Python数据科学动手实践课程Data Science in Action using Python

随着非结构化数据中数据的爆炸性增长，我们有充足的机会设计，开发和部署AI模型。尽管有许多课程可以教您数据科学，但是您需要有关如何选择问题，探索数据，开发和部署模型以及使用用户反馈和学习来改进模型的逐步指南。本课程涵盖了许多大数据挑战，并修改了CRISP-DM以处理大数据。本课程为您提供了一种经过我们修改以处理AI和大数据的AI模型开发和部署的方法。我们的修改已在许多现实生活中的大型项目中进行了尝试。我们将为该数据科学项目选择一个真实的案例研究，并将提供有关所选案例研究的数据科学项目的设计/原型设计的动手经验。

我们将数据科学家分为点击者和编码者。答题器是那些使用带有用户界面的数据科学工具提供高级规范的数据科学家。示例包括SPSS Modeler，Excel和Alteryx。在每种情况下，您都可以添加公式，但无需编写代码。第二组数据科学家是那些使用带库的过程语言来编写数据科学工作代码的人。Python是数据科学家中最流行的语言。本课程的目的是为您提供数据科学的入门编码经验。如果您对Clicker课程感兴趣，我们提供一门使用Alteryx的课程，内容完全相同。此外，我们的数据科学方法论课程也是为具有有限开发背景的业务分析师和项目经理设计的。

Gain hands-on experience in building a Data Driven AI engagement using Python

Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 48.0 KHz
Language: English | VTT | Size: 3.2 GB | Duration: 7h 28m

What you’ll learn
Students will learn proven modified CRISP-DM methodology to deal with big data challenges as we move from BI world to AI world
Students will use real case study for this data science project and will gain 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 Examples include SPSS Modeler, Excel and Alteryx. This course is for coders not Clickers. For clickers we have designed similar courses like “Data Science Methodology” and “Data Science in Action using Alteryx”
This course is designed for data scientists and data engineers who use a procedural language with libraries to write code for data science work. This course uses Python to show all necessary steps and activities needed for successful data science engagement.

Description
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 and 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 a number of 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 Alteryx for exactly the same content. 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 methodology

After 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 production

In 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.

Who this course is for:
This course is for anyone interested in becoming a data scientist such as Students, Business Analysts, Developers Testing professionals
There are four possible careers where this course can be used as introductory material such as data scientists, AI or automation engineer, test engineers and finally knowledge engineers