Data Science is a trending domain that is creating millions of job opportunities. It is a multi-disciplinary domain that combines Math, Statistics, Coding, and Data Visualization. Data Science is the process of extracting insights from datasets that contain both structured as well as unstructured datasets by putting algorithms on them and then using them to create a pictorial representation to make or form decisions that will help the business or organization grow.
Data Science is a huge field that is so big that there are so many concepts to explore. But since this is a relatively new field, there are lots of updates that surface each day, and a lot of advancements happen in the field. Like AI, ML and Big Data are increasingly being used within Data Science projects. That’s why if you are aspiring to become a Data Scientist, it will be good to have working knowledge and experience in these domains and tools. You can check out a Data Science Training Course from a recognized training partner. Here we will discuss how you could become a pro in Data Science in 12 months in these 12 steps.
Start with programming:
Data Science cannot happen without programming. You must have good working experience with coding. There are many languages that experts will suggest, like Python and R. It depends on many factors which language you will choose, the company you are working for, the packages and libraries you use, and different parameters which will help you decide which language is better. I will suggest you learn Python and SQL languages. Python is a very beginner-friendly language and has many advantages over R. It has many libraries and packages which will help you master Data Science.
SQL is a database language that is used to manage and handle RDBMS (Relational Database Management System).
Many hate Math, and it’s one main subject of Data Science. If you wish to become a pro in Data Science, then learning Math is the only way to go about it. You need to learn Linear Algebra, Probability, and Statistics, Calculus, etc. Data Science is a wide field combining many fields of Math.
Develop a hands-on approach:
Developing a hands-on approach is the most important step in all of the 12 steps required to become a pro in Data Science in 12 months. There are many theoretical aspects of Data Science, which you have to learn. But they aren’t enough. You must practice and develop your practical skills from scratch otherwise, those essential concepts won’t stick in your head.
Practice various libraries:
The best part of choosing Python is that it has various libraries and packages that you must practice and master to advance your Data Science journey. It’s easy to install and use these libraries, which are easy and efficient to use as they reduce the time to code. It increases productivity. You can learn about Pandas, Matplotlib, NumPy, Scikit-learn, NLTK, etc.
Learn Exploratory Data Analysis and Data Visualization:
The visual aspects are one of the essential parts of the Data Science project. Without these graphics or visual objects, it’s impossible to capture the insights of the datasets. EDA or Exploratory Data Analysis helps you extract insights beyond data modeling generated by data. It will help you get a detailed analysis of the data in our hands. ML is used extensively in this field.
Revise and Practice:
The only way every little Data Science concept is going to stick in your head is by revising and then practicing it daily. It’s hard to recall if you don’t inculcate the habit of practicing daily. Keep updating your knowledge base and keep revisiting the already studied concepts. If you are ready for job interviews, then check out Data Science Interview Questions.
Exploring and Analysis:
Before committing yourself to any project you must analyze the problem statement and then explore datasets and analyze it. As data available on the intern are not clean or will be too clean. So you must always analyze and explore before starting out the project.
Now start working with Smaller projects:
Once you feel you are ready with all the concepts of Data Science, then start working with smaller projects which will give you enough ideas. To understand the essential beauty of Data Science, you need to work on projects bit by bit. Then only you will be able to understand the essential concepts hands-on.
Research is an integral part of any Data Science project. To start a project, outline a problem statement, or advance your features, or extract insights you need to do a lot of research work. Especially if you are to build a model with AI or ML, then you need to have a lot of data that you need to source, understand, transform, and then clean to use in the prepared models.
Finally, build end-to-end projects:
Working on projects will help you learn concepts much faster and revise them by getting sufficient hands-on experience. End-to-end projects will help you learn much more and will help you revise the entire Data Science concepts. Think of it as a CAPSTONE project which combines everything you learned during the period of 12 months.