Post-Bootcamp Studies as a Software Engineer
Published: 2024-01-29 18:31:01

#Introduction

As a current Bootcamp student and aspiring Full-stack Developer, the question on my mind, as well as the minds of fellow cohorts, is quite simple. When we're done here, how do we land the ideal career? With that in mind, I've started to take a look at the field of AI integration and Development. What follows are a few overarching core concepts for breaking into the industry at an entry level, compiled from the perspective of a nascent software engineer.

Core Languages One Should Know (or at least be familiar with)
JavaScript is indispensable for web development, enabling both client-side and server-side applications through frameworks like React and Node.js. As it is considered a foundational language among most Development teams, it is not uncommon for technical interviews to involve a demonstration of proficiency in JS, if not the frameworks.

Python excels in simplicity and versatility, making it ideal for various applications, from web development to data science. Python has one of the most user-friendly data structures out there; it can be found in industries as varied as gaming to medical databasing, and everything in between.

An example of Python code used to fine-tune AI modeling:

```python
import tensorflow as tf
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.datasets import mnist

# Load dataset (for example, MNIST handwritten digits dataset)
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# Normalize the images
train_images = train_images / 255.0
test_images = test_images / 255.0

# Build the model
model = Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    Dense(128, activation='relu'),
    Dense(10, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Train the model
model.fit(train_images, train_labels, epochs=5)

# Evaluate the model
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)

print('\nTest accuracy:', test_acc)

```

Java and C# have and without a doubt will remain critical for enterprise and mobile app development. Particularly for devs working with legacy code found in the majority of long-standing code found in large companies.

An example of Spring Boot, a popular web building framework for Java:
```
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;

@SpringBootApplication
@RestController
public class ExampleApplication {

    public static void main(String[] args) {
        SpringApplication.run(ExampleApplication.class, args);
    }

    @GetMapping("/hello")
    public String hello(@RequestParam(value = "name", defaultValue = "World") String name) {
        return String.format("Hello %s!", name);
    }
}

```

##Directed Study Resources
Focused learning platforms such as Pluralsight and LinkedIn Learning are often quoted as prime learning resources with a broad, if somewhat general, scope. EdX and Coursera provide access to university courses that deepen foundational knowledge, giving you a bit more depth to your learning. As a resident of Colorado, I've also recently learned that Udemy is provided to anyone possessing a library card. Keep an eye on local resources and benefits!

Project-based learning is crucial to any developer. One of the first pieces of advice given to me was "don't stop coding." I've learned almost as much from making side projects which interested me.

Familiarity with agile methodologies and DevOps practices, including tools like Jenkins and Docker, is becoming increasingly important in modern development environments.

##Essential AI Readings
For those aiming to specialize in AI, reading and understanding the work already done by others is a must. A few books which are widely recommended are:

"Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig
"Pattern Recognition and Machine Learning" by Christopher M. Bishop
"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
For a broader perspective, especially on the implications and implementations of AI, "Weapons of Math Destruction" by Cathy O'Neil is a must-read. It takes on the challenge of highlighting the impact of widely used algorithms and the effect they have on society as a whole.