Data Science

Understanding Data Science

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Data science is a multifaceted discipline that merges mathematics, statistics, advanced programming techniques, AI, and machine learning, all combined with domain expertise. Its primary goal? To unveil valuable insights hidden within an organization’s vast amounts of data. These insights then shape decision-making and strategic initiatives.

The exponential growth in the amount and sources of data marks data science as one of the rapidly expanding domains in every sector. Hence, it’s hardly surprising that Harvard Business Review described the data scientist’s role as the “21st century’s sexiest job”. Companies are now more than ever leaning on these professionals to decipher data and offer impactful suggestions to boost business results.

A typical data science services project undergoes several phases, including:

  1. Data Ingestion: This is the starting point, where structured and unstructured data is gathered from various sources. Collection techniques might range from manual input and web scraping to harnessing real-time data from gadgets and systems. Data could encompass structured information like client details or unstructured inputs such as images, social media interactions, and IoT data.
  2. Data Storage & Processing: Given the diverse formats and architectures of data, organizations must decide on appropriate storage solutions. Teams dedicated to data management lay down the rules for data storage and its configuration, streamlining the subsequent analytics, machine learning, and deep learning operations. Activities during this phase include cleaning, deduplicating, transforming, and amalgamating data, primarily using ETL processes. Proper data preparation ensures its quality, paving the way for its transfer to data lakes, warehouses, or similar storage sites.
  3. Data Analysis: In this phase, data scientists embark on an initial exploration of the data. They identify biases, detect patterns, and gauge the spread of values. Such an analytical journey helps frame hypotheses for A/B tests and determines if the data is fit for modeling, be it for predictive analytics, machine learning, or deep learning. As the reliability of a model gets ascertained, companies start depending on these insights for their business choices, leading to enhanced scalability.
  4. Communication: The final step is about translating the insights into digestible formats. This is achieved through reports, charts, and other visualization mediums, making it simpler for business experts and stakeholders to comprehend. While programming languages like R or Python have visualization capabilities, data scientists might also resort to specialized visualization tools.
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The terms “data science” and “business intelligence” (BI) might seem interchangeable given their connection to data analysis within an organization. However, their primary objectives differ.

Business intelligence (BI) broadly encompasses the technologies facilitating data preparation, mining, management, and visualization. Using BI tools, organizations can distill actionable insights from raw data, promoting data-driven decisions across diverse sectors. Though there’s a similarity with data science tools, BI is primarily retrospective, delving into historical data to depict what previously transpired. The insights derived are descriptive, helping organizations decode past events to shape future strategies. BI typically deals with static, structured data.

On the other hand, while data science also handles descriptive data, it usually extends to identifying predictive factors. These factors aid in classifying data or forecasting future trends. It’s crucial to note that data science and BI complement each other. Progressive organizations leverage both to maximize the potential of their data.

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