Learn Data Structures and Algorithms
The course of Data Structures and Algorithms (DSA) aims at teaching undergraduate students the knowledge of organizing data and estimating the running time of algorithms when they develop programs. Topics covered include representation and implementations of abstract data types, elementary algorithm analysis skills, lists, stacks, heaps, queues, hash tables, binary trees, and graphs, recursive techniques, and sorting algorithms. Learning the basic skills of DSA can take students from an introductory understanding of C programming language to a higher level and help them cultivate systematic programming discipline.
On this page, you’ll find some study resources such as lecture notes and related codes for a 3-credit course “BSAS202 Algorithm Design and Data Structure (in C language)” at the Department of Engineering Science of Macau University of Science and Technology.
Lecture Notes
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Assignments
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Recommended Resources
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{{< spoiler text="👉 Click to view the solution" >}}
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👉 Click to view the solution
Math
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Example math block:
{{< math >}}
$$
\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}
$$
{{< /math >}}
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$$\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}$$Example inline math {{< math >}}$\nabla F(\mathbf{x}_{n})${{< /math >}} renders as $\nabla F(\mathbf{x}_{n})$
.
Example multi-line math using the math linebreak (\\):
{{< math >}}
$$f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\
1-p_{0}^{*} & \text{if }k=0.\end{cases}$$
{{< /math >}}
renders as
$$ f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\ 1-p_{0}^{*} & \text{if }k=0.\end{cases} $$Code
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```python
import pandas as pd
data = pd.read_csv("data.csv")
data.head()
```
renders as
import pandas as pd
data = pd.read_csv("data.csv")
data.head()
Inline Images
{{< icon name="python" >}} Python
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Python