Is there anything wrong with not eating sugar? From morning tea to sweets. Sugar adds sweetness not only to our food, but also to our lives. Without this sugar, we would not be able to eat foods like sweets, chocolate, cakes or soft drinks. But where did this sugar come from and how did it reach our kitchen? Sugar and then brown sugar were first made in India from sugarcane juice. Historical books and various reports from India and abroad provide evidence of this. According to a research report titled ‘A History of Sugar: The Food Nobody Needs, But Everyone Craves’ published by the British media outlet network ‘The Conversation’, sugar was made in India around 2,500 years ago, i.e. 500 BC. From here, the technology to make it spread eastwards to China and reached the Middle East via Iran. In the first century, the historian Pliny the Elder, who wrote the encyclopedia ‘Naturalis Historia’, said that Indian sugar was better than sugar made in Arabia. It is said that sugar first reac...
IBM data science professional
IBM data science professional is a role within the field of data science that involves utilizing the tools and techniques of data science to solve complex business problems and drive decision making within an organization. The role typically involves working with large and complex datasets to uncover insights and trends, and using statistical and machine learning methods to develop predictive models and algorithms.
In order to be successful in this role, an IBM data science professional must have a strong background in mathematics, statistics, and computer science, as well as experience with programming languages such as Python and R. They must also have strong problem-solving skills and the ability to work effectively in a team environment.
The job of an IBM data science professional typically involves a wide range of tasks and responsibilities, including:
Developing and implementing data-driven solutions to complex business problems
Working with large and complex datasets to uncover insights and trends
Using statistical and machine learning methods to develop predictive models and algorithms
Collaborating with other teams, such as product managers and software developers, to integrate data-driven solutions into business processes
Communicating findings and results to stakeholders, both technical and non-technical
Keeping up-to-date with the latest advancements and technologies in the field of data science
Some specific examples of projects that an IBM data science professional might work on include:
Building a predictive model to forecast sales or customer behavior
Developing an algorithm to optimize supply chain operations
Creating a recommendation engine to personalize product or content suggestions for customers
Analyzing customer feedback data to identify trends and improve customer experience
Developing a fraud detection system to identify and prevent fraudulent transactions
Overall, the role of an IBM data science professional is to use the tools and techniques of data science to uncover insights and drive decision making within an organization. This requires a strong background in mathematics, statistics, and computer science, as well as the ability to work effectively in a team environment.
Python for a data science project
Python is a widely-used programming language in the field of data science. It is known for its simplicity and versatility, making it a popular choice for data scientists who want to quickly and easily develop powerful and effective data-driven solutions.
One of the key strengths of Python is its rich ecosystem of libraries and frameworks specifically designed for data science. These libraries, such as NumPy and pandas, provide powerful tools for working with large and complex datasets, performing advanced mathematical operations, and developing predictive models and algorithms.
In a data science project, Python can be used for a wide range of tasks, including:
Cleaning and preprocessing data: Python's libraries and frameworks make it easy to import, manipulate, and transform large and complex datasets, allowing data scientists to prepare the data for further analysis.
Exploring and visualizing data: Python's libraries and frameworks provide powerful tools for exploring and visualizing data, allowing data scientists to quickly and easily uncover insights and trends within their datasets.
Developing predictive models and algorithms: Python's libraries and frameworks provide a wide range of tools for developing predictive models and algorithms, including machine learning libraries like scikit-learn and TensorFlow.
Communicating results: Python's libraries and frameworks also provide tools for creating clear and compelling visualizations and reports, allowing data scientists to effectively communicate their findings to stakeholders.
Integrating with other systems: Python is a versatile language that can be easily integrated with other systems and technologies, making it a popular choice for data science projects that require collaboration with other teams or integration with existing systems.
Overall, Python is a powerful and versatile language that is widely-used in the field of data science. Its rich ecosystem of libraries and frameworks, combined with its simplicity and versatility, make it a popular choice for data scientists who want to quickly and easily develop effective data-driven solutions.
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