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What You'll Learn
What is Data Science?
Data science is a multidisciplinary study that combines the methodologies and processes of advanced analytics, mathematics, statistics, machine learning, artificial intelligence, and computer engineering. This is done to analyze large unstructured and structured data and drive informed decision-making.
Data scientists can leverage the insights to explore data in-depth and answer several questions like what kinds of events happened, why they happened, what are the future possible scenarios, what can be done with the findings, and more.
Data Science is a field of applied mathematics and statistics that provides useful information based on large amounts of complex data or big data.
Simply put, data science helps organizations with:
Exploring Opportunities for Organizational Growth
Delving Into Customer Behavior, Choices, Intentions, and More.
Streamline Business Operations & Reduce Costs
Measuring Business
Performance
Understanding Exactly What Will Happen Next
Identifying Potential Threats & Steps to Mitigate Them
History of Data Science
The term data science is quite old. It has been relevant since the 1960s. Almost 50 years ago, John Tukey – an American mathematician and statistician, strongly suggested the need to renew academic statistics.
He emphasized the existence of a new, unrecognized discipline that would explore data to comprehend what it is trying to suggest. He defined it as ‘data analysis’ – laying the groundwork for the discipline of data science that followed.
Later, almost 10 to 20 years ago, John Chambers, Bill Cleveland, and Leo Breiman had their own different perspectives on how academic statistics is beyond the classical domain of theoretical statistics.
Chambers emphasized the importance of data preparation and presentation. Breiman emphasized prediction rather than interpretation. And Cleveland came up with the term “data science” to explain his theory.
Today, data science continues to grow as a discipline that leverages computer science and statistical methodology to generate accurate predictions, gain insights, and make smarter business decisions.
It might have started with statistics but now comprises concepts like Generative AI, Machine Learning, Internet of Things, Robotics, and many more.
The popularity of these concepts has created a demand for various job roles within the data science discipline. According to the Bureau of Labor Statistics, the employment of data scientists is predicted to grow 35% by 2032 – this is faster than the average for all occupations.
Discover what Data Means to AI and the Must-Know Steps & Tips for Data Preparation!
Lack of high-quality information is keeping you from making the most of AI trends and implementing your initiatives the right way. The solution? Data Preparation.Key Statistics About Data Science You Should Know
The global data science market will grow at a CAGR of 15.6% by 2027.
2022 data science stats reveal that around 149 zettabytes of data will be copied, captured, and curated in 2024.
Text data makes up 91% of the data utilized in data science
47% of organizations agree that data science has helped them gain a competitive advantage.
McKinsey claims that organizations that leverage advanced analytics are 23 times more likely to beat their competition in customer acquisition.
Data science platforms are predicted to reach a value of 322.9 USD billion by 2026. The global market size will grow at 27.7% CAGR.
Data Science for Business – Best Benefits
You have great volumes of data flowing into your organization. How do you make use of that data effectively? Enter data science.
1. Make Accurate Business Predictions
When you have high-quality, reliable, and accurate data, you know exactly what change, growth, or business improvement you want to look forward to. Data science can help you make this possible by leveraging technologies like machine learning, artificial intelligence, predictive modelling, pattern matching, generative AI, and more.
It makes it easier to work with historical data and generate accurate, trustworthy, and actionable analyses. With the help of predictive analysis, you analyze data that matters and analyze future events that will impact your business.
Here’s a retail use case example to understand this better:
Let’s say you are a retail business. You can leverage data science to analyze your historical sales data, latest social media trends, and more to forecast product demand for the upcoming season.
If you see a correlation between jacket sales and cold, chilly weather, you can proactively increase your jacket inventory in regions expecting or experiencing cold climate.
Social media trends can help determine potential demand and you can tweak your marketing strategies accordingly.
2. Enable Real-Time Intelligence
As a medium or large-sized company, it might become difficult at times to adjust quickly to changing marketing conditions in real-time. If you want to run a business profit, accurate data and speed are a must. Data science helps you achieve real-time data processing.
Data science when combined with robotic process automation helps you create computerized live intuitive dashboards and search and analyze data in real-time.
Result? You have fast access to relevant data from various sources, you are able to respond to changing market needs and make more informed and accurate business decisions.
3. Facilitate Data Security
Data security can make or break your business! According to a Statista report, around 8 million records were exposed in the fourth quarter of 2023. Another 2022 Annual Data Breach Report by Identity Theft Resource Centre suggests that there have 817 publicly reported data comprises in the United States. Data science helps secure private and sensitive data and protect your customers and organization from data breaches.
The discipline leverages machine learning tools and technologies to analyze both current and historical data and predict potential data threats. Machine learning algorithms can analyze large datasets, and you can train these algorithms to detect malicious activities.
4. Make Quicker Business Decisions
Poor decision-making can make your business fail. The U.S. Bureau of Labor Statistics claims that 20% of small businesses go bankrupt within one year, and more than 50% fail within five due to poor decision-making.
Data science can help prevent this and simplify the decision-making process. It helps you enable real-time data review and generate up-to-date business performance reports. This, in turn, enables you to derive actionable insights and make accurate and faster business forecasts.
Data Science for Generative AI – Why It Matters?
You have a lot of data with you. But is your data structured and organized? Is it trustworthy? Can you use your data to train large language models and identify meaningful patterns? If the answer is no – it’s time that you leverage data science! Your generative AI models and applications are built on large amounts of text and data science ensures that this information is relevant, organized, and of high-quality.
Data science techniques like natural language processing, image generation, and many others can help pre-process and interpret large data sets to create effective generative AI models. Also, due to its ability to secure sensitive information and enable end-to-end data governance, data science helps you unlock responsible AI.
Get Started – Use a Data Science Platform
A data science platform provides a scalable and integrated environment that streamlines the entire data science and analytics lifecycle. It can help you with different phases like data ideation, integration, model development, model training, model deployment, and more.
Modern data science platforms can also help you build, test, and deploy machine learning models in the cloud.
Leverage Databricks as Your Data Science Platform - LumenData can Take Care of Technology Implementation
Databricks Data Intelligence Platform helps businesses simplify their data science workflow – right from data preparation to sharing useful, actionable insights.LumenData is a trusted Databricks Consulting Partner and can help you build, deploy, and implement flexible data architectures and enable self-service analytics.
About LumenData
LumenData is a leading provider of Enterprise Data Management, Cloud, and Analytics solutions and helps businesses handle data silos, discover their potential, and prepare for end-to-end digital transformation. Founded in 2008, the company is headquartered in Santa Clara, California, with locations in India.
With 150+ Technical and Functional Consultants, LumenData forms strong client partnerships to drive high-quality outcomes. Their work across multiple industries and with prestigious clients like Versant Health, Boston Consulting Group, FDA, Department of Labor, Kroger, Nissan, Autodesk, Bayer, Bausch & Lomb, Citibank, Credit Suisse, Cummins, Gilead, HP, Nintendo, PC Connection, Starbucks, University of Colorado, Weight Watchers, KAO, HealthEdge, Amylyx, Brinks, Xylem, Clara Analytics, and Royal Caribbean Group, speaks to their capabilities.
For media inquiries, please contact: marketing@lumendata.com.
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