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Processing Real-world Data Efficiently with Rust

Congratulations!

Reflect on your Rust learning journey and discover valuable resources and references to further develop your programming skills. This lesson guides you to additional materials, tutorials, and dependencies useful for advancing your proficiency with Rust in data processing.

We'll cover the following...

Some parting words

Before we part ways, we want to thank you for dedicating your time and effort to learning so many useful things in Rust. We know that our beloved language can be both tricky and counterintuitive, and we wish you all the best in your learning path and your working career.

Find more learning material

The Educative platform provides a wide selection of courses in Rust and many others in other languages and technologies.

We also invite you to take a look at our answers section, which contains short tutorials on different topics.

Useful references

Here is the actual Cargo.toml [dependencies] section used throughout this course. This includes all the crates used along with their versions and features.

Scheme
[dependencies]
anyhow = "1.0.45"
csv = "1.1.6"
dotenv = "0.15.0"
mongodb = "2.0.1"
rand = "0.8.4"
redis = "0.21.3"
reqwest = { version = "0.11.6", features = ["json", "blocking"] }
rusqlite = "0.26.1"
scraper = "0.12.0"
select = "0.5.0"
serde = { version = "1.0.130", features = ["derive"] }
serde_json = "1.0.68"
sqlx = { version = "0.5.9", features = ["mysql", "runtime-tokio-native-tls"] }
tokio = { version = "1", features = ["full"] }
ndarray-rand = "0.14.0"
ndarray-stats = "0.5.0"
noisy_float = "0.2.0"
poloto = " 3.11.1"
plotly = { version = "0.6.0", features = ["kaleido"] }
vega_lite_3 = "0.3.1"
warp = "0.3.2"
handlebars = "4.1.5"
ndarray = { version = "0.15", default-features = false, features = ["approx"] }
ndarray-linalg = { version = "0.14.1", optional = true }
smartcore = { version = "0.2.0", features=["ndarray-bindings"]}
polars = "0.17.0"
docx-rs = "0.3.2"
image = "0.23"
mnist = "0.5.0"
show-image = {version = "0.6", features = ["image"]}