Beginner's Guide to Coding and Data

Level: Beginner

Data science explained | Learning to code for data science beginners - Programming concepts playlist

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Understanding Data Science and Programming in Data Science

In this piece, we delve into the world of data science and how programming plays an integral role. We'll begin by examining the three major topics that will guide us toward a comprehensive understanding of data science.

Our roadmap for this discussion involves:

1. Defining what data science is
2. Exploring an example project focused on stock trading
3. Discussing the role of programming in data science

What is Data Science?

The first step in our journey is to establish what data science is. Data science is an interdisciplinary field focusing on the data lifecycle and its application in decision-making. We want to highlight two terms here:

• Interdisciplinary
• Data Lifecycle

Being interdisciplinary means that data science is a blend of various disciplines such as mathematics, computer science, and domain-specific fields. The domain could be anything from business to healthcare to astronomy, essentially any field that uses data.

The concept of the data lifecycle refers to how data evolves over time, from creation to eventual deletion. It's important to understand that data science has gained prominence due to technological innovations that have extended data lifecycles.

A Practical Example: Stock Trading Project

To see data science in action, let's consider an example: building a stock trading bot. This project allows us to understand that finance and trading are the domain fields for this exercise. We can divide the structure into four parts:

```let framework = [
"Data Source",
"Data Storage",
"Data Analytics",
"Decision"
]
```

The four elementsβData Source, Data Storage, Data Analytics, and Decisionβserve as the building blocks of most data science projects. These components work in tandem to turn raw data into actionable insights.

The Role of Programming

Now that we understand the framework, let's explore how much programming is involved at each step. Here, the level of difficulty can vary from simple data operations to complex AI algorithms.

• Data movement and transformation
• Querying and data retrieval
• Statistical and AI-based analytics
• Decision-making application design

Programming serves as one of the vital tools in the data science landscape, aiding in problem-solving and decision-making based on data. Whether you're a beginner or an expert, there's room for growth.

Ready to delve deeper into data science and programming? We recommend jumping into our data science programming playlist to build your skills and kickstart your projects.

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