Dealing with big data is ever growing and wide in scope. Why is it the case? It is wide and ever-growing because it has a consistent and ever-increasing component, structure and changing environment.

Whether you aim at becoming a big data scientist or you are already practicing, you need to understand that what it takes to learn big data is in stages or phases.

Before you progress into the stages, it is expedient to know the following;

Several programing languages are available for big data analysis. But, among the various programming languages, Python, R and Java are the top choices used in data science technology. An interesting part of the gist is that, once you begin and progress to intermediate and to Pro stages, you’ll realize that you can win most big data battles and problems.

Train with an Identified Big Data tech tool

If you have not heard of Hadoop before, this is an opportunity for you to explore as it is one of the oldest platform for big data. About 90 per cent of most big data scientists begin their data science career journey with Hadoop. Also, with big data, Hadoop is often your first step to learning big data before advancement to other platforms such as Cassandra and so on.

Although you can learn with other platforms like Spark and Cassandra, it is always advisable to begin your Big data journey with Hadoop before getting used to others.

Have an understanding of other Stremas

Don’t forget; Big data is a multidisciplinary. It requires that you are on your toes at all times. You wouldn’t want to be relaxed while several other software, computational tools, and concepts are there to learn. In an increasing software discipline, learning is tantamount to training. Hence, you’ll keep learning.

“Learning big data is simple yet tasking but if you can dedicate yourself, it is interesting.” There are several available online sources or platforms that can aid your learning journey of Big data.


This is one of the top analytics platform affiliated to University of California. Courseara takes you into big data in the areas of business, computer science and data science in general.  As a big data beginner, it offers you a range of topics that covers basics of big data, integration, modeling and machine learning algorithms etc.


This is another trusted big data platform that teaches you programming language useful for you to learn about big data.


This is an interesting platform like others. UPGRAD is recent and carefully growing up to becoming big in the big data world.


Powered by Microsoft, EDX can take you through your big data career focusing on R and Python programming languages.


This is also a slowly growing platform that equips you with projects and exercises that prepares you for Big data analytics career.

These are highly suggestive platforms where you can hone your skill in big data analytics. However, some criteria are important for you to consider when selecting these platforms. Pay attention to the following categories while selecting platforms for you to begin your big data training.

First, know the availability of the teachers. If teachers are not available to keep up with your learning goals, there is no point registering on the platform. Else, your time would be wasted at the end of the day.

Second, know the types or kinds of assignments and projects on the topics they offer. For example, projects and assignments are made clear to you on EDX, UDACITY, COURSEARA and many others. You should evaluate each platform on that.

Third, you should evaluate your chosen platform on their syllabus updates. Check if their teaching syllabuses are directed towards current trends.

Fourth, find out about their study scope and sequence of topics. A typical training session should have a directed topic structure that follows with your learning goals. This is important for you to know when selecting your big data learning platform.

Five, ensure you find out that there are supporting course materials to aid your learning goals. Platforms are to provide dataset for you to practice with, and that should be different from your assignments and projects which solutions must be provided to.

While many platforms afford you the opportunity to know and learn big data, it is highly suggestive for you to compare different platforms on what they have to offer and even take courses that are available in a platform but you find unavailable in other platforms. This will make your big data knowledge robust and with that, you can tackle any big data challenge.

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