About This Course

In the modern era, making a career in data science is one of the most common dreams for people working in the tech space. We've designed this course to help realize that dream. In this course, we'll cover some queries related to data science.

Common queries covered in this course

This course covers the following topics:

Use cases

Now, we’ll see some common use cases of data science techniques. This will help us understand the power of data science and why it is one of the most demanding skills in today's world.

• Predictive modeling aims to make accurate projections about future events or trends by analyzing patterns or relationships in immense datasets, including fraud detection, network intrusion, medical anomaly, credit risk assessment, marketing campaign optimization, inventory forecasting, predictive maintenance, and predictive policing.

• Recommendation systems suggest items or actions to users based on their past selections, behaviors, or traits. They are commonly used in various industries, such as e-commerce, streaming platforms, social media, music streaming, news platforms, travel, online gaming, job portals, online education, and email marketing. The goal of recommendation systems is to provide personalized experiences to users by presenting them with appropriate and attractive items or suggestions.

• Natural language processing (NLP) is concerned with the interactions between computers and human (natural) languages that enable computers to analyze, understand, and generate human language. This includes text classification, named entity recognition, machine translation, speech recognition, text summarization, sentiment analysis, question answering, text generation, and dialogue systems.

• Clustering and segmentation are used to group similar objects into clusters and divide a larger group into smaller, more homogeneous segments. Both clustering and segmentation have a wide range of applications, including customer segmentation, market segmentation, image segmentation, text segmentation, product grouping, user behavior analysis, and gene expression analysis.

• Time series forecasting is the process of using historical time series data to make predictions about future values. Time series forecasting is widely used in various fields, including sales, finance, stocks, marketing, production, and weather forecasting.

• A/B testing involves the comparison of two versions of a product, component, or feature (A and B) to determine which one is more satisfactory or persuasive. It is typically used in website conversion rate optimization, email marketing, online advertising, app development, product development, pricing strategy, customer and content experience optimization, and marketing campaign optimization to achieve desirable results.