Data Science Methodology

Go to Course: https://www.coursera.org/learn/data-science-methodology

Introduction

### Course Review and Recommendation: Data Science Methodology on Coursera If you’re looking to build or enhance your skills in data science, the **Data Science Methodology** course offered on Coursera is an exceptional choice. This course not only provides foundational knowledge for aspiring data scientists but also equips them with practical methodologies to tackle real-world data challenges. Here’s a detailed overview of the course, what you can expect, and why I highly recommend it. #### Course Overview The **Data Science Methodology** course serves as an excellent entry point into the world of data science. It emphasizes the importance of thinking and working like a successful data scientist by exploring two notable methodologies: the Foundational Data Science Methodology and the widely recognized CRISP-DM (Cross-Industry Standard Process for Data Mining). By the end of this course, you will understand how to methodically approach various data science scenarios, making it easier to apply your skills in real-world situations. #### Syllabus Breakdown The course is structured into comprehensive modules, each designed to develop specific skills and understanding. 1. **From Problem to Approach and From Requirements to Collection**: - **What You Learn**: Understanding the significance of problem definition, business understanding, and analytic approach is vital for any data science project. This module helps you identify the crucial steps needed to define the data requirements effectively. - **Hands-On Experience**: Engaging hands-on labs allow you to practice assessment criteria for data content quality and manage data gaps effectively. 2. **From Understanding to Preparation and From Modeling to Evaluation**: - **What You Learn**: This module dives deeper into data preparation, cleaning, and modeling techniques. You will understand how to handle missing or misleading data and what makes a robust analytical process. - **Practical Labs**: The hands-on labs are key, reinforcing the theoretical concepts by allowing you to apply data preparation and modeling techniques in a controlled environment. 3. **From Deployment to Feedback and Final Evaluation**: - **What You Learn**: You will explore how to deploy data models and the iterative feedback process necessary for refining solutions. Understanding stakeholder involvement is also emphasized. - **Project Work**: You’ll work on real business problems involving datasets related to emails, hospitals, or credit cards, which solidifies learning through practical applications. 4. **Final Project and Assessment**: - **What You Learn**: The culmination of the course involves applying the CRISP-DM methodology to a business problem of your choice. This is a comprehensive assessment of your learning. - **Peer Evaluation**: The interactive component of evaluating a peer’s assignment not only aids in reinforcing your understanding but provides insight into different approaches to the same problem. #### Why I Recommend This Course 1. **Structured Learning**: The methodical breakdown of content, paired with hands-on labs, creates a solid learning framework beneficial for both beginners and those looking to refine their skills. 2. **Real-World Relevance**: The focus on applying learned methodologies to solve actual business problems ensures that you not only learn theory but can also implement it practically. 3. **Engaging Curriculum**: With interactive elements like peer reviews and practical labs, you can actively engage with the material, leading to a deeper understanding and retention of concepts. 4. **Flexible Learning**: Coursera's platform allows you to learn at your own pace, making it easy to fit studies into your busy schedule. 5. **Networking Opportunities**: Collaborating with peers brings a wealth of shared knowledge and varied perspectives, enhancing the overall learning experience. In conclusion, if you’re serious about a career in data science, the **Data Science Methodology** course on Coursera is highly valuable. It not only prepares you to solve complex data challenges but also establishes a strong foundation in methodologies that govern the field. Take the leap; your future self will thank you for the investment in your education.

Syllabus

From Problem to Approach and From Requirements to Collection

In this module, you will discover what makes data science interesting, learn what a data science methodology is, and why data scientists need a data science methodology. Next, you’ll gain more in-depth knowledge of the first two data science methodology stages: Business Understanding and Analytic Approach. You’ll discover how to identify considerations and steps needed to define the data requirements for decision tree classification during the Data Requirements stage. Next, learn about the processes and techniques data scientists use to assess data content, quality, and initial insights and how data scientists manage data gaps. Round out this week with practical hands-on experience learning how to approach the Business Understanding and the Analytic Approach stage tasks and the Data Requirements and Collection stage tasks for any data science problem.

In this module, you will learn what data scientists do when their tasks and goals are to understand, prepare, and clean the data. You’ll examine the purposes, characteristics, and goals of the data modeling process. You’ll also explore how to prepare a data set by handling missing, invalid, or misleading data. Then check out the hands-on labs where you can gain experience completing tasks relevant to the Data Understanding, Data Preparation, and Modeling and Evaluation stages. You’ll be able to apply the skills you learn to future data science problems.

When you complete this module, you’ll be able to describe the deployment and feedback stages of the data science methodology. You’ll learn how to assess a data model’s performance, impact, and readiness. You’ll be able to identify the stakeholders who usually contribute to model refinement. You’ll also be able to explain why deployment and feedback should be an iterative process. To complete your hands-on lab experience, you’ll devise a business problem to solve using data related to email, hospitals, or credit cards. You’ll demonstrate your understanding of data science methodology by applying it to a given problem. You’ll construct responses that address each phase of the CRISP-DM based on a chosen business problem. After submitting your work, you’ll evaluate your peers’ final projects and provide constructive ideas and suggestions that fellow learners can apply right away.

Before completing your final project, learn how CRISP-DM data science methodology compares to John Rollins’ foundational data science methodology. Then, apply what you learned to complete a peer-graded assignment using CRISP-DM data science methodology to solve a business problem you define. You'll first take on both the client and data scientist role and describe how you would apply CRISP-DM data science methodology to solve the business problem. Then, take on the role of a data scientist and apply your knowledge of CRISP-DM data methodology stages to describe how you would solve the business problem. After you submit your assignment, you'll grade the assignment of one peer who is enrolled in this session. Let's get started!