The conundrum of choice

In May 2017 The Economist published a remarkable article titled, "The world’s most valuable resource is no longer oil, but data " (https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data) and indeed the big burst of opportunities in the field of data science justifies the statement in itself. Big corporations are spending billions on their data science teams to gain an edge in this novel field

As a data science enthusiast who's about to begin his career in the field of data science, 𝐭𝐡𝐞 𝐬𝐢𝐧𝐠𝐥𝐞 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐭𝐡𝐚𝐭 𝐈 𝐟𝐚𝐜𝐞𝐝, 𝐰𝐚𝐬 𝐭𝐫𝐲𝐢𝐧𝐠 𝐭𝐨 𝐟𝐢𝐧𝐝 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐬𝐨𝐮𝐫𝐜𝐞 𝐭𝐨 𝐠𝐚𝐢𝐧 𝐭𝐡𝐞 𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞

Anyone associated with this field will be aware of the plethora of specializations, nano degrees, courses, blogs and other resources available online. Offering such courses at lofty prices has become a market in itself.

As a beginner, it becomes very overwhelming to choose the right path to begin with, and most of the times, it takes a lot of exploring to find the right source which leads to a lot of time wasted. Besides this, many of the relevant courses available are too costly for college students to afford.

From my personal experience, I feel that a mix of free blogs, books and following people from the community is the way to go forward, instead of taking a single course or a specialization or a set of multiple courses. Here are some of the reasons to justify the statement.

(1.) Data Science is a very deep and immersive field. A course or a specialization which needs to have a stipulated time period of completion simply cannot deep dive into individual topics.

(2.) Data Science like every novel technology is very dynamic. I have seen very few courses that are able to cope up with the ever-changing tools, libraries and frameworks and are able to stay up to date and relevant.

(3.) I personally feel some of the blogs are written by people who specialize in a particular tool or topic and hence are able to communicate their knowledge better as compared to most of the courses available which are taught by a single tutor.

(4.) The monetary factor of not being able to afford these courses.

Now that we know that reading blogs can help, there's actually a big downside to this: which ones to read among the millions.

This is the reason that I plan to start a series of posts where I'll share my favourite blogs, articles and books related to different topics of machine learning and deep learning like data preparation and processing, data visualization, feature selection, model selection and deployment, evaluation metrics among others.