We live in a data-driven world. Everything that we consume or engage with is influenced by data. This assimilated data needs to be analyzed to provide insights. Hence, data science has witnessed significant growth today. No industry today has been left untouched by the assistance of Data Science. Concerning the accumulation of data, it is impossible for any industry to not be dependent on Data Science. Data Science and Artificial Intelligence have proven to be a gift to our fast-moving world. Many industries have implemented changes in the field of Data Science in order to grow their businesses. Hence, over the years, the spike in data integration has led to its technical growth, leading to a rise in demand for data scientists. Many educational institutions provide various data science courses, IT courses, and other courses in the same domain.
Through this, we can understand the way it has gained popularity in the IT domain. If you are intrigued to learn more about Data Science, let’s understand What is Data Science. And why has it boomed so much today?
What is Data Science?
Data Science is the study of data in order to extract meaningful insights. It is a mixture of statistics and mathematics which is driven by expertise in machine learning, artificial intelligence (AI), and computer science. Data Science is said to be responsible for accumulating, assimilating, and interpreting data in order to obtain valuable information which is further utilized for decision-making.
Importance of Data Science
Every business today, irrespective of how large or small has been highly influenced by the effects of Data Science. It is not only important to keep for businesses to work along with the parameter of Data Science but also necessary with respect to growth and profit making. Data Science has contributed greatly to the field of e-commerce, healthcare, human resources, and entrepreneurs to help with huge amounts of data.
History of Data Science
The journey of Data Science began in the 1960s and later the term Data Science was coined by William S. Cleveland in the year 2001. Data science was developed in response to the growing availability of enormous volumes of data and the necessity to interpret this data in order to derive insightful conclusions and make wise judgments. Data Science has existed for several years. But over the past decade, there has been immense growth in the field of Data Science as the world witnessed enormous technological and digital growth.
What is Data Science Process?
Data Science Process in short is a process through which the data is flowed step-by-step into different stages with its own task to perform to have a better overall understanding of the data. While different steps can be involved for different data science processes, here are a few which are more often used
Firstly, it is important to comprehend how is Data Science anyway helpful in problem identification of tasks. The first stage is to clearly understand the area of problem or issue that has been persisting and needs to be addressed. Problem identification is important with the scope of business growth and in order to give suitable solutions to the problem identified.
In this stage, the data is accumulated from different sources. It can be through numerous databases, APIs, even manual data entry, or web scraping. The data collected needs to be relative, comprehensive, and contribute to the problem at hand.
Data Cleaning and Processing:
As the data is collected from many sources, it is accumulated even in an unstructured way. Such raw unstructured raw data is often incomplete and consists of errors, incorrect values, and inconsistencies. In order to maintain the data quality, data cleaning, and processing is taken place. After such data is identified and processed, the data is further sent for analysis.
Exploratory Data Analytics (EDA):
Exploratory Data Analytics (EDA) is used to understand the characteristics of the Data. It analyzes data patterns and relationships between the data through the help of data visualization and statistical data analysis.
Feature Selection and Engineering:
Based on the data accumulated through EDA, relevant features of the data are used to build a predictive model. Feature engineering is the process of combining or transforming the existing features of the model.
This stage involves selecting appropriate machine learning algorithms to train the accumulated data. The data is trained with the aim to optimize the model with the help of learning modules and achieve the best performance result.
Appropriate performance metrics are used in order to understand the undertaking of the learning model in action and validate the techniques to evaluate how well the implementation of the data is going.
By model building and evaluation, once a model is created and if it fulfills the productivity parameter, it is deployed to make predictions on new data which is used in the future. The deployment is further implemented in the existing model to integrate the data.
Communication of Results:
The results obtained from the data analysis done through this process are further communicated to the stakeholders through reports, visualizations, or presentations. Based on the results, decision-making, and effective measures are taking place in order to mark the growth of the institution.
Monitoring and Maintenance:
After deployment, the data science process does not end, models need to be monitored on a daily basis in order to track their productivity and ensure their performance levels and make updates as needed. Even more or so, data science projects do often require maintenance and continuous upgradation for desired results.
Scope of Data Science in Today’s India:
No business or occupation has been left untouched by the dynamics of data science. Ranging from large multinational corporations to small emerging entrepreneurs, every profession is highly influenced by data. In today’s modern world, data serves as the most prominent tool for decision-making and the basis for a company’s growth.
India has witnessed a rapid rise in its tech industry in recent years. The meteoric piling of data in each sector today has made it necessary to cause digital and technical enhancement in the same domain. This growth has caused a demand for data science courses and data scientists who possess the skills to extract valuable data from large datasets. Each sector today is trying to build a robust data infrastructure with digital advancements and machine learning algorithms, further demanding skilled data scientists.
Educational Reach and High Job Opportunities
To meet the high demand for data scientists, India saw a swift increase in the number of educational programs for the same. Training institutions are offering various data science and artificial intelligence courses, IT courses, and data engineering courses. Educational programs are providing data science course certification as well as machine learning and AI certifications. These programs equip their students with the required skills and practical experience before they start in the same industry.
With the way data science and artificial intelligence have paved their paths in all sectors, the demand for skilled professionals outweighs its supply. Recruiters are actively looking for highly skilled and professional people to fit into this fast-moving position. Hence, highly competitive compensation packages are making data science an attractive career option for individuals with the necessary skills and knowledge.
Data is and will always be the first and best foundation for any business’s success. Driven by data, this field in India has emerged from the need for decision-making, innovation, and digital transformation. The higher job opportunities indicate a great demand for data science professionals in the country today. And this leading demand has paved the way for great learning opportunities for data science courses. I hope you found this blog the best to your interest and was of most help to you.