Python + Sql + Tableau: Integrating Python, Sql, And Tableau

Python、SQL 和 Tableau 是数据科学领域使用最广泛的三种工具。Python 是领先的编程语言；SQL 是最广泛使用的与数据库系统通信的手段；Tableau 是数据可视化的首选解决方案；简单地说——SQL 帮助我们存储和操作我们正在处理的数据，Python 允许我们编写代码和执行计算，然后 Tableau 实现了漂亮的数据可视化。

在这三个支柱上进行深思熟虑的集成可以每年在报告人员方面为企业节省数百万美元。因此，不用说，雇主在发布数据科学家和商业智能时正在寻找 Python、SQL 和 Tableau分析师职位描述。不仅如此，他们还想找到一个知道如何同时使用这三种工具的候选人。这就是重复性数据分析任务可以自动化的方式。因此，在本课程中，我们将教您如何集成 Python、SQL 和 Tableau。

一项基本技能，可以让你比其他候选人更有优势。事实上，区分你的工作简历并获得面试通知的最佳方法是获得其他候选人缺乏的相关技能。而且因为，我们准备了一个其他地方没有讨论过的主题，所以您将掌握一项真正有可能使您的个人资料与众不同的技能。许多人知道如何用 Python 编写一些代码。其他人使用 SQL 和 Tableau 来然而，很少有人能够看到全貌并集成 Python、SQL 和 Tableau 以提供整体解决方案。在不久的将来，大多数企业将通过实施您将在本课程中看到的技术来自动化他们的报告和业务分析任务。

如果您最终成为自动化此类任务的人，这对于您未来在公司或顾问的职业生涯将是无价的。我们在一家大型全球公司的经验告诉我们，具有这些技能的顾问可以收取四位数的费用每小时的金额。该公司很乐意支付这笔钱，因为从长远来看，最终产品会带来显着的效率。本课程首先介绍了软件集成这一概念。

我们将讨论一些重要的术语，例如服务器、客户端、请求和响应。此外，您将了解数据连接、API 和端点。然后，我们将继续介绍本课程围绕的真实示例练习——“工作缺勤”数据集。

接下来的预处理部分将让您了解 BI 和数据科学在现实生活中的工作情况。这一点非常重要，因为数据科学家的大量工作包括预处理，但许多学习材料都忽略了这一点，然后我们将继续对我们的数据应用一些机器学习。

您将学习如何从机器学习的角度探索手头的问题、如何创建目标、这部分练习需要什么样的统计预处理、如何训练机器学习模型以及如何对其进行测试。一个真正全面的 ML 练习。连接 Python 和 SQL 不是立即的。我们已经在课程的整个部分展示了这是如何完成的。

在该部分结束时，您将能够将数据从 Jupyter 传输到 Workbench。最后，正如所承诺的，Tableau 将使我们能够可视化我们一直在使用的数据。我们将准备几张富有洞察力的图表，并将结果一起解读。如您所见，这是一次真正全面的数据科学练习。没有必要三思而后行。如果您现在参加本课程，您将获得宝贵的技能，这些技能将帮助您从其他竞争工作的候选人中脱颖而出。

See the full picture: Learn how to combine the three most important tools in data science: Python, SQL, and Tableau

Last updated 6/2021
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.80 GB | Duration: 5h 15m

What you’ll learn

How to use Python, SQL, and Tableau together
Software integration
Data preprocessing techniques
Apply machine learning
Create a module for later use of the ML model
Connect Python and SQL to transfer data from Jupyter to Workbench
Visualize data in Tableau
Analysis and interpretation of the exercise outputs in Jupyter and Tableau

Requirements

Basic coding skills in Python
Basic knowledge of SQL
Basic ability to use Tableau for data visualization

Description

Python, SQL, and Tableau are three of the most widely used tools in the world of data science. Python is the leading programming language;SQL is the most widely used means for communication with database systems;Tableau is the preferred solution for data visualization;To put it simply – SQL helps us store and manipulate the data we are working with, Python allows us to write code and perform calculations, and then Tableau enables beautiful data visualization.

A well-thought-out integration stepping on these three pillars could save a business millions of dollars annually in terms of reporting personnel.Therefore, it goes without saying that employers are looking for Python, SQL, and Tableau when posting Data Scientist and Business Intelligence Analyst job descriptions. Not only that, but they would want to find a candidate who knows how to use these three tools simultaneously. This is how recurring data analysis tasks can be automated.So, in this course we will to teach you how to integrate Python, SQL, and Tableau.

An essential skill that would give you an edge over other candidates. In fact, the best way to differentiate your job resume and get called for interviews is to acquire relevant skills other candidates lack. And because, we have prepared a topic that hasn’t been addressed elsewhere, you will be picking up a skill that truly has the potential to differentiate your profile.Many people know how to write some code in Python.Others use SQL and Tableau to a certain extent.Very few, however, are able to see the full picture and integrate Python, SQL, and Tableau providing a holistic solution. In the near future, most businesses will automate their reporting and business analysis tasks by implementing the techniques you will see in this course.

It would be invaluable for your future career at a corporation or as a consultant, if you end up being the person automating such tasks.Our experience in one of the large global companies showed us that a consultant with these skills could charge a four-figure amount per hour. And the company was happy to pay that money because the end-product led to significant efficiencies in the long run.The course starts off by introducing software integration as a concept.

We will discuss some important terms such as servers, clients, requests, and responses. Moreover, you will learn about data connectivity, APIs, and endpoints.Then, we will continue by introducing the real-life example exercise the course is centered around – the ‘Absenteeism at Work’ dataset.

The preprocessing part that follows will give you a taste of how BI and data science look like in real-life on the job situations. This is extremely important because a significant amount of a data scientist’s work consists in preprocessing, but many learning materials omit that Then we would continue by applying some Machine Learning on our data.

You will learn how to explore the problem at hand from a machine learning perspective, how to create targets, what kind of statistical preprocessing is necessary for this part of the exercise, how to train a Machine Learning model, and how to test it. A truly comprehensive ML exercise.Connecting Python and SQL is not immediate. We have shown how that’s done in an entire section of the course.

By the end of that section, you will be able to transfer data from Jupyter to Workbench.And finally, as promised, Tableau will allow us to visualize the data we have been working with. We will prepare several insightful charts and will interpret the results together.As you can see, this is a truly comprehensive data science exercise. There is no need to think twice. If you take this course now, you will acquire invaluable skills that will help you stand out from the rest of the candidates competing for a job.

Also, we are happy to offer a 30-day unconditional no-questions-asked-money-back-in-full guarantee that you will enjoy the course.So, let’s do this! The only regret you will have is that you didn’t find this course sooner!