All About Data Science and Its Growing Importance for Businesses

Feb 25, 2019

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With the world entering the era of big data, the need for its storage has also increased. This was a cause of concern for businesses until 2010 and their focus was centered around building solutions and frameworks to store the huge data volumes. Today Hadoop and other frameworks have successfully solved the issue of storage and the focus of businesses has shifted to the processing of this data. Data Science is the secret sauce here. The future of artificial intelligence lies in Data Science. Therefore, it is very crucial to understand what is data science and how can it add premium value to your business.

Are you wondering how to identify order and patterns in data? Data science may hold the key to success. Get in touch with our data scientists to know how identifying patterns, as well as spotting trends, can help the bottom line of your business. 

What is Data Science?

Data Science is a blend of various algorithms, tools, and machine learning principles that help unveil hidden patterns from the raw data. Data Science is a more forward-looking approach that analyzes the past or current data and predicts the future outcome that further helps in making informed decisions. It has the potential to answer open-ended questions such as “what” and “how” scenarios. Data scientists need to master the full spectrum of the data science life cycle in order to uncover useful intelligence for their organizations. There is a need for a certain level of flexibility and understanding to maximize outcomes at each phase of the data science process.

Data science is an interdisciplinary field that deals with systems and processes, that are used to extract insights or knowledge from huge amounts of data. Data extracted can be structured or unstructured. The Data Science Process is a continuation of data analysis fields that includes predictive analysis, statistics, and data mining. Data science visualization is a vast field that uses a lot of techniques and theories that are a part of other fields like mathematics, information science, chemometrics, statics, and computer science. Some of the methods used in data science include:

  • Probability models
  • Machine learning
  • Signal processing
  • Data mining
  • Statistical learning
  • Database
  • Data engineering
  • Visualization
  • Pattern recognition and learning
  • Uncertainty modeling
  • Computer programming

With the ongoing technological advancements, many aspects of data science are gaining immense importance, especially big data. Data science is not just limited to big data, which in itself is a vast field because big data solutions are more focused on pre-processing and organizing the data rather than analyzing it.

Want to know how data-driven techniques and data analytics can help you solve business-related problems? Request for more information now.

Difference between Data Science and Machine Learning

Difference #1: Data science tools create insights from data dealing with all real-world complexities. This comprises tasks like extracting data and understanding requirements etc. Whereas, machine learning classifies or predicts outcome accurately for new data point by learning patterns from historical data using mathematical models.

Difference #2: In data analytics, most of the input data is generated as human consumable data which is to be analyzed or read by humans like images or tabular data whereas input data for machine learning are transformed specifically for algorithms used.

Difference #3: Data analytics and science require domain expertise, strong SQL skills, ETL, and data profiling skills whereas machine learning requires a strong understanding of mathematics, data wrangling with SQL model-specific visualization.

Quantzig’s analytics solutions offer clients the right big data technologies, tools, capabilities, and frameworks to handle the variety, volume, and velocity of data to help organizations make informed business decisions while ensuring information security and privacy. Request a free proposal to know more about our portfolio of services.

Benefits of Data Science

Benefit #1: Helps Understand Customers Efficiently

Data science tools help brands to understand and analyze their customers in a much empowered and enhanced manner. Customers are the base and soul of any brand and have a very important role to play in the success and failure of any brand. With the help of data visualization, brands can connect with their customers in a personalized manner. Furthermore, this can ensure better brand engagement and power.

Benefit #2: Improves the Brand’s Engagement with Customers

Data science is gaining huge attention because it enables brands to communicate their story in an engaging and effective manner. When companies and brands use this data in a comprehensive way, they can share their story more effectively with their target audience, thereby creating better brand engagement. After all, nothing better than human emotions can connect with consumers more effectively.

Benefit #3: Assists in Complex Problem Solving

Data science is a field that is constantly evolving and growing. With so many data science tools being developed, almost every day, big data is assisting organizations and brands to solve complex problems in human resources, IT, and resource management in a strategic manner. This means better use of both material and non-material resources.

Benefit #4: Findings of Data Science are Applicable to Every Industry

This is one of the most important aspects of data science that its results and findings are applicable to any sector such as healthcare, travel, and education. Understanding the true and appropriate implications of data science can help sectors in analyzing and addressing their challenges in an effective fashion.

Benefit #5: Aids in Mitigating Challenges of Demand and Sustainable Future

Data analytics focuses on the use of general methods without modifying its application, irrespective of the domain. This approach is quite different from traditional statistics that generally tend to focus on offering solutions specific to particular domains or sectors. Today, data analytics and science have far-reaching implications. With the aid of speech recognition, machine translation data science is helping almost every industry to mitigate challenges of demand as well as paving way for a sustainable future.

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