How Beginner Can Start to Learn Data Science!!!

“In our world of Big Data, businesses are relying on data scientists to glean insight from their large, ever-expanding, diverse set of data … while many people think of data science as a profession, it’s better to think of data science as a way of thinking, a way to extract insights using the scientific method.”

— Bob E. Hayes

Data is the buzzword today, and data science is a significant field for any industry. An aspiring data science enthusiast can learn data science from multiple platforms and sources, not necessarily from a college itself, and hold a degree. Different blogs and articles highlight the online courses and learning opportunities in various programs that a learner has to observe. After resorting to information and guidance from several sources, one should go for a specific platform to start learning because the right choice of learning material and pedagogy play a crucial role in building knowledge. This article does not focus on guiding one towards specific online platforms and courses. Here, we concentrate on some of the essentials to start learning in data science and understand the subject, as data science is highly singular than all other software concepts out there. One must note that every learner is unique and has their methodologies to understand a concept, and there is no one-size-fits-all approach, especially learning data science.

Getting started with Data science is not a strenuous task. All it needs is a dedicated and motivated mindset with a constant quest for self-improvement. Along with the suitable material, software, and learning methods, one can easily kick start their study and tread on the path of excellence.

Getting Started for a Beginner:

Data science encompasses preparing data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis. Analytic applications and data scientists can then review the results to uncover patterns and enable business leaders to draw informed insights. the definition of data science says that it is a field that maneuvers mathematics, statistics, and programming skills and concepts to extract insights from data. Programming skills, along with mathematical and statistical skills, are fundamental; thus, one has to be well versed with a programming language such as Python or R that are commonly used everywhere, specifically Python, as it is more popular and advantageous than R.

For learning material, aspirants can choose what they find best. Some may find YouTube video is best and some may find online course is convenient and some may love learning from textbook. Whatever the knowledge source you choose to learn don’t forgot to practice it. And when we code there is most of the time, we get errors. So don’t be afraid of “I am getting lot of errors, I can’t do data science”.  Errors in code are like something that we learn something new from it. when it comes to programming, there is no absorbing of concepts without getting errors and debugging them.

After choosing a beginner level introductory course or textbook can start learning along with a good programming practice. there are lot of free platforms where you can start your learning.

For getting into data science, there is nothing like finishing studying a concept. Data science enthusiasts will be learning throughout their lives. Thus, it is imperative that while learning programming and modeling problems, mathematical and statistical skills must be studied concurrently and brushed upon frequently. Statistical concepts form the backbone of machine learning models.

Hence, it is clear that one must keep learning with determination and start applying their learning on different platforms. Like Kaggle platform provide dataset and python kernel where you can start your programming and find interesting insights. Taking up one project and working on it will boost your confidence and your career. Projects can also be a good feedback tool because they give an immense understanding of the steps we go wrong and improve.

Self-learning does not mean that you to learn and follow everything by yourself. You can google if you don’t understand anything and try to look at the different solutions provided by experience data scientist try to understand it. It will boost your confidence and motivated. And if you are learning solo try to discuss data science concept with your friend who is in the field. Discussing things will harden your basic theoretical knowledge.

Start doing projects in team.as you do more projects you will learn more from it. Because new projects give you new problem and new errors etc. working in team will give you another experience. experience of different people, time management etc.so never stop learning.

I will be sharing some platforms where you can practice your learning and also you will get certificates. this will be advantage on your resume.

Programming practice:

1)   W3school

2)    Kaggle

3)    Hackerrank

4)    DataCamp

5)   freeCodecamp

Platforms:

1)   jupyter notebook

2)   google colab

Conclusion:

This article does not preach a standard methodology or a set of rules for getting started with data science. Instead, it acts as a simple guide for an absolute beginner who is an aspiring data scientist, clueless about where to head and what to do. Starting learning can seem challenging; however, practice and staying motivated is vital in getting into this vast field. After getting in touch with the basics, relevant internships can also be excellent opportunities for upskilling and expertise. Never stopping learning is the key.

Happy learning!!!