Preface

In today’s data-driven era, data analytics has become an indispensable core capability across all sectors. From commercial decision-making to scientific researches to financial forecasting to medical science and healthcare, insights derived from data are remolding our outlooks on the world. Facing the vast sea of data and more complex analytical requirements, conventional data processing tools become unable to live up to users’ expectations, making programming more and more a necessary skill for data analysts.

1. Why data analytics needs programming?

Programming offers unprecedented flexibility and powerful ability to data analysts. Compared with the ready-for-use analytical applications, programming can:

• Process big data: When data amount exceeds the processing limit of a tool, such as Excel, programming becomes the only choice.
• Achieve complex analytics: Programming is the way of achieving custom algorithms and complex data conversions.
• Automate process: Programming is used to automate repeated operations, greatly increasing analytical efficiency.
• Integrate & Scaleup: Programming allows seamlessly integrating data analytics into a larger system or work process.

2. Why choose SPL instead of Python?

Now Python is undoubtedly the most popular language for data analytics and programming. It provides a great number of libraries and has extensive community support. Yet we choose SPL (Structured Process Language) for this analytics course because it:

• Is intended for data analytic: SPL is a language specifically designed for structured data processing, featuring intuitive and succinct syntax. It is particularly well-suited for data analytics scenarios, avoiding the complexity of general-purpose programming languages like Python.
• Has gentle learning curve: For programming beginners, SPL is easier to start then Python, enabling programmers to more quickly focus on data analytics rather than being tangled in programming details.
• Has large performance capacity: In processing structured big data, SPL’s built-in parallel processing mechanism, cursor operation and other techniques usually have higher efficiency than Python.
• Offers peripheral tools: SPL also offers SPL WIN, which uses transaction interface, and SPL XLL, which helps Excel with the work. After becoming familiar with SPL programming, you can make better use of these peripheral tools for data analytics.

We’ll start from the basics to guide you through the entire SPL skill set for data analytics. From beginners who do not have any programming experience to data analysts who want to expand their toolkits, all can acquire practical knowledge and skills. We not only take an interest in technical implementations but also emphasize the nurturing of data analytic thinking, which will help users to extract insights from the vast ocean of data.

Let’s begin a data analytics programming journey to unlock the value and opportunities hidden within data!

Contents

1. Preparation
2. Some simplest data analytics use cases
3. Grouping & aggregation
4. …