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In the last few years, big data has become central to the tech landscape. You can consider big data as a collection of massive and complex datasets that are difficult to store and process utilizing traditional database management tools and traditional data processing applications. The key challenges include capturing, storing, managing, analyzing, and visualization of that data.

A significant number of newcomers in data science tend to spend a huge amount of time to develop theoretical knowledge and earn certifications only. While theoretical knowledge is certainly required to become a good data science professional, recruiters don’t put much emphasis on certifications only. Instead, they tend to evaluate the potential of a candidate by going through his/her work.

Being a data analyst would mean you’ll have several skill-sets that one needs to work in the domain of data science. However, it doesn’t mean you can easily jump from your data analyst career into the role of a data scientist. Before discussing if being a data analyst could act as a step to becoming a data scientist, let’s take a look at what each of these professionals do.

From businesses and government institutions to non-profit organizations, there is a seemingly-infinite quantity of data that can needs to be sorted and interpreted to get information that can be applied for a wide array of purposes. With “big data” being the buzz word these days, pursuing a data scientist career has become one of the hottest trends of modern times. Whether you take up a full-time data science course or opt for a data science bootcamp in Silicon Valley, you’ll need to known certain trending technologies (as mentioned below) to make a mark in this field:

Today, a huge number of businesses across the globe are striving to make sense of the massive amount of disparate data captured by them through various means. With the help of data scientists, they can transform this inordinate volume of data into actionable insights which can influence their operations heavily. And that’s probably the biggest reason the data scientist career is considered one of the most attractive in the tech field. While data science leaves a profound impact on the tech field, it has also started transforming almost all other sectors – from telecommunications and retail to agriculture and health, just to name a few.

Though having a computer science background or a degree would help you become a data scientist, it’s not a must-have thing if you’ve got your eyes set on a career in data science. According to DJ Patil (who became the first chief data scientist of the US in 2015), data science doesn’t care about if you’ve got a degree or what you majored in. The thing that really matters is what you do with data.

Data mining refers to the process where a large amount of data is analyzed to extract new and hidden information from it, which can then be used to boost business efficiency. In other words, you can say data mining searches for valid, hidden, and potentially useful patterns in large data sets. Since data mining needs multi-disciplinary skills, you’ll have to use statistics, machine learning, AI (artificial intelligence), and database technology. Since data mining helps you discover previously unknown/unsuspected relationships amongst the data, you can use the insights gathered from it for scientific discovery, sales and marketing, fraud detection, etc.

Data science is an extremely dynamic field where a significant number of aspects keep on changing on a regular basis and we can expect them to bring even more value in the upcoming future. Despite the omnipresence of data science professionals in almost all business sectors these days, the field is still in its nascent stage. While it’s true that the extensive use of this field is currently limited to a few fields, over the next few years, we can expect it to start powering a lot of fields in a similar way. In this post, we’re going to explain why data science is being considered the future of the tech field.

In today’s business landscape, data science has become almost ubiquitous and is steadily gaining control
over the tech field. With a massive amount of data being generated on a regular basis, a huge number of
organizations are trying to leverage the power of data science. With the help of data scientists,
businesses can come up with effective solutions for their problems and predict present and future
trends, which directly lead to success. But as with any other field, data science also comes with its fair
share of difficulties.