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Why Learning Data Science Live is Better than Self-Paced Learning

Why Learning Data Science Live is Better than Self-Paced Learning

Introduction

As more businesses look to data-driven technologies like automation and AI, the need for talented workers who can interpret the data is only expected to rise. In fact, IBM predicts that the demand for data scientists will soar 28% by 2020. Due to high demand and lucrative offers, many people are already shifting their career choice towards data science, hence data science live classes are becoming popular.

Are you among those preparing for data science jobs? If you are, you might have also faced a dilemma of either go for self-learning or take up a live course online. In this blog, we will be talking on differences between self-learning and live courses. Furthermore, we will also be looking at why taking a course can be a wiser choice.

Learning Methodologies

With the advent of advanced communicational technology and more diverse content consumption media, training and learning have become easier than ever. Practical training has experienced perhaps the greatest leap forward

Data science learning methods
  1. Self Paced

    With self-paced learning you can make your own decisions instead of completing a specific data science course within a certain amount of time, learners are able to learn concepts within the time that is needed for them. Each participant can decide what time is needed to complete a given course.
    Advantages can be like no time pressure, no need for a schedule, suitable to people with different learning styles

  2. Instructor-Led Online Courses

    This type of training is facilitated by an instructor either online or in a classroom setting. Instructor-led training allows for learners and instructors or facilitators to interact and discuss the training material, either individually or in a group setting. Online instructor-led training is known as virtual instructor-led training or VILT.
    Advantages can be easier to adapt, more social and easier to enforce capabilities

You can also have a look at topmost skills to become a data scientist here

Challenges in learning data science

Data science is not an easy nut to crack. It is difficult to master regardless of your professional experience. You may face a lot of difficulties while learning data science when starting your career. Let us list some reasons and understand why actually data science is a little tougher to learn

3 difficulties in learning data science
  1. A vast content to learn

    Data science consists of a large number of concepts and fields. Few examples can be problem definition to data fetching, cleaning, featuring, modeling, visualization and what not. It is impossible to learn all the things at once. This case gets even trickier when you realize that all the separate fields are important and can not be omitted. To be a good data scientist, one has to learn all the things like maths and statistics, machine learning, programming languages, communication, and analytical skills etc. Becoming a data scientist is not a short-term process. It demands years of efforts and diligence.

  2. Rapidly expanding and dynamic field

    Data science is relatively new and a major chunk of it is still under research. There have been advancements happening in data science all around the globe. With such fast advancements, it is difficult to catch up to the latest concepts in data sciences. Algorithms are evolving, visualizations are getting smarter, the problem-solving methodology is also under constant development. There are high chances of other frameworks or techniques rolling out by the time you finish with one. Hence, it is a little difficult to learn data science and catch up with all the advancements in short duration. Although basics are nowhere to go, you will need much more than that to tackle real-world problems.

  3. Research Acumen

    Data sciences demand research acumen from learners. Data science is more about researching new techniques and methodologies to enhance solutions for real-world problems. Learners should not only be skilled at using available techniques but should also be able to creatively suggest new ones. Furthermore, these tasks get more complex when you are still in a learning stage. Only be able to use available techniques will make you a business analyst rather than a data scientist.

Why Data Science live learning?

Learning data science comes along with its own set of challenges. It becomes practically very difficult to handle above-mentioned challenges alongside learning. A self-paced approach is a little less capable of handling the high learning curve in data science. What do we do then? We have to look at instructor based mode of learning. In this section, we will be looking at reasons why we should go for an instructor based online learning for data science

why instructor led data science training is better
  1. Easy to maintain motivation

    One problem with the self-paced learning program is keeping the motivation high throughout the learning. Learners tend to burn out eventually and it happens even faster when learning is exponential. Motivation is the prime fuel which powers self-paced learning. Without motivation, self-paced learning cannot happen. This the reason why you need an instructor based method. It keeps your schedule and learning under constant check. Furthermore, it also reduces the chances of lagging behind in terms of your pre-defined targets.

  2. Doubts clearance

    One of the most important reasons to opt for the instructor-led method is getting the daily doubts cleared. Getting doubts cleared on time is important for higher learning rate. If doubts are not cleared, you won’t be able to move ahead even with all your study material.

  3. Career guidance and assistance

    Apart from regular learning schedules, you can also get career advice and guidance from your instructors. Data science opportunity is the only reason that you are learning data science in the first place. With industry experience and contacts, instructors can impart valuable guidance to the learners about their career.

    You can also look at data science interview questions here.

  4. Knowing what to learn

    Keeping the motivation to learn is one thing and knowing what to learn is other. Data science is a very vast field. One just can not learn all of it at once because it is too big to explore. Instructor-led programs help learners identify the correct topics to study and in the right order. Instructor-led data science programs can help learners focus more on basics and getting them right. Learning advanced topics early can lead to a terrible mixture a bland basics curry and undercooked advanced concepts.

    You can have a look at our comprehensive data science course and syllabus here

  5. Real-time projects

    Data science projects offer you a promising way to kick-start your career in this field. Not only do you get to learn data science by applying it, but you also get projects to showcase on your CV! Nowadays, recruiters evaluate a candidate’s potential by his/her work and don’t put a lot of emphasis on certifications. It wouldn’t matter if you just tell them how much you know if you have nothing to show them! That’s where most people struggle and miss out. Instructor based data science courses provide you with the opportunity for hands-on industry projects. Learnings while doing a project are immense and can not be matched with any tutorial.

    Looking for some good data science projects? You might want to check them out here

  6. High Knowledge intake

    Instructor-led courses enable learners to digest concepts at a faster rate. Although a bit more effort has to be put in as compared to self-paced courses, results are much better. You end up learning more and in less time. This result is more important when you are learning something like data science as there is a lot to learn.

Conclusion

Both methods have distinct pros and cons, as well the purposes. In our experience, learners prefer self-paced training for basic conceptual learning and virtual instructor-led training for more advanced training. Since both serve somewhat different purposes, there is no clear winner between the methods. But when learning data science is concerned, instructor-based learning has an edge over self-paced learning. Owing to the vast knowledge in the data science field and continuous advancements happening, instructor based online learning is a good bet to place.