Data scientist is among the most desired career paths today. It's an exciting and highly paid career that presents you with tons of development opportunities. But the term data science remains confusing for many people even now. Many people outside of the profession are confused about how data scientists help businesses worldwide and why their services are so in demand.
Data science is an interdisciplinary field. Because data science is built on the foundations of computer science, business, statistics, and mathematics, it's difficult to draw a clear boundary between what belongs in the field and what doesn't. People have stated that data science and statistics are the same things. However, we would argue that data science is more comprehensive since it extends beyond statistics to cover more relevant topics for dealing with digital content and massive data. Another misunderstanding is that data science and Artificial Intelligence (AI) are synonymous. Though AI utilizes data science methods, AI and Data Science are not interchangeable terms.
Data science uses both structured and unstructured data. File formats like spreadsheets and CSV are used to store structured data. Spreadsheets containing transaction information are an example of such data. On the other hand, unstructured data is anything that isn't structured. Unstructured data may include images, video, and audio files.
It depends mostly on company size. Smaller firms hire only one or two data personnel to have end-to-end ownership of data analysis processes. However, due to the company's ability to afford more resources, there will be a greater degree of specialization working in larger organizations. Data collection, storage, data pre-processing, data organization, data visualization, creation of KPI dashboards, statistical inference, managing machine-learning models, experimentation, and A/B testing are the main activities that a data scientist can perform but may not do in a business environment. However, data scientists spend the most time forming a hypothesis, finding the necessary data, and cleaning it. Only a tiny fraction of the valuable hours are dedicated to performing analysis and interpreting the findings.
The first skill we need to talk about is scripting with programming languages like Python or R. Besides mathematics and statistical skills, data scientists require a sound knowledge of programming languages. Python continues to be one of the more widely used programming languages among data scientists. Besides Python, there are a few other programming languages such as R, SQL, and SAS, which currently share the attention in the community.
The second skill for data scientists using Python as the programming language is the scientist's command of Python libraries. Some popular data science libraries are pandas, NumPy, scikit-learn, and Matplotlib. For deep learning, a data scientist can use libraries like Tensorflow, Keras, Theano, and Pytorch.
Sound knowledge of GPU hardware and CUDA will give further advantage to a data scientist. Data science and deep learning models are getting complex every day. Machine learning techniques such as artificial neural networks and natural language processing, among others, are involved. They require a highly data-parallel architecture in which only a powerful machine other than a CPU can accomplish the computations. Data scientists use GPUs to accelerate these analytical applications.
The next skill is a deep understanding of algorithms. According to a 2020 survey done by 365 Data Science, around 71 percent of data scientists have been utilizing this method in their work. Besides logistic regression, other algorithms such as decision trees, convolutional neural, and feed-forward neural networks are also in demand for data science projects.
The fifth skill is developing an excellent comfort with cloud service providers. When data increases quickly in organizations, almost every enterprise moves their data on the cloud instead of on-premise solutions. Apart from languages and algorithms skillset, data scientists need to understand how an organization can store and process data on the cloud. The previous survey also reveals that 43% of data scientists work on Amazon Web Services (AWS) while 33% and 16% of data scientists use Google Cloud and Microsoft Azure, respectively.
Lastly, a strong command of visualization tools is also an important skill to have. Visualization plays an important role where a data analyst needs to show where an organization's data is leading. The popular visualization tool Tableau is preferred by many data scientists today. Besides Tableau, Microsoft BI is another preferred tool by data scientists.
Regardless of the method, the final goal of a data scientist is to make a meaningful contribution to the business and create value for it. There are two approaches to accomplish this. First, assist a company in making better selections regarding its consumers and staff. Netflix and YouTube can recommend the content you want to see next, and banks use data science methods for fraud detection. Optimizing existing operations is another critical approach for data science to offer value to a company. A logistics company, for example, would profit from a better delivery schedule, while a manufacturing company could utilize predictive analytics to identify maintenance needs and avoid production stoppages.
In today's, fast-paced world, industries rely on data to make business decisions. Data Science churns raw data into meaningful insights, and, a skilled data scientist will know how to dig out meaningful information with the data they come across. Businesses today need such experts that can make strong data-driven decisions, therefore, the role and importance of data scientists is increasing by the day.