Data Science touches every aspect of our lives, from deciding where to vacation, predicting the weather or tracking sales trends. It encompasses a wide range of disciplines and uses many techniques, including data acquisition, preprocessing, machine learning algorithms, pattern evaluation and knowledge representation.
Cleveland’s taxonomy posits data science as a fully-fledged scientific discipline, elevated from “statistical methods” and “technology”. This distinction has four implications. Visit for Data Science Training in Pune.
1. Identifying the Problem
The Data Science Foundations post-baccalaureate certificate provides a strong base on which to advance one’s career. It includes introductory courses in computer science and statistics that develop an applied understanding of concepts from these disciplines.
Using an analytical framework, data scientists explore questions and identify meaningful analytics-driven solutions. The first step of the process is framing the problem. The framework offers a structured way to articulate the problem as a question, decide how to solve it and present that solution to stakeholders.
In this course you will learn the fundamental statistical theories and methods that form the backbone of Data Science. You will also learn how to apply these tools in a real-world setting through a combination of hands-on projects, lectures and quizzes. You will also gain insights into the different aspects of the Data Science life cycle, such as data acquisition and preprocessing, ML algorithms, pattern evaluation and knowledge representation. Moreover, you will be introduced to the Python programming language, which is a core tool in the Data Science domain.
2. Identifying the Data
Data science is a new field that combines statistics and computer science with an understanding of how to apply cutting-edge technology-boosted algorithms for business and social good. Harvard Business Review called it “the sexiest job of the 21st century,” and companies from start-ups to Fortune 500s are scrambling for talented data scientists.
To build a strong knowledge base, start by thinking about the purpose it will fill for your organization. This might include answering employee and customer questions, streamlining work processes, or providing support for specific data niches. Once you understand the need it will serve, research where your current response times are the longest and how many questions come in on a regular basis.
A major advantage of a knowledge base is that it allows an organization to store their expert understanding in a formal, machine-interpretable format. It also removes the need for passing this information amongst multiple people and helps reduce data siloes.
3. Identifying the Algorithm
Data science is a broad discipline that incorporates skills from many fields including math, statistics, computer programming and communication. The field is constantly evolving and new techniques are being invented. Consequently, students should be encouraged to explore and experiment with various methods as they gain experience.
Data visualization experts are skilled at designing and presenting information in online layouts, images, dashboards, and interactive features. They are also adept at translating technical and statistical analyses for multiple nontechnical audiences.
Data scientists who have strong programming skills are familiar with a variety of languages and algorithms. This knowledge includes searching (Linear and Binary Search), sorting algorithms (Breadth-First and Depth-First) and string pattern matching. They can also use a variety of data manipulation techniques to extract insights from raw data sets. This can include aggregation, summarization, data filtering, and model selection. In addition, they have a solid understanding of the foundations of mathematics and statistics, including data quality and provenance issues.
4. Identifying the Solution
Data science is often used in business to identify opportunities such as new products, features and services that can increase sales or customer satisfaction. This is a key use case for data science, but it’s also possible to find other ways to leverage the process, such as improving internal processes and eliminating wasteful activities.
Some studies focus on the knowledge domains and fields related to analytics & data science (Bowne-Anderson, 2018). This approach includes identifying the skills required by associating them with the tasks that data scientists perform.
These include data collection and cleaning, machine learning algorithms, pattern evaluation and knowledge representation, as well as statistical modeling. These skills are supported by the four key components of the analytics & data science knowledge model: technical, analytical, business and communication.