Course objectives

After completing this course, students will be able to:

• • Write Python code, covering essential programming constructs like  data types, control flow, and functions.
    
  • Utilize Python libraries for  network data analysis and visualization, including cleaning, transforming, and presenting data.
    
  • Apply statistical methods and algorithms to identify patterns and trends in network data.
    
  • Automate repetitive tasks involving file systems, spreadsheets (Excel & CSV), and email communication.
    
  • Build robust and reusable Python scripts by incorporating  error handling and debugging techniques.
    
  • Gain a foundational understanding of the relationship between  Data Science and Artificial Intelligence.

Course outlines

• • Crush course of python programming language 
  • • Write the first program
    • Data Types and Variables
    • Type Conversion (Type Casting)
    • Getting Input from the User
    • Control Flow: Making Decisions and Repeating Actions
    • • Conditional Statements (if, elif, else) 
      • for loops
      • while loops
      • Loop Control Statements (break, continue)
    • Types of Operators
    • • Arithmetic Operators
      • Assignment Operators
      • Comparison (Relational) Operators
      • Logical Operators
      • Bitwise Operators
    • Data structure in python (list, Tuples sets and Dictionaries)
    • • Functions: Reusable Blocks of Code
      • Defining and Calling Functions
      • Arguments and Parameters (Default parameter values, Arbitrary arguments
      • Scope of Variables (Local vs. Global scope)
      • Lambda Functions (One-line functions)
    • Modules and Packages (Built-in Modules, Creating Your Own Modules)
  • Introduction to Network Data Analysis
  • • What is Network Data Analysis (NDA)
    • Dealing with open datasets
    • Tools used in Data Analysis
    • Data analytics applications
  • Data Analysis processdata analyst versus data scientist
  • • Who is a data analyst?
    • Difference between data analyst and data scientist
    • Steps involved in Data Analysis
  • Network Data Cleaning, Transformation and Visualization
  • • Normalizing the network data
    • Enriching, and structuring the network data to make it suitable for analysis.
    • Applying statistical methods, algorithms, and logic to identify patterns, trends, correlations, and anomalies.
    • This is using Python libraries like Pandas, NumPy, and Scapy.
    • Presenting the insights using graphical formats (charts, dashboards) to aid decision-making.
  • The most important libraries in python used in data analysis
  • • Numpy library
    • Pandas library
    • Matplotlib library
    • Seaborn Library
    • Dpkt library and Netmiko library
  • Numpy library (part 1)
  • • Advantages of NumPy array over pure python data structure
    • Create a 1D-Array
    • Create 2D-Array
    • Array indexing
    • Array slicing
    • Array shape and reshape
  • Numpy library (part 2)
  • • Data types in Numpy library
    • Array join and split
    • Array sort and array search
    • Matrix multiplication (matmul)
    • random numbers in Numpy
    • Generate random numbers and random arrays
  • Matplotlib and Seaborn Libraries (part 1)
  • • What is Matplotlib?
    • The key concepts in the design of matplotlib
    • Pyplot, plotting and markers in Matplotlib
    • linestyle, labels and grid in Matplotlib
    • Understand subplots
    • Visualize arrays with matplotlib
  • Matplotlib and Seaborn Libraries (part 2)
  • • Draw simple line Plots
    • Draw scatter Plots
    • Histograms, Binnings, and Density
    • Customizing Plot Legends
    • Seaborn Versus Matplotlib
  • Pandas library (part 1)
  • • What is Pandas? And why we use Pandas?
    • Pandas DataFrame and how to create one.
    • Reading and writing data to and from files by a Pandas Dataframe.
    • How to access, modify, add, sort, filter, and delete data.
  • Pandas library (part 2)
  • • computing some statistics using Pandas
    • use Pandas on real-world data what is the time-series data
    • How to work with time-series data
    • Making Changes to time-series data such as (add Additional Columns
  • Pandas library (part 3)
  • • Exporting and Importing Data combining DataFrames across rows or columns
    • Load Files Into a DataFrame
    • Data cleaning and removing duplicates
    • Pandas Objects (Histograms, Density Plot and Scatterplot)
    • Working With Missing Data
  • Network Data Analysis and visualization using Python
  • • Why we use Python in Network Data Analysis,
    • Data collection/acquisition.
    • Data cleaning and preprocessing.
    • Exploratory Data Analysis (EDA).
    • Modeling/Advanced analysis.
    • Interpretation and communication of results
    • How to work with text file format and reading Data from other sources
    • Using Pandas, NumPy and Matplotlib packages in the Network Data Analysis process
  • Artificial Intelligence and Data Science
  • • AI Programming Languages
    • The branches of Artificial Intelligence (Machine learning, Deep learning, expert system, etc)
    • Overview of the main branches of Artificial Intelligence
    • • Overview of Machine learning
      • Overview of Deep learning
      • Machine learning versus deep learning
    • Link between Data Science and Artificial Intelligence
    • • What is Data Science?
      • Future of Data Science and Artificial Intelligence
      • Key Similarities: Data Science and Artificial Intelligence
      • Key Differences: Data Science and Artificial Intelligence
  • Automating File System Operations
  • • The os and shutil Modules:
    • • Understanding file paths (absolute vs. relative).
      • Getting the current working directory
      • Changing directories
      • Creating and deleting folders
      • Listing files and folders
    • Copying, Moving, and Deleting Files:
    • • shutil.copy(), shutil.copytree().
      • shutil.move().
      • os.unlink() (for files)
      • os.rmdir() (for empty folders).
    • Reading and Writing Plain-Text Files:
    • • Opening files (open()).
      • Reading file content (read(), readline())
      • Writing to files (write(), writelines()).
    • Managing ZIP Files (zipfile module):
    • • Creating and adding files to ZIP archives.
      • Extracting files from ZIP archives
  • Automating Spreadsheet (Excel & CSV) with Python Using openpyxl for Advanced Excel Control: xlsxwriter for Rich Excel Reports:
  • • Opening and creating Excel workbooks.
    • Accessing worksheets.
    • Reading and Writing data from / to cells.
    • Manipulating rows and columns
    • Using the csv module for structured data.
    • Reading data with csv.reader.
    • Writing data with csv.writer
    • Saving modified Excel & CSV files.
    • Reading data from multiple sheets/files and consolidating.
    • Updating values in spreadsheets based on logic.
    • Modifying existing Excel files, formatting cells, adding formulas, conditional
  • Automating Email Communication smtplib, email modules
  • • Sending plain text and HTML emails (smtplib).
    • Attaching files to emails.
    • Receiving and reading emails (imaplib)
    • Automating report distribution via email.
  • Working with Dates and Times (datetime module)
  • • Getting current date/time.
    • Formatting dates and times.
    • Performing date/time arithmetic
  • Scheduling Tasks
  • • How to make your Python scripts executable by the scheduler.
    • OS-level schedulers (such as Task Scheduler on Windows).
    • Using Python libraries like schedule or
    • APScheduler for in-script scheduling.
  • Robustness and Reusability of Automated python Scripts
  • • Error Handling (try, except, finally):
    • Debugging:
    • Program Organization & Command Line Arguments