A-Z of essential data science concepts

A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://news.1rj.ru/str/datasciencefun

Like if you need similar content 😄👍

Hope this helps you 😊

How to convert image to pdf in Python

# Python3 program to convert image to pfd
# using img2pdf library
 
# importing necessary libraries
import img2pdf
from PIL import Image
import os
 
# storing image path
img_path = "Input.png"
 
# storing pdf path
pdf_path = "file_pdf.pdf"
 
# opening image
image = Image.open(img_path)
 
# converting into chunks using img2pdf
pdf_bytes = img2pdf.convert(image.filename)
 
# opening or creating pdf file
file = open(pdf_path, "wb")
 
# writing pdf files with chunks
file.write(pdf_bytes)
 
# closing image file
image.close()
 
# closing pdf file
file.close()
 
# output
print("Successfully made pdf file")

pip3 install pillow && pip3 install img2pdf

Python for Data Science

Let's go through important Python Topics Everyday starting with the first one today

Variables & Data Types in Python

What are Variables?
Variables are used to store data so you can refer to it and manipulate it later in your code. You don’t need to declare the type of variable explicitly in Python — it figures it out based on the value you assign.

Example:
x = 10 name = "Alice" price = 19.99 is_active = True

Data Types in Python

int – Integer numbers
Example: x = 5

float – Decimal numbers
Example: pi = 3.14

str – String or text
Example: name = "John"

bool – Boolean values (True or False)
Example: is_logged_in = False

list – An ordered, changeable collection
Example: fruits = ["apple", "banana", "cherry"]

tuple – An ordered, unchangeable collection
Example: coordinates = (10, 20)

set – An unordered collection of unique items
Example: unique_ids = {1, 2, 3}

dict – A collection of key-value pairs
Example: person = {"name": "Alice", "age": 25}

React with ❤️ if you want to continue this posting Python Series everyday

Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

ENJOY LEARNING 👍👍

Control Flow (if, elif, else) in Python

What is Control Flow?

Control flow allows your code to make decisions. You use if, elif, and else statements to run different blocks of code depending on conditions.

Basic Structure:

if condition:
# code runs if condition is True
elif another_condition:
# code runs if the second condition is True
else:
# code runs if none of the above are True

Example:

age = 20

if age < 18:
print("You are a minor.")
elif age == 18:
print("You just became an adult!")
else:
print("You are an adult.")

Comparison Operators:

== (equal to)

!= (not equal to)

< (less than)

> (greater than)

<= (less than or equal to)

>= (greater than or equal to)


Logical Operators:

and – Both conditions must be True

or – At least one condition must be True

not – Reverses the result (True becomes False)


Example with logical operators:

age = 25
is_student = False

if age > 18 and not is_student:
print("Eligible for the job.")

React with ❤️ if you want to continue explaining important Python concepts

Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

ENJOY LEARNING 👍👍

Loops in Python (for & while)

What are Loops?

Loops let you repeat a block of code multiple times. They’re essential for tasks like iterating over data, automating repetitive steps, or checking conditions.


1. For Loop

Used to iterate over a sequence like a list, string, or range.

Example:

fruits = ["apple", "banana", "cherry"]

for fruit in fruits:
print(fruit)

You can also use range() to loop a specific number of times:

for i in range(5):
print(i)


2. While Loop

Repeats as long as a condition is True.

Example:

count = 0

while count < 3:
print("Count is:", count)
count += 1


Loop Control Statements:

break – exits the loop completely

continue – skips the current iteration and continues with the next

pass – does nothing (used as a placeholder)


Example:

for i in range(5):
if i == 3:
break
print(i)

Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

ENJOY LEARNING 👍👍

Functions in Python

Functions are reusable blocks of code that perform a specific task. They help keep your code organized, modular, and easier to debug.


Defining a Function:

You use the def keyword to define a function.

Syntax:

def function_name(parameters):
# code block
return result

Example:

def greet(name):
return f"Hello, {name}!"

message = greet("Alice")
print(message)


Key Concepts:

Parameters: Inputs you pass into the function (e.g., name)

Arguments: Actual values passed when calling the function (e.g., "Alice")

Return Statement: Sends back a result to wherever the function was called



Default Arguments:

You can set default values for parameters.

def greet(name="Guest"):
return f"Hello, {name}!"


Keyword Arguments:

Call functions using parameter names for clarity.

def add(a, b):
return a + b

result = add(b=3, a=2)


Why to use Functions:

- Reduce repetition

- Make code easier to understand and maintain

- Allow modular programming


React with ❤️ if you're up for the next topic 📖 List Comprehensions

Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

ENJOY LEARNING 👍👍

List Comprehensions in Python

It’s a concise way to create new lists by applying an expression to each item in an iterable (like a list or range), optionally filtering with conditions.


Basic Syntax:

new_list = [expression for item in iterable]

Example:

squares = [x**2 for x in range(5)]
print(squares) # Output: [0, 1, 4, 9, 16]


With Condition:

even_numbers = [x for x in range(10) if x % 2 == 0]
print(even_numbers) # Output: [0, 2, 4, 6, 8]


Equivalent Without List Comprehension:

squares = []
for x in range(5):
squares.append(x**2)


Why to use list comprehensions?

- It’s more readable and compact

- Runs faster than traditional for loops (in most cases)

- Helps clean up your code

React with ❤️ if you're up for the next topic 📖 Dictionaries in Python

Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

ENJOY LEARNING 👍👍

Dictionary in Python

A dictionary is a collection of key-value pairs. Each key is unique and maps to a value. Dictionaries are unordered (until Python 3.7) and mutable, meaning you can change them.


Creating a Dictionary:

person = {
"name": "Alice",
"age": 25,
"city": "New York"
}


Accessing Values:

print(person["name"]) # Output: Alice
print(person.get("age")) # Output: 25

Using .get() is safer because it won’t crash if the key doesn’t exist.

Adding or Updating Items:

person["email"] = "alice@example.com" # Add new key
person["age"] = 26 # Update value


Deleting Items:

del person["city"]


Looping through a Dictionary:

for key, value in person.items():
print(key, ":", value)


Why Use Dictionaries?

Fast access via keys

Ideal for storing structured data (like a JSON object)

Very flexible in combining with loops and functions


React with ❤️ if you're up for the next topic 📖 Exception Handling in Python

Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

ENJOY LEARNING 👍👍