We know with evolving time, a new technology comes in or says one that exists takes a new shape and so, is DATA SCIENCE. Nowadays everyone is behind or wants to be a Data Scientist but not knowing the steps to take or where to begin from? In order to make the path easy, we are going to study the Road Map for Becoming a Data Scientist from scratch.

What is Data Science?

But before that, let us understand what is data science? We know human beings are made to make mistakes at some point of time and repetitive mistakes or say a phase of life becomes an experience. A day comes when S/he has to take the decision to change the lifestyle and at that point in time S/he recalls all the mistakes and tries to PREDICT the FUTURE by rectifying the mistakes. Here, the mistakes or say experience acts like a DATA with which S/he derives the insights and predicts the future accordingly.

Therefore, the process of deriving insights and giving opinions or arriving at some decision from an unstructured/structured data using various techniques is what DATA SCIENCE is all about.

Latest example is COVID-19 where DATA SCIENCE played a vital role:

  • Data Science gave a accurate picture of Coronavirus Outcomes
  • Data Scientist devised a speedier way to handle contact tracing
  • Data Scientists used Machine Learning to Find Possible Cures Faster
  • Data Science helped in tracking the spread

ROADMAP🚶(overview)

Data Science Skills ➤ Superpower/Specialization ➤ Hands-on Experience ➤ Top Notch Resume Perfect Portfolio ➤ Interview

#01 – Data Science Skills:

  1. You should know how to complete a simple project from End-to-End.
  2. Should have knowledge of Mathematics and Statistics(descriptive, inferential), Probability, and linear algebra
  3. Knowledge of SQL to communicate with database for Data Science
  4. Data Extraction, Transformation, and Loading
  5. Data Wrangling, Data Exploration and Data Mining
  6. Strong R/PYTHON SKILLS or both
  7. Should be familiar with at least 10 algorithms: Linear Regression, Logistic Regression, SVM, Random Forest, Gradient boosting, PCA, K-means, Collaborative filtering, kNN, ARIMA (Machine Learning & Deep Learning Algorithms)
  8. BI Tools, Tableau and Excel
  9. We should know how to use these algorithms in Industry

#02 - Superpower/Specialization:

In simple words, just like AVENGERS have their special powers even you should have some superpower which is nothing but a STRONG SPECIALIZATION in any one skill or more depending on your capability.

For Example:

  1. If you are a software engineer, then it is your superpower
  2. If you can build dashboards then VISUALIZATION is your superpower
  3. If you worked as a consultant, then Solving Business problems is your Superpower

Therefore, you need to figure out What is Your Superpower?

#03 - Hands-on Experience:

Only learning the skills will not help but you should know how to use your skills to work on some projects and gain more hands-on experience.

For that,

  • You should work on a lot of live projects which can help in enhancing your skills and expertise over the technologies.
  • Besides that, you should be able to solve problems using public data at platforms such as Kaggle.com, HackerEarth, etc.

#04 - Top-Notch Resume:

(Source: towardsdatascience.com)

  • Should be easy to find relevant information in 6 seconds or less
  • Highlights only the best/most important experiences
  • Visually stands out against the sea of cookie-cutter applications
  • Use the correct formula to frame your projects and experiences in terms of business impact(even if they were personal/academic projects)
  • Format: What you did -> How you did it -> Impact it made
  • Bad: built recommender system in python
  • Good: built recommender system in python using collaborative filtering and matrix factorizations that resulted in a 3% increase in basket size and a $3M increase in yearly revenue
  • Make sure your resume is easy to read — use www.readable.io and aim for a 5th grade reading level
  • Make sure you have the proper keywords that using www.jobscan.co

#05 - Perfect Portfolio

(Source: towardsdatascience.com)

  • Your projects should tell an easy to follow story
  • Should clearly visualize your results
  • Should be well-documented with high-quality, organized code
  • Includes a clear write of what you did and why
  • Demonstrates you can do the job of a data scientist

#06 - Interview

(Source: Quora)

Be prepared to code

  • SQL: There is no excuse for being weak in SQL as a Data Scientist.
  • General coding: You should be comfortable writing code with Python or R like you use them every day.

Be prepared to talk about data science / machine learning

  • Algorithms: you don’t need to know all of them, just the ones you usually use.
  • Techniques: things like feature engineering, evaluation metrics, cross-validation, or how to prevent overfitting…
  • Past experience, what you built, what was the impact, what did you learn from.

Be yourself

  • Don’t try to give excuses. Data Science is a large and generally new field, no one knows deeply about everything. If you are not sure or do not know about certain things, it is okay to just say so.
  • Be confident about who you are, what you have done, with evidence to back it up.
  • Be curious, hungry, and show initiatives.

-Yogesh Raghupati (Author)