|
via Udemy |
Go to Course: https://www.udemy.com/course/data-science-in-action-using-python/
Certainly! Here's a comprehensive review and recommendation for the Coursera course based on the provided details: --- **Course Review and Recommendation: Data Science Methodology with Python on Coursera** **Overview:** This course is an excellent choice for individuals looking to develop a structured, hands-on understanding of data science and AI model development, especially in the context of big data. Unlike many generic data science courses, this program offers a practical, step-by-step methodology tailored for large-scale projects, modified from traditional CRISP-DM frameworks, making it highly relevant for tackling real-world challenges. **Key Features:** - **Real-Life Case Studies:** The course provides a practical approach by working through a real case study, enabling learners to translate theory into actionable skills. - **Hands-On Practice:** Through exercises focusing on data exploration, preparation, modeling, evaluation, deployment, and monitoring, learners gain valuable practical experience. - **Python Integration:** It introduces Python as the primary language for executing data science tasks, offering tutorials and exercises suitable for beginners and those with limited coding experience. - **Diverse Audience:** Whether you are a business analyst, project manager, or aspiring data scientist, the course is designed to be accessible, with additional options for clicker-style tools like Dataiku for non-coders. - **Modular Approach:** From environment setup to deploying models in production, the curriculum covers the entire lifecycle of data science projects. **Pros:** - Clear, structured methodology adapted for big data challenges. - Real-life case study enhances learning relevance. - Practical Python exercises build coding proficiency. - Suitable for various skill levels, including business analysts and project managers. - Emphasizes deployment and ongoing model monitoring, crucial for production-ready AI. **Cons:** - Requires some commitment to learn Python if you're new to programming. - Focus on big data may be overwhelming for absolute beginners without prior data science exposure. - The course's comprehensive nature might require significant time investment. **Who Should Enroll:** - Beginners eager to learn data science with a practical, guided approach. - Data professionals aiming to understand large-scale AI model deployment. - Business analysts or project managers seeking a methodology to guide their data projects. - Those interested in applying data science directly to real-world problems with minimal coding. **Final Recommendations:** If you are looking for a practical, methodology-driven course that teaches not just the how-to but also the strategic approach to handling big data projects, this course is highly recommended. Its combination of real-life case studies, hands-on exercises, and broad applicability makes it a valuable addition to your data science learning journey. Be prepared to invest time in learning Python, as it is central to many exercises, but rest assured that the skills gained will be highly applicable in professional settings. **In summary:** This course stands out for its comprehensive, practical perspective on data science tailored for big data scenarios. It bridges the gap between theoretical knowledge and real-world application, making it a smart choice for aspiring and practicing data scientists alike. ---
With explosive growth of data in unstructured data, we have ample opportunities to design, develop and deploy AI models. While there are many courses which teach you Data Science, you need a step-by-step guide on how to select a problem, explore data, develop & deploy models, and improve the model using user feedback and learning. This course covers many big data challenges and modifies CRISP-DM to deal with big data. This course provides you a methodology for AI model development and deployment as modified by us to deal with AI and big data. Our modifications have been tried on many real-life large-scale projects. We will select a real case study for this data science project and will provide hands-on experience in Designing / prototyping a Data science engagement on the chosen case study. You will be able to use the results in your day-to-day life.We divide the data scientists into clickers and coders. Clickers are those data scientists who use a data science tool with a user interface to provide a high-level specification. Examples include SPSS Modeler, Excel and Alteryx. In each case you can add formula, but do not need to write code. The second set of data scientists are those who use a procedural language with libraries to write code for data science work. Python is the most popular language among data scientists. The objective of this course is to get you an introductory coding experience in data science. If you are interested in a clicker course, we offer a course using Dataiku. In addition, our data science methodology course is also designed for Business Analysts and Project Managers with limited development background.Course starts with two critical activities· Set up Environment - step by step instructions in preparing sandbox environment for executing all your python code· Data Science Methodology - to review key steps, tasks and activities associated with our data science methodologyAfter above section, This course introduces our 7 step data science methodology and use Python to explain each step using our real life use case example. These 7 steps include· Step 1: Describe Use Case to explain selected use case for data science work· Step 2: Describe Data to describe Data Sources and explain data sets using Python as a language.· Step 3: Prepare Datasets to Prepare Data Sets using Python· Step 4: Develop Model will provide hands-on exercises in applying many AI modeling techniques on data sets such as time series analysis, classification, clustering, regression, and forecasting. All these exercises will be using Python as a language.· Step 5: Evaluate Model will provide measurements to Evaluate your AI Model Results· Step 6: Deploy Model will provide process for deploying your AI models.· Step 7: Monitor model will provide process for continuous monitoring and evaluating your models in productionIn this course, we will give you an opportunity to design a use case and then work on its implementation using Python as your primary language. You should download all data sets and sample python code. Complete all assignment in each section of the course and submit your final notebook using instructions provided.