We may anticipate that big data will continue to be a part of our world as it gets more digitized. The importance of big data and analytics will only grow over the next years. This new role can be perfect for you if you decide to pursue a career in the field of big data and analytics.
Data scientists often earn $116,000 year, which is a highly acceptable wage. Even entry-level workers may expect great pay, with an average annual salary of $92,000. As more companies see the advantages in hiring big data and analytics specialists, demand for their services will only increase. There is a scarcity of experts in the industry, according to over 80% of data scientists.
In what ways should people be educated?
In the field of data science, PhD or master’s degree holders make up 92% of the workforce. Just 8% of people have bachelor’s degrees, as opposed to 44% who have master’s degrees and 48% who have doctorates. So it stands to reason that people who want to grow in their employment and have the best chance of having a long, successful career with outstanding pay will strive toward attaining a higher degree of education.
Some of the most popular credentials among experts in this field include the Certified Analytics Professional (CAP) certification, the EMC Data Science Associate (EMCDSA) certification, the SAS Certified Predictive Modeler certification, and the Cloudera Certified Professional: Data Scientist certification (CCP-DS). These certificates are given based on the information and abilities that have been proven to be particularly adept.
Now is a great moment to enter the sector because many of the hired data scientists have held their employment for less four years. This is due to the lack of sufficient time for this relatively new sector to develop any norms. Currently, investing in the big data and analytics arena is like buying a property on the first floor of a fast expanding IT business.
Performs a Variety of Functions
Many professionals in the workforce today do overlapping tasks. They could act as researchers charged with extracting knowledge from the company’s data. They could also contribute to organizational management. This is the professional workforce, to the tune of 40%, to be exact. Innovator and developer positions may be fulfilling for certain people. The more roles a person can play, the more valuable they are to the team.
Flexibility in one’s work ethic has its advantages as well. Although 41% of positions in data science are now in the technology industry, its significance is also being recognized in other fields. The industries of marketing, management consultancy, healthcare, finance, government, and gaming fall under this heading.
Add More Skills
People who want to make themselves more marketable to potential employers in the big data and analytics industry might think about enrolling in extra courses in the area. Here are just a few possible lines of research:
- Hadoop and MapReduce
- Data Engineering
- Real-Time Processing
- NoSQL Databases
- GTA Support
- Excel
- Data Science with R
- Data Science with SAS
- Data Science with Python
- Data Visualization – Tableau
- AI and Machine Learning
- CloudLabs for R and Python
It is crucial to keep a competitive edge through continual education in cutting-edge technology if you want to be a vital member of any team. You may demonstrate your initiative and drive in this way, and any employer you work for will find you to be a valuable asset.
Keep up with the Changes
Analytics and big data are topics that are quickly developing. The field will evolve and advance along with technology. It should be a top priority for anyone who is serious about improving their career in the quickly changing field of big data and analytics to stay current on any changes that could have an effect on their line of work.
Analyzing a lot of data and working with it might be a fulfilling professional path.
Analysis Methods for Big Data
Big Data analysis often falls into one of four categories:
1. Analytical Description
This organizes historical data into a way that is clear. When generating reports regarding the operations and finances of your business, you may use this information. For tallying data from social media networks, it is also helpful.
Example: The Dow Chemical Company improved the distribution of its office and laboratory space by using historical data analysis. Dow employed descriptive analytics to identify unoccupied spaces. The business was able to save about $4 million annually by combining its office space.
2. Statistical Analysis for Diagnosis
This is done so that the issue’s cause may be found and fixed. Drill-down, data mining, and data recovery are just a few examples of these techniques. Businesses utilize diagnostic analytics because they may use them to identify root causes of problems.
Example of Use: Despite more consumers adding things to their shopping carts, a report published by an online store claims that sales have decreased. It’s possible that the form didn’t load properly, that shipping would be too expensive, or that there wouldn’t be enough payment alternatives. The problem can be clarified using diagnostic analytics.
3. Analytic Predictions
With the help of this kind of analytics, forecasts for the future may be formed utilizing data from the past and the present. Predictive analytics uses data mining, artificial intelligence, and machine learning to make predictions about the future. You may predict shifts in customer behavior, market movements, and more by using it.
Utilization Case: What steps must be taken to protect their clients from fraudulent transactions is decided by PayPal. The company builds a model that predicts possible fraudulent transactions using predictive analytics, which gathers data about previous transactions and user behavior.
4. The Use of Predictive Analytics
This kind of analytics will let you know exactly what to do if you have an issue. Perspective analytics may be useful for both descriptive and predictive analytics. Usually, AI and ML are employed.
Utilization Case: One of the main uses of prescriptive analytics is to maximize profits for airlines. This method of data analysis allows for the creation of an algorithm that automatically alters flight pricing in response to variations in demand, weather, destination, peak travel periods, and oil prices.
