptions that are part of the algorithm?
  • Where does it fail?
  • And the master question to test your expertise. If given time and resources, can you manually run an algorithm with just a pen and paper?

If you can answer these for any algorithm then you have acquired experience for that algorithm at the data science level. Practice makes a man perfect is a right saying to be quoted here!

  • Stop believing myths

Whilst on the learning path of data science, you might get to know about several myths. My recommendation to you here is DON’T BELIEVE ANY MYTHS, I repeat, DON’T BELIEVE THEM AT ALL! Rather, focus more on:

  • Data science is about being able to answer questions and generate value for business, not software 
  • Learning the definitions matter more than learning the syntax 
  • Creating and sharing projects is what you are going to do in an actual data science job, and practicing this way will give you a head start

  • Practice on storytelling with data

A story in the sense of data science is a tale of what you've discovered, how you find it, and what it means. An explanation could be the revelation that in the past year the company's sales have fallen by 20 percent. It's not enough to merely state the fact — you're going to have to explain why sales fell, and how to address it. The key components of data-storytelling are: 

  • Understanding and contextualization 
  • Find ways to investigate 
  • Using imperative data visualization 
  • Applying various data sources 
  • Have a coherent narrative

  • Supplementing data

In your data scientist job, you will be given raw datasets only. So, you should have the ability to combine raw datasets before performing data analysis. The first move in creating a supplementing a good quality dataset is to know what expertise to show. The key skills businesses seek in data scientists, and therefore the primary skills they want to show in a portfolio, are: 

  • Control of communication 
  • Power to work with others 
  • Technical know-how 
  • Power to reason with data 
  • Motivation and the potential to take action

Are you a future-ready Data Science professional?

Learning data science will be time-consuming – say 3 to 12 months or more of regular learning is required. By demonstrating a higher demand for junior data scientists than the US and the UK, India has won the position of the best country to start a career as a data scientist. If you just have a Bachelor's degree, it is still the place to be. You just began a life-long adventure, which promises exciting experiences to enjoy. So keep your interest refreshing, develop your collection of programming skills, and good luck in your career in data science! The ball is in your court!

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