How should I start learning Data Science?
There is no doubt about the increasing demand for data scientists across the globe. Learning and having a career in data science will make you understand how promising and well-compensated the field is. In 2020, Glassdoor reported that the average data scientist in San Francisco makes over $140,000; which is $113,000 a year for data scientists in the United States.
Even if you do not wish to pursue data science as a career, learning the skills and improving data literacy can help you in your current career and can pay lucratively as well. Employees having good data skills can help their respective companies to be more in demand by becoming more data-driven.
Let us have a look at the steps to start learning data science:
Figure out what you need to learn:
Data science is considered one of the awe-inspiring fields. Many people may misguide you that you need to master machine learning, visualisation, programming, databases, calculus, statistics, linear algebra, deep learning, natural language processing, experimental design, clustering, and more to become a data scientist. That is not true at all.
Data science is nothing but the process of asking interesting questions and answering those using data algorithms. The workflow of data science processes as:
The workflow of Data Science
The workflow of data science does not require any mastery in any of the skills mentioned above, only the knowledge and skills of working on data using programming languages are good enough for a start. To become a data scientist, you may need high fluency in mathematics, but to start learning data science basic understanding of mathematics is enough. The skills mentioned above will surely help you to become a proficient data scientist at some point in your career but to begin your journey as a data scientist, mastering all those skills is not needed.
Get comfortable with Python:
For data science, Python and R are both great programming languages. Both languages support the data science workflow, though Python tends to be more prevalent in the industry, and R is more popular in the academic community.
Instead of learning both languages, you should focus on learning one language to get started. If you chose Python, you might need to install the Anaconda distribution as it simplifies the process of package installation and management on Windows, OSX, and Linux. Focus on mastering data types, data structures, conditional statements, comparisons, imports, functions, loops, and comprehensions, rather than focusing on becoming a language expert.
Learn data analysis, manipulation, visualisation, and storytelling:
To cut an edge, learning data analysis, manipulation, visualisation, and storytelling is essential. As a data scientist, most of your work will include data cleaning. Knowing a few algorithms well is much better than having an ‘average’ knowledge of multiple algorithms. Being well acquainted with linear regression, k-means clustering, and logistic regression will help you to finish the project efficiently and interpret the results effectively.
Learn and understand machine learning in more depth:
The most appealing part of data science is automatically extracting insights from the data or predicting the future using machine learning models. Machine learning is an integral part of data science because:
Since machine learning is a highly evolving and complex field, grasping its fundamentals and understanding how to do effective machine learning is very important. Before deciding on any machine learning algorithm, ask yourself the following questions to be sure about your models:
Keep learning and practicing:
It is easy to stop climbing a steep mountain like data science. But if you will stop climbing, you will never reach the peak. If you find yourself getting too comfortable with your skills, or you ever wish to learn and practice more, you can follow the tips given below:
Having a degree in data science, along with possessing the skills of the same will help you bag various lucrative jobs. Companies will hire you based on your skills and their requirements. When employers are looking at your resume for hiring, they will consider your degrees, your skills, and your portfolio. Also, joining any university won’t serve the purpose. You should look out for institutions that render quality learning along with a promise of making its students a leader.
A degree in Data Science from SRM University AP and LEARNXT
If you wish to begin learning data science, you can consider the elite courses in Data Science as offered by SRM University AP and LEARNXT in collaboration. You can get enrolled in MBA (Data Science) with specialisations in Business Analytics or Machine Learning and Artificial Intelligence, MSc (Applied Data Science), PG Diploma in Applied Data Science, and PG Diploma in Machine Learning and Artificial Intelligence. Having hands-on real-life projects and experiential learning will help you gain expertise in the field.
Though this article may not be exactly a road map for you, you can still consider it as a rough set of guidelines to follow as you learn data science. Don not feel constricted and try to develop data science expertise naturally. Do what keeps you motivated to learn.
Stay tuned to LEARNXT to know how you can start learning Data Science.