The data scientist profession has piqued the public's interest after being dubbed the "sexiest job of the twenty-first century" by Harvard Business Review. Many people are interested in this position because the title has a unique ring to it. However, it is the name that generates the most concerns. There are many online data science institutes offering the best data science courses that will help you to master the essentials of data science.
There are numerous articles and blogs on the internet feeding the curious questions with sets of information. There are various tools, courses, and applications for data scientist aspirants. In fact, the possibilities are endless. However, a majority of these counsels and information may be difficult and humongous to navigate for any fresh candidate.
So we have curated the basic 8 steps to becoming data scientists, most ideal for those just starting out their data science career plans.
How to become Data Scientist?
Before you do anything further, keep in mind that a data scientist has a stronger statistical background than a software engineer and is more skilled at software engineering than any statistician.
8 Critical Steps to Become Data Scientist
1. Improve your statistics, arithmetic, and machine learning skills
The Mathematical component of Calculus, Linear Algebra, and Statistics are integral to data science. Hence, your entry into the data science field necessitates your intimate familiarity with these concepts. Machine Learning has crucial importance in data science. Its role is to essentially automate the data analysis process and to provide real-time data-driven predictions with no interference from humans. Getting to learning concepts and operational principles of learning is a mandatory skill.
2. Learn how to program
The best way to go through this is to choose a first programming language. Python and R are open-source languages significantly employed by programmers and data scientists.
3. Recognize databases
As a data scientist student, you will frequently work with text files containing data. Once you enter the industry, however, a database is almost always employed to store information. It will be saved in databases like MySQL, MongoDB, Postgres, Cassandra, and several others.
4. Get Mastery of Data munging, visualization, and reporting
Data Munging entails the process of turning one raw format into another for easier consumption
While data visualization is the production and study of visual representations of data, the final hurdle to overcome in any data analysis is translating the analysis and results into an understandable report. Equip yourself with all of the skills you'll need.
5. Raise your game with Big Data
The underlying strategy and analysis must change when working with data at the magnitude of the web. The majority of data scientists work on problems that can't be solved on a single computer. They have big data sets that need to be processed in a dispersed manner. Hadoop is an open-source software framework for storing and analyzing huge datasets on commodity hardware clusters.
6. Experiment, practice, and network with other data scientists
Participate in competitions. Meet your peers in the data science field. Develop your intuitions by working on a pet project. These are your options for moving forward and definite steps to becoming a data scientist.
7. Internships, boot camps, or fetching a job
Be decisive, determinant, and organize your career path. Enter the real-life data analysis ecosystem with your freshly obtained knowledge and expertise.
8. Participate in the community by following it and engaging with it
Be on the constant move to developing yourself and upgrading your skills by networking and participating in the data science community. Look for what sites and people to follow. Connect with sites like KDnuggets, and people like Hilary Mason, and DJ Patil.
Those are the fundamental step how to becoming a data scientist. This sure will help and boost your career plan.