What is data science?
Data science is the study of combining programming, advanced analytics, artificial intelligence (AI), machine learning, mathematics, and statistics with subject matter expertise to reveal hidden actionable insights in any organization’s data. This requires a lot of strategic planning and decision-making capabilities.
Data science also involves the use of scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It involves the use of statistical and machine learning techniques to analyze large volumes of data and identify patterns, trends, and relationships. The insights generated from data science can be used to inform business decisions, optimize processes, and create predictive models for future outcomes.
Data is used for any organization’s growth and development which in turn benefits the economy of the country. Hence it’s correctly said, “Data is the new oil”.
Different Stages in Data Science
Capture: This is the first and very vital stage as this is the base of data science. In this stage, data is collected in raw structured or unstructured form. E.g.: When you order food from any food delivery app, you enter all your data like name, phone, location, food preferences, price of food, restaurants, etc. This data is stored in the app and is collected by the food delivery company for further processing.
Data storage and processing: In this stage, the raw data collected is cleaned and transformed in form that can be used. This includes data warehousing, data cleansing, data staging, data processing, transforming, and combining the data using ETL (extract, transform, load) jobs. This data preparation is essential for promoting data quality before loading into a data warehouse, data lake, or other repository.
Data analysis: In this stage, the data is analyzed to find some patterns, similarities, and connections between data that can be used for future business decision-making. Data analysis includes different types of analysis like
- descriptive analysis – E.g.: A food delivery app may record the number of orders per day. Descriptive analysis will reveal the spikes in order and when the orders are low and when the orders are very high.
- diagnostic analysis – E.g.: Conducting a more detailed analysis on why the orders are more on a particular day or month, this may lead to the finding that people order more on weekends or during the holiday season.
- predictive analysis – E.g.: the food delivery app might use data science to predict food order patterns for next month or next year by creating an algorithm that may look at past year’s data and take marketing actions early this year.
Communication: In the end, the processed and analyzed data is presented in form of reports or a data visualization format that is easy for the decision-makers to understand and take strategic decisions. This communication should be clear as the business analyst and other decision-makers should be able to understand and get the exact information from the processed data.
Importance of data science
Data Science involves understanding massive amounts of data from various sources and deriving valuable insights in order to make better data-driven decisions. Data Science is extensively used in diverse fields like marketing, healthcare, finance, banking, etc.
Data Science enables businesses to measure, track, and record performance metrics in order to improve enterprise-wide decision-making. Companies use trend analysis to make critical and strategic decisions that will improve customer engagement, company performance, and profitability. Using data science organizations can identify and refine the target audiences by combining existing data with additional data points to generate useful insights.
Applications of data science
Ride-sharing apps and food delivery apps collect users’ names, addresses, preferences, locations, etc. to provide better services in the future to the customers.
Data science is widely used in the financial sector for providing personal finance advice and fraud detection.
Data Science enables businesses to use social media content to gain real-time media content usage patterns. This allows companies to create content for specific target audiences, measure content performance, and recommend on-demand content. In the healthcare industry, physicians use Data Science to analyze data from wearable trackers in order to ensure the well-being of their patients and make critical decisions. Data Science also enables hospital administrators to reduce wait times and improve care.
Challenges faced in data science
Data preparation: Before using data for analysis, data scientists spend more than 70% of their time cleaning and preparing it to improve its quality – that is, to make it accurate and consistent. Most of them consider it to be the most time-consuming and mundane part of their jobs.
Data security: Cyberattacks are becoming more common as organizations migrate to cloud data management leading to confidential data becoming vulnerable. As a result of repeated cyberattacks, regulatory standards have evolved, extending the data consent and utilization processes, further aggravating data scientists.
Too many data sources: As organizations continue to use various apps and tools and generate data in various formats, data scientists will need to access more data sources in order to make meaningful decisions. This leads to manual data entry and time-consuming data searching, which results in errors and repetitions, and, ultimately, poor decisions.
One of the many challenges in data science is dealing with huge, messy, or incomplete data, which can result in inaccurate or biased results. Data scientists must consider the ethical way when working with data, by ensuring data privacy and avoiding bias in their models.
Difference between data analyst and data scientist
Data analytics is a subset of data science, though the terms are used interchangeably. Data science is a broad term that encompasses all aspects of data processing, from data collection to modeling to insights. Data analytics is primarily concerned with statistics, mathematics, and statistical analysis. It focuses solely on data analysis, whereas data science is concerned with the larger picture surrounding organizational data.
In most workplaces, data scientists and data analysts collaborate to achieve common business objectives. A data analyst may spend more time on routine analysis and reporting. A data scientist may design methods for storing, manipulating, and analyzing data.
Conclusion
Data science is a complex yet interesting process that can enable any organization to take some strategic decisions based on analyzed and processed data which can lead to the development of the organization.
The quality of data will be more refined and we will have access to this data as new and efficient technology is introduced.
