Internship Diary- Chapter 3

Understanding a little more about Data Science

In a world overflowing with data, data scientists are the modern-day detectives. In simple words, data science is like detective work with data. Imagine you have a big box filled with different kinds of puzzle pieces, and your job is to put them together to reveal a picture and solve the puzzle. Data science has lots of information called data that can be referred to as puzzle pieces. This data can come from different sources, like numbers, words, or pictures. We collect and organize this data for better understanding. After analyzing the data and finding answers to our questions, we create reports or visualizations to explain what we discovered. It's like putting together the puzzle pieces to reveal a beautiful picture that helps people understand things better.

In recent years, there has been a push to make data science more accessible to people with less technical expertise. One way this is achieved is by using graphical user interfaces (GUIs) or drag-and-drop interfaces. Instead of writing code to perform data cleaning or manipulation, users can simply select and configure the desired operations through the interface. Additionally, some platforms and libraries provide pre-built models and algorithms, which users can leverage without extensive coding knowledge. Furthermore, help users build models without needing to understand the intricacies of machine learning algorithms. These tools automate the process of selecting and tuning models, making it easier for non-experts to experiment with different approaches and find the best solution for their data.

Speaking about the model, it is like a set of rules or patterns that we derive from the data. It captures the relationships and patterns present in the data and allows us to generalize that knowledge to make predictions or gain insights about new or unseen data. Some common types of models used in data science include Regression, classification, clustering, and neural network. Once a model is built, it is trained using historical data, where we know the outcomes or the correct answers. The model learns from this data and adjusts its internal parameters to minimize errors or differences between predicted and actual outcomes. Once trained, the model can be used to make predictions or uncover insights from new data that it hasn't seen before.

For Example:

1.      A model could predict the likelihood of a patient developing diabetes or identify patients who are at high risk of readmission to the hospital.

2.      models are used to evaluate the creditworthiness of individuals applying for loans

3.      recommendation systems on e-commerce platforms use models to suggest products based on a customer's purchase history and preferences, increasing the likelihood of making relevant recommendations and improving customer satisfaction.

During my time at Tiger Analytics, I have gained insight into the various models employed by the company. One aspect that sets Tiger apart is its specialization in accelerators. These accelerators can be interpreted as Lego blocks, ready to be utilized as needed. Over the course of working with numerous clients, Tiger has developed a repository of codes, frameworks, and best practices that are widely adopted in the industry. This proves invaluable as it eliminates the need to start building solutions from scratch when clients present their unique challenges. Instead, Tiger Analytics accelerators serve as a solid foundation to build solutions upon it, expediting the process. These accelerators encompass Tiger Machine Learning, an automated machine learning tool that highly favors data scientists, Tiger NLP, showcasing successful AI and natural language processing cases for clients, Tiger blueprints, encompassing the best practices adhered to by Tiger, and much more. Models have always played a vital role in the field of data science, and the development of accelerators at Tiger has further enhanced their efficacy and efficiency.

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