The data science field has opened up a world of opportunities in various sectors as its application penetrated technically all fields including defense, healthcare and pharmaceuticals, banking and finance, retail, and others. There has been a massive increase in volumes of data being generated and consumed in recent times thanks to the rapid growth of devices connected to IoT and increased access to the internet. This data carries some valuable insight that takes data science to extract. For as long as data is being generated, data analytics remains necessary for data-driven decision making.
The popularity of Data Science
In the world today, data powers everything that we do, and businesses are becoming more and more data-driven. Large volumes of transactional, social, and IoT data are generated every single minute, and the need to draw insight and discover hidden patterns from this data makes data science indispensable. However, it is not just the need that has sparked off the demand for data science and Simplilearn’s data science certification course. Some growth drivers like the ones listed below are also playing a huge role in the growth of data science.
- The growth of big data – The massive growth of data has created an opportunity to derive insight from data and ultimately, the demand for data science services, techniques, software, and skills.
- Awareness that data science is critical. Because of the availability and access to data, businesses are becoming more aware of the importance of the insights that this data holds and the importance of data science as a field. As such, predictive analysis has grown to become the core driver of business operations.
- Advancement of big data technologies. Technologies like cloud computing, Hadoop, NoSQL database system, and other technologies are making it possible to acquire, store, and analyze big data thanks to their capacity to handle big data effectively.
- The growing need for data security and data protection. With the explosive growth of data from the various source, there is an equally huge concern about data security and protection. This has further created opportunities in the field beyond the usual advancements.
- High demand and low supply or data science skills. Data science roles have expanded due to the availability of computing technologies that manipulate big data. IBM indicated that the demand for data scientists has increased by 28% in 2024. Interestingly, there was increased demand by enterprises for in-house data scientists whose average salary was way above the national average, of $117,345 in 2024. Despite the many data scientists actively participating in the industry, there still is a great shortage of skilled professionals as the industry demands are dynamically expanding.
What is Data Science?
As businesses have grown to generate more and more data, they need data science to help analyze and discover hidden patterns and trends from it. Data science is a discipline that makes data useful. Data science as a practice involves the use of various scientific tools, systems, algorithms, and expertise to extract insight from both structured and unstructured data.
A field that emerged in 2001, data science has evolved to trends like graph analytics, Data fabric, Data Privacy by design, and augmented analytics.
Data Science process explained
The data science process can be recursive that requires one to refine each step to arrive at the relevant conclusion. It is important that you not only understand the client’s needs but also translate it into a concrete problem that will lead to business goals.
1. Framing the problem
In framing the problem, you go out of the way to understand the business, the context of operation, and get all the information to solve the problem raised. Your data analysis can only be considered successful if it provides solutions that a client needs.
This should involve knowing the business goals and establishing what problem exists that needs to be solved. At the point of initiating the project, translate the client’s challenges into actionable objectives. Having a good foundation for the project helps you to identify even the unspoken business issues. Prioritize the business needs to enable you to tailor your analysis to provide a very specific and relevant business solution.
2. Data collection and review
Where data is available, data scientists then decide on and sort the data that is useful for the project. If not, what more data do you need? From where and how do you obtain it? Keep in mind the available resources like time, money, and infrastructure to help you decide on how to obtain this data.
During the process of collecting data from various sources, it is vital to store it in a uniform format for ease of modeling. Be sure to check for unusual patterns or anomalies. Data may be in formats like excel, which can be transferred by Python or R. For this, technical skills in MySQL will count a great deal in processing the data.
3. Data processing
Also known as data wrangling, data processing involves preparing data to make it usable in the data analysis process. This involves fixing errors of data collection, filling in missing data values, removing invalid or duplicate data values, and performing other tasks to clean the data.
4. Data exploration, data modeling, and data analysis
The initial analysis, also referred to as data exploration, involves understanding information, looking for obvious correlations, patterns, and characteristics in a data set. At this point, a predictive data model is built that will be used to store data in a database. A data model is made up of objects and relationships between the objects that the data scientist is interested in tracking and analyzing.
Once this has been done, the data scientist then progresses to perform an in-depth analysis using tools and techniques like statistical modeling and algorithms to establish valuable insights and predictions.
5. Communication and visualization of analysis results
Some see communication as the most challenging part of a data scientist’s job as it involves presenting the technical findings in ways that make sense to internal users’ and external customers. Unless the stakeholders can comprehend and use technical findings to make decisions, these findings will be useless.
Data visualization involves presenting analysis findings using visual elements like pie charts, graphs, and maps. Some scientists will present the same information in several formats for different applications for instance in reports or websites. In their presentation, data scientists also use storytelling and at this point, it is very critical to have excellent communication skills.
Data scientists can enhance the results of data visualization with the help of data streaming for higher accuracy and real-time information. But what is data streaming? It is a process that publishes results as soon as any data gets fed into a system. Data scientists can use data streaming practices and incorporate them into their visualizing methods to achieve high-quality results for understanding business trends, market volatility, and other business needs.
Why should you learn Data Science?
We have already seen, there exist a high-demand and low supply for data science professionals in the field. At the rate that data science is growing, many opportunities are opening up in various sectors. As the ‘sexiest job of the 21st century’, data science is one of the few fields that accords professionals way too decent packages. But these come with having the right skills and in many cases, experience.
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Additionally, data drives the world. With how fast data is being generated, the world has come to appreciate the insight that this data carries. As a data scientist, you have the fulfilling opportunity to make a difference and promote data-driven initiatives in businesses in the various sectors.
Finally, data science does not limit you. Data science is applicable in most if not all sectors. Acquiring data science skills will not stop you from pursuing your interest, rather it will accelerate your pursuit.
5 Valuable Data Science skills
- Applied statistics and math
- communication and presentation
- programming languages including R and Python
- Machine learning
- Data wrangling and visualization
Conclusion
Data science roles like Data Scientists, Data Engineers, and machine learning Engineers are pursuable. The best way to learn data science is by doing. Additionally, consider enrolling in a data science course to take your career from level to level as training is a crucial part of any career. Learning data science will equip you with skills and give you added advantage of working in the industry of your choice. Whether you opt for the energy market to drive informed decision-making and efficiency in business processes or in the retail business to establish customer trends and behaviors, you will have applied your acquired skills.
