Big Data seems to be governing the world at the moment. The tech revolution encountered unprecedented growth and this is the reason why the top in-demand skills that companies list for their interviews are entirely changed today. People who want to get hired in this industry should master SQL, BI (Business Intelligence), SAS analytics software, data analytics, data science and – very important – machine learning. The growing job market raised a few questions among specialists, as certain companies focused on some technologies only. This is how the fight between giants started. Some companies prefer data science, while others rely on machine learning. Other companies prefer using all technologies to obtain the best results. This article is meant to present the most popular technologies of today in detail, as well as a forecast regarding what’s going to happen in the next years.
The data analytics process explained
Data analytics refers to making decisions based on a complex process of analyzing data. It starts with gathering data and information, providing insight and finally making a decision. Of course, this requires human intervention. Data analytics shouldn’t be mistaken for data science. These are different fields that involve different steps. In some way, data science incorporates data analytics. Data analytics represents a series of steps that result in the automation of insights. These insights are gathered in datasheets that further help the decision-making process. In addition, data analytics involve other data handling procedures.
The most common industries that use data analytics are gaming, healthcare, and travel. The people who want or are required to work with data analytics must possess skills in programming, statistics, data intuiting and data visualization. The main technologies used in data analytics are data modeling software, diagraming, documentation and data profiling. Basically, everything is based on software, but the knowledge and skills of the employees will still matter in the long run.
The lifecycle of data science and what it involves
Data science is more complex, as it involves several subcategories that include some of the processes present in data analytics. In order to understand data science as a whole, it is recommended to start with data analytics, considering that data science is more of an umbrella term that encompasses more processes. Data science always begins with understanding the business and defining what objectives it has. These objectives must be tackled through data science in the end. The second step in the lifecycle of data science is mining. Scraping data can take a while if the right tools are not being used. Depending on each business’ preferences in terms of analyzing data, the needed software is used. Then, the gathered data must be cleared of inconsistencies or missing values.
Once the database is completed, the exploration step begins. This is where employees intervene by using data visualization tools. Once data was explored, the important features will be selected and consolidating, transforming it from raw data into engineered data. The lifecycle continues with predictive modeling, that completes the loop and sends you to machine learning. Thus, data science and machine learning are dependent on each other for pattern discovery, but there are other ways in which data science can be used without collaborating with machine learning. For instance, data science can use regression and supervised clustering. You can read here about this topic to completely understand the relationships between the two.
Diving deep into machine learning
Machine learning is a method that helps in discovering patterns in data. The interesting thing about machine learning is that it doesn’t have to be programmed to find these patterns because it is able to learn by experience, just like a human being. Machine learning algorithms can be separated into three different types. The first type is called supervised learning and it is based on logistic regression. Unsupervised learning is the second one and it is based on hierarchical clustering. Finally, reinforcement learning is based on the Markov decision process and Q learning.
Machine learning can be considered a part of a larger scheme that directs people’s attention to what was initially discussed in this article – Big Data. Because the large volume of data requires strong tools to keep it organized, more technologies have to gather together. Machine learning can be placed in the same category as data analytics and data mining. By combining these with diverse software tools, methods, algorithms and Big Data itself, the result is big data analytics. By merging these experimental and theoretical disciplines with the perspectives offered by machine learning and data science, the perfect tool for determining data patterns emerges.
According to several studies, the number of jobs requiring knowledge in fields such as data science or machine learning will rise tremendously. By 2020, around 2 million offerings for data professionals will be existent all around the globe. Data science will continue to develop at a fast pace, soon replacing the data scientist or not needing as much influence as it currently does. Yet this change won’t happen very soon. Deep learning should become more simplified, so data scientists that are not specialized in this area can work with this technology without issues. The hiring market will be strongly influenced by the advance of these technologies and their benefits will be known to all people. Data-driven decision-making will become a common, inescapable practice.
SAS still possess the gold in terms of analytics tools, but in a few years from now on it seems like Python is going to replace it. Some already consider SAS an outdated tool, and people with SAS skillsets only might be disadvantaged in such conditions. The open-source movement will become more and more visible, meaning that employers will start looking for professionals who expanded their knowledge in more areas. It’s important to state that data scientists won’t become obsolete because the advances that happen. Of course, some tasks will be automated, but there are still processes that need human intervention deeply.