Big data analytics courses: A brief discussion on the curriculum and the benefits of online courses

Since the 2000s, India has been on a constant growth trajectory to position itself as a major economic powerhouse. The resilience of the Indian economy has been proven even during some of the major financial crises of the past two decades. The dot-com bubble was followed by the 2008 financial crisis and, most recently, by the COVID-19 pandemic. During these events, India and all the world’s economies went through a recession. What is surprising is how quickly the Indian economy stabilized after these events. Where some developed countries took years to get back on track, the Indian economy recovered within just a couple of years after each of those events. This shows the inherent strength of our economy, and it won’t be a stretch to say that the prospects are very promising.

An untapped potential

The Indian economy is primarily service-based. The lack of manufacturing capability can be attributed to a couple of things. Firstly, the bureaucratic hurdles made it very hard to run a business in India. Secondly, poor infrastructure placed Indian companies in a disadvantaged position compared with some of the major manufacturing powerhouses of the world. The recent push toward the “Make in  India” initiative has seen a significant reduction in bureaucratic hurdles and enthusiastic investment in infrastructure development. Very recently, the RBI came out with the data that every 1 rupee spent on infrastructure development will yield a return of 3.8 rupees within just a decade. Further solidifying the idea that India has amazing untapped potential in the manufacturing sector. 

Rise of data science and analytics

Data analytics have become a major part of business management. Seeing the potential for growth in the Indian economy, it becomes quite evident that the requirement for adept data analysts will rise to unforeseen levels. Making it a viable career choice for the next generation. Even working professionals can take up big data analytics courses in search of a better career path and more stability. Multiple institutes specialize in developing courses specifically catering to working professionals.

Data science and analytics courses

Data science is an amalgamation of various subjects such as mathematics, programming, artificial intelligence, and cloud computing. These subjects unite to form the symphony of modern data science and analytics. Big data analytics courses carefully construct a curriculum capable of giving the students a holistic view of the entire data science and analytics processes. Let’s briefly discuss these subjects.

Mathematics 

Mathematics is an integral part of data science. There is no data science without it. But one can be something other than a mathematical prodigy to become a great data scientist or analyst. The best data science or big data analyst courses have a carefully constructed curriculum that includes all the required mathematical concepts for data science. They need to learn some specific topics required for working as a data scientist. These topics are statistics, linear algebra, calculus, and probability theory.

Programming language  

Mathematics and programming come together to make modern data analysis a reality. A data scientist is responsible for developing data analysis algorithms that cater to the specific needs of their employer. This is why it is necessary to have a good grasp of programming. Most data science courses include Python in their curriculum. Even though there are multiple programming languages for developing analytical algorithms, Python has stayed at the top of the list of relevant programming languages. Mostly because it is easier to learn and has a vast library of resources developed by the incredible programming community. The best big data analytics courses emphasize teaching programming to every student. 

Data cleaning and structuring 

Data comes in all shapes and sizes. The one thing that is common in almost all data sets is inconsistencies. These inconsistencies are a major hurdle in finding accurate results from the analysis process. Therefore, analysts have to spend a chunk of their time removing the inconsistencies in the data sets. Big data analytics courses teach students about various tools and techniques used for cleaning and structuring data sets. 

Artificial intelligence  

Artificial intelligence has become necessary for undertaking more advanced forms of data analysis. There are mainly four types of data analysis. Descriptive analysis, diagnostic analysis, predictive analysis, and prescriptive analysis. Out of these four data analysis methods, two rely heavily on the implementation of artificial intelligence.

In predictive analysis, the goal is to predict the future with reasonable accuracy. Humans are terrible at predicting the future. Our minds cannot take in all the things that can affect future outcomes. But machine learning algorithms can predict the future with various degrees of accuracy. This is why data analysts develop machine learning algorithms that can take in large data sets and give accurate predictions. Predictive analysis is an amazing tool for businesses. It can help them prepare for possible adversities that may arise in the future. 

Prescriptive analysis is the most advanced form of data analysis. Data analysts use deep learning algorithms that can take large data sets and analyze them to find patterns and actionable insights.

Cloud computing  

Cloud computing plays a crucial role in modern data analysis. The availability of increasingly large data sets has meant that the only bottleneck an analyst faces today is computational power. The lack of computational power restricts analysts and forces them to work with considerably smaller data sets. Now, small data sets tend to give flawed insights. While increasing the data set yields better accuracy, Cloud computing solves the problem of developing and maintaining sophisticated computational hardware. A business can use cloud services for analyzing large data sets. This is why most big data analytics courses focus heavily on teaching students about various cloud computing services and the process of using these services efficiently.

Dashboard creation 

Data analysts must present their findings to the managing directors or relevant authorities. But how does one go about explaining complicated topics in an easily comprehensible manner? Well, the answer is simple. Using dashboard creation tools, an analyst can create an interactive dashboard that gets the idea through to the relevant authorities. Big data analytics courses emphasize teaching students the process of creating efficient dashboards.

Online data analytics courses

Online data analytics courses have seen an increase in demand over the years. Online courses extend the opportunity for upskilling to working professionals. Everyone knows that to stay relevant in a volatile job market, one must constantly upgrade themselves and acquire skills that will help them in their career paths.

Online courses are specifically designed with the needs of working professionals in mind. This is why these courses have classes outside working hours and focus heavily on an industry-aligned practical approach. Online courses are also cheaper when compared with regular courses. Online courses offer much-needed flexibility to working professionals. The modern work environment puts a great deal of stress on working professionals. Working hours have increased from 8 hours to more than 12 hours per day for some people today. The long and stressful working hours are terrible for professionals’ physical and mental well-being. Online courses allow professionals to take on their upskilling adventure while maintaining a healthy work-life balance. This is why online big data analytics courses should be a priority for professionals looking for big data analytics courses.