Agenda for Week 3

Hands-on Workshop: Python for Data Science

  1. Basic Python programming skills with a focus on data analysis.
  2. Introduction to Python programming language
  3. Basic Python syntax and data structures (lists, tuples, dictionaries)
  4. Introduction to Python libraries for data science (Pandas, Numpy)
  5. Reading data into Python and performing basic data cleaning
  6. Simple data analysis using Python

Part 1: Python Basics

Variables and Data Types

In Python, we can store information in variables. There are several types of data we can store, including integers, floating point numbers, strings, and Booleans.

   # Integer
   x = 10

   # Float
   y = 10.0

   # String
   z = "Hello, World!"

   # Boolean
   a = True

Arithmetic Operations

Python supports all the basic arithmetic operations.

   # Addition
   print(5 + 5)

   # Subtraction
   print(5 - 2)

   # Multiplication
   print(3 * 3)

   # Division
   print(10 / 2)

   # Exponentiation
   print(4 ** 2)

Logical Operations

Python also supports logical operations, which are often used in conditional statements.

   # And operation
   print(True and False)

   # Or operation
   print(True or False)

   # Not operation
   print(not True)

Conditional Statements

Conditional statements are used to perform different computations or actions depending on whether a condition evaluates to true or false.

   # Define a variable
   x = 10

   # If statement
   if x > 0:
       print("x is positive")

   # If-else statement
   if x % 2 == 0:
       print("x is even")
       print("x is odd")

   # If-elif-else statement
   if x < 0:
       print("x is negative")
   elif x == 0:
       print("x is zero")
       print("x is positive")


Loops are used to repeatedly execute a block of code.

   # For loop
   for i in range(5):

   # While loop
   i = 0
   while i < 5:
       i += 1

   # Loop through a list
   fruits = ["apple", "banana", "cherry"]
   for fruit in fruits:


Functions are reusable blocks of code that perform a specific task.

   # Define a function
   def greet(name):
       return "Hello, " + name

   # Call the function

   # Function with multiple parameters
   def power(base, exponent):
       return base ** exponent

   # Call the function
   print(power(2, 3))

Part 2: Python Data Structures

Python has four basic inbuilt data structures: Lists, Tuples, Sets, and Dictionaries.


A list is a collection of items. It is ordered, changeable, and allows duplicate elements.

   # Define a list
   fruits = ["apple", "banana", "cherry"]

   # Access list items by index

   # Change the value of a list item
   fruits[1] = "blueberry"

   # Add an item to the list


A tuple is similar to a list, but it is ordered and unchangeable.

   # Define a tuple
   fruits_tuple = ("apple", "banana", "cherry")

   # Access tuple items by index

   # Trying to change the value of a tuple item throws an error
   # fruits_tuple[1] = "blueberry"  # This will throw an error


A dictionary is an unordered collection of key-value pairs.

   # Define a dictionary
   fruit_colors = {
       "apple": "red",
       "banana": "yellow",
       "cherry": "red"

   # Access dictionary items by key

   # Change the value of a dictionary item
   fruit_colors["banana"] = "green"


A set is an unordered collection of unique items.

   # Define a set
   fruits_set = {"apple", "banana", "cherry", "apple"}
   print(fruits_set)  # Duplicates are removed

Part 3: Introduction to Pandas and NumPy

Creating arrays in NumPy

   import numpy as np

   # Create a one-dimensional array
   a = np.array([1, 2, 3])

   # Create a two-dimensional array
   b = np.array([[1, 2, 3], [4, 5, 6]])

Manipulating arrays in NumPy

   # Change an element of the array
   b[0, 0] = 10

   # Get the shape of the array

Creating DataFrames in Pandas

   import pandas as pd

   # Create a DataFrame from a dictionary
   df = pd.DataFrame({
       "Name": ["Alice", "Bob", "Charlie"],
       "Age": [25, 30, 35],
       "Occupation": ["Doctor", "Engineer", "Teacher"]

Manipulating DataFrames in Pandas

   # Select a column

   # Add a new column
   df["Salary"] = [100000, 120000, 90000]

   # Delete a column
   df = df.drop("Age", axis=1)

Reading data from CSV files

   # Assuming we have a CSV file "data.csv"
   df = pd.read_csv("data.csv")

Please note: you’d need to have a data.csv file available and replace data.csv with the path to your file. Uncomment this section when ready to use.

Basic data exploration

   # Get the shape of the DataFrame

   # Get information about the DataFrame

   # Describe the DataFrame


In this lab, we have covered the basics of Python including data types, arithmetic and logical operations, conditional statements, loops, and functions. We’ve also explored Python’s basic data structures and were introduced to the data science libraries Pandas and NumPy. Practice these concepts and get comfortable with them, as they are the building blocks for more complex data science tasks.

Remember, the aim is to create a learning experience where students can get their hands dirty with code while also understanding the theory behind the actions they perform.

title: Weekly Meeting Agenda subtitle: baseline for weekly meeting agenda author: Thaddeus Thomas —

Each meeting can have a unique structure depending on the nature of that weeks session.

Example of weekly meeting

General framework to ensure meetings are well-organized and efficient.

One-hour meeting structure

  1. Introduction (5-10 minutes):
    • Welcome participants.
    • Share the agenda for the meeting.
    • Recap the last meeting (if applicable) and note any follow-ups.
  2. Main Agenda (40-45 minutes):
    • This is where the main activities of the meeting will take place.
    • For a workshop or lecture, this will include the main presentation and demonstration.
    • For a discussion, this may involve presenting the topic and then facilitating a group discussion.
    • For a hackathon or project showcase, this would include the actual work or presentations.
  3. Q&A/Discussion (5-10 minutes):
    • Reserve some time for attendees to ask questions or discuss the day’s topic further.
    • Encourage participation and interaction.
  4. Conclusion and Looking Ahead (2-3 minutes):
    • Wrap up the meeting and summarize key points.
    • Briefly mention what the next meeting will entail.
    • Thank everyone for their participation.