Learn to Tackle Data Science Problems with Coursera's Data Science Methodology Program

Data Science Methodology by Coursera has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem-solving is relevant and properly manipulated to address the question at hand. If you are wondering which course is best for Data Scientists, this can be a good option. 

Accordingly, in this course, you will learn: 

• The major steps involved in tackling a data science problem.
• The major steps involved in practicing data science, from forming a concrete business or research problem, collecting and analyzing data, to building a model and understanding the feedback after model deployment.

Alex Aklson, Ph.D., one of the instructors of this course, is a Data Scientist in the Digital Business Group at International Business Machines (IBM) Canada. Alex has been intensively involved in many exciting data science projects, such as designing a smart system that could detect the onset of dementia in older adults using longitudinal trajectories of walking speed and home activity. 

It’s a self-paced, well-curated beginner-level course of not more than 8 hours and taught online by experienced faculty members. In this module, you will learn about why we are interested in data science. What is Data Science Methodology? and why do data scientists need a methodology? 

You will be taught about the data science methodology and its flowchart. In the course, you will learn about the first 2 stages of the data science methodology, namely Business Understanding and Analytic Approach. Finally, through a lab session, you will learn how to complete the Business Understanding and the Analytic Approach stages and the Data Requirements and Data Collection stages pertaining to any data science problem. 

The modules of this course are detail-oriented, comprehensive, and practical. It is designed in a way that in each class, the theory is connected with practical examples. The assessment method involves completing assignments and undertaking projects under various modules throughout the degree. These can be research-based or concept specific. 

Technically, this course can be fragmented into 3 prime modules:

Module 1: From Problem to Approach and from Requirements to Collection

Module 2: From Understanding to Preparation and from Modeling to Evaluation

Module 3: From Deployment to Feedback

Right now, I am an undergraduate involved in open-source live projects. This course has genuinely assisted me in attaining technical clarity regarding data science and boosted my confidence to make a lucrative career out of it in the future.