Explore about Data Science
Definitions
Data science is the practice of mining large data sets of raw data, both structured and unstructured, to identify patterns and extract actionable insight from them.
Simple data analysis can interpret data from a single source, or a limited amount of data. However, data science tools are critical to understanding big data and data from multiple sources in a meaningful way. A look at some of the specific data science applications in business illustrate this point and provide a compelling introduction to data science.
Role in the Industry
A data scientist is more likely to look ahead, predicting or forecasting as they look at data. The relationship between the data analyst and data is retrospective. A data analyst is more likely to focus on specific questions to answer digging into existing data sets that have already been processed for insights.
Many sectors can rely on data science for their businesses in the current era. Examples include healthcare, marketing, banking and finance, and policy work.
Bussines
Data science and analytics come together when data science is applied in a business setting. Data science helps businesses better understand the specific needs customers have based on existing data. For example, with customer age, purchase history, past browsing history, income, and other demographics, a data scientist can more effectively train models for search and product recommendation.
Finance
领英推荐
Data science is a powerful tool for fraud detection and prevention, honing the ability of financial institutions to recognize problematic patterns in data faster. Data science can also help reduce non-performing assets, revealing downward trends sooner.
Healthcare
Data science is revolutionizing healthcare through the utilization of data generated by wearables for health monitoring and prevention. McKinsey described a "big data revolution" in healthcare in 2018, suggesting that applying data science to the US healthcare system could potentially cut healthcare spending by $300 billion to $450 billion, constituting 12 to 17 percent of its total cost.
Marketing
Data science goes further than this kind of analysis. It can also predict future patterns, identifying actions that could meaningfully affect overall business strategy. For instance, data scientists can uncover optimal price points, bids for programmatic advertising or ways to generate new customers in the future based on trends in existing data.
A data scientist is more likely to look ahead, predicting or forecasting as they look at data. The relationship between the data analyst and data is retrospective. A data analyst is more likely to focus on specific questions to answer digging into existing data sets that have already been processed for insights.
After some explanation above, why Data Science was interested for me?
Firstly, Data Science remains highly sought after by industries, offering numerous opportunities for advancing my career in future employment.
Secondly, learning Data Science is essential for gaining a comprehensive understanding of exploratory and analytical data techniques, which are crucial for enhancing business processes.
Lastly, mastering Data Science enables me to make informed decisions as a key decision-maker within the company, ensuring the best choices are made for our business.