Practical Data Science: Real-World Applications and Case Studies

Data science uses advanced analytics methods including machine learning modeling to make today’s computing processes automated and smart. These tools and algorithms help solve problems in a wide range of real-world application domains.

Educators are now designing and running large courses that implement hands-on practical training in the sort of computational work that Nolan and Temple Lang imagined would happen within statistics. These new courses involve students in the full data science process, from understanding the problem to procuring and analyzing data to creating visualizations.

Real-World Case Studies

A data science use case is a concrete real-world problem to be solved using data science techniques. It includes outlining a clear goal and expected outcomes, assessing available resources, determining feasibility, identifying potential risks, and selecting appropriate data sets. Visit for Data Science Training in Pune.

The technology used by data scientists is transforming the way businesses run their operations. It’s helping farmers optimize their crops and animal farms, making them more profitable while reducing the environmental impact of their activities.

Image classification is a popular data science project, and Garrick Chu (Springboard Data Science Career Track alum) used it to classify dogs based on their appearance for his final project. Similarly, Divya Parmar used sports and analytics to predict NFL offensive and defensive efficiency for her capstone project. She also applied machine learning to identify the most effective cancer treatment methods for patients.

Getting Started

Data Science is a multifaceted field with a wide range of applications and techniques. Whether you want to become a high-level expert or simply learn useful coding skills, this repo can help you get started.

Data Scientists use statistical and analytics methods, machine learning models, and visualization to process and interpret large volumes of data for strategic business decisions. Learn how to solve real-world business problems such as a hotel recommendation system, text emotion detection, telecom churn rate prediction, and more.

You’ll start by exploring how to gather and clean data, then move on to analyzing the data using statistical and machine learning models. Finally, you’ll practice visualizing and presenting the results of these analyses, particularly in the case of high-dimensional data. You’ll also learn to scale these analytical methods to big data settings, where multiple machines and distributed computation are used.

Data Collection

Data science is the process of gathering, measuring, and analyzing accurate data from various sources to find answers to research questions, evaluate outcomes, and forecast trends and probabilities. Accurate data collection requires careful planning and execution.

After collecting data, the next step is to analyze it using predictive statistical models. The goal is to produce predictions that improve real-world outcomes. This is accomplished through a number of methods, including statistical analysis, modeling techniques, and machine learning.

In this course, you learn to use the full suite of modern data science tools and techniques for analyzing SAP ERP data. You’ll get hands-on experience with open source DS/ML tools and the theory behind canonical modeling algorithms. You’ll also deploy a text classifier into a production environment and apply big data techniques for tuning model accuracy and human-in-the-loop verification.

Data Analysis

In data science, you must be able to manage the process that can transform hypotheses and data into predictive analytics models. This includes acquiring and managing the data, choosing and implementing the modeling technique, validating the results, and communicating the insights to other stakeholders.

Data analysis focuses on understanding and presenting data, particularly high-dimensional structured data. You’ll learn how to use a full suite of tools, including relational and time series methods, graph and network algorithms, free text processing and geographic information systems processes; statistical and machine learning techniques like linear and nonlinear regression, classification, unsupervised learning, and anomaly detection; and scale these analytical techniques to big data regimes involving distributed storage and computation.

You’ll also practice deploying and tuning an ML model with hyper-parameter tuning using Amazon SageMaker, and setting up a human-in-the-loop pipeline to correct misclassified predictions and generate new training data. You’ll finish the course with a real-world case study on online subscription business revenue optimization using a natural language processing model.

Data Visualization

Data science is the interdisciplinary process of managing the transformation of hypotheses and data into actionable predictions. These predictions can be about who will win an election, which products will sell well together, which loans will default, or which advertisements will be clicked on.

Visualization is a key part of this work, as it helps to understand and interpret these complex relationships in data. The human brain recognizes and processes images more rapidly than text, and visualization can be a powerful communication tool to help people make sense of large datasets.

In this course, you will learn the full set of skills necessary to complete a data science project from start to finish. You will practice data collection and preparation, exploratory data analysis, model building using machine learning and big data techniques, and production deployment.