✅ Python Basics for Data Science: Part-1

Variables Data Types

In Python, variables are used to store data, and data types define what kind of data is stored. This is the first and most essential building block of your data science journey.

1️⃣ What is a Variable?
A variable is like a label for data stored in memory. You can assign any value to a variable and reuse it throughout your code.

Syntax:

x = 10  
name = "Riya"  
is_active = True

2️⃣ Common Data Types in Python

• int – Integers (whole numbers)

age = 25

• float – Decimal numbers

height = 5.8

• str – Text/String

city = "Mumbai"

• bool – Boolean (True or False)

is_student = False

• list – A collection of items

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

• tuple – Ordered, immutable collection

coordinates = (10.5, 20.3)

• dict – Key-value pairs

student = {"name": "Riya", "score": 90}

3️⃣ Type Checking
You can check the type of any variable using type()

print(type(age))       # <class 'int'>  
print(type(city))      # <class 'str'>

4️⃣ Type Conversion
Change data from one type to another:

num = "100"
converted = int(num)  
print(type(converted))  # <class 'int'>

5️⃣ Why This Matters in Data Science
Data comes in various types. Understanding and managing types is critical for:
• Cleaning data
• Performing calculations
• Avoiding errors in analysis

✅ Practice Task for You:
• Create 5 variables with different data types
• Use type() to print each one
• Convert a string to an integer and do basic math

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✅ Python Basics for Data Science: Part-2

Loops Functions 🔁🧠

These two concepts are key to writing clean, efficient, and reusable code — especially when working with data.

1️⃣ Loops in Python
Loops help you repeat tasks like reading data, checking values, or processing items in a list.

For Loop

fruits = ["apple", "banana", "mango"]
for fruit in fruits:
    print(fruit)

While Loop

count = 1
while count <= 3:
    print("Loading...", count)
    count += 1

Loop with Condition

numbers = [10, 5, 20, 3]
for num in numbers:
    if num > 10:
        print(num, "is greater than 10")

2️⃣ Functions in Python
Functions let you group code into blocks you can reuse.

Basic Function

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

print(greet("Riya"))

Function with Logic

def is_even(num):
    if num % 2 == 0:
        return True
    return False

print(is_even(4))  # Output: True

Function for Calculation

def square(x):
    return x * x

print(square(6))  # Output: 36

✅ Why This Matters in Data Science
• Loops help in iterating over datasets
• Functions make your data cleaning reusable
• Helps organize long analysis code into simple blocks

🎯 Practice Task for You:
• Write a for loop to print numbers from 1 to 10
• Create a function that takes two numbers and returns their average
• Make a function that returns "Even" or "Odd" based on input

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✅ Python for Data Science: Part-3

NumPy Pandas Basics 📊🐍
These two libraries form the foundation for handling and analyzing data in Python.

1️⃣ NumPy – Numerical Python
NumPy helps with fast numerical operations and array handling.

Importing NumPy

import numpy as np

Create Arrays

arr = np.array([1, 2, 3])
print(arr)

Array Operations

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(a + b)        # [5 7 9]
print(a * 2)        # [2 4 6]

Useful NumPy Functions

np.mean(a)          # Average
np.max(b)           # Max value
np.arange(0, 10, 2) # [0 2 4 6 8]

2️⃣ Pandas – Data Analysis Library
Pandas is used to work with data in table format (DataFrames).

Importing Pandas

import pandas as pd

Create a DataFrame

data = {
    "Name": ["Riya", "Aman"],
    "Age": [24, 30]
}
df = pd.DataFrame(data)
print(df)

Read CSV File

df = pd.read_csv("data.csv")

Basic DataFrame Operations

df.head()       # First 5 rows  
df.info()       # Column types  
df.describe()   # Stats summary  
df["Age"].mean()  # Average age  

Filter Rows

df[df["Age"] > 25]

🎯 Why This Matters
• NumPy makes math faster and easier
• Pandas helps clean, explore, and transform data
• Essential for real-world data analysis

Practice Task:
• Create a NumPy array of 10 numbers
• Make a Pandas DataFrame with 2 columns (Name, Score)
• Filter all scores above 80

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✅ Python for Data Science: Part-4

Data Visualization with Matplotlib, Seaborn Plotly 📊📈

1️⃣ Matplotlib – Basic Plotting
Great for simple line, bar, and scatter plots.

Import and Line Plot

import matplotlib.pyplot as plt

x = [1, 2, 3, 4]
y = [10, 20, 25, 30]
plt.plot(x, y)
plt.title("Line Plot")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()

Bar Plot

names = ["A", "B", "C"]
scores = [80, 90, 70]
plt.bar(names, scores)
plt.title("Scores by Name")
plt.show()

2️⃣ Seaborn – Statistical Visualization
Built on Matplotlib with better styling.

Import and Plot

import seaborn as sns
import pandas as pd

df = pd.DataFrame({
    "Name": ["Riya", "Aman", "John", "Sara"],
    "Score": [85, 92, 78, 88]
})

sns.barplot(x="Name", y="Score", data=df)

Other Seaborn Plots

sns.histplot(df["Score"])          # Histogram  
sns.boxplot(x=df["Score"])         # Box plot  

3️⃣ Plotly – Interactive Graphs
Great for dashboards and interactivity.

Basic Line Plot

import plotly.express as px

df = pd.DataFrame({
    "x": [1, 2, 3],
    "y": [10, 20, 15]
})

fig = px.line(df, x="x", y="y", title="Interactive Line Plot")
fig.show()

🎯 Why Visualization Matters
• Helps spot patterns in data
• Makes insights clear and shareable
• Supports better decision-making

Practice Task:
• Create a line plot using matplotlib
• Use seaborn to plot a boxplot for scores
• Try any interactive chart using plotly

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✅ Python for Data Science: Part-5

📊 Descriptive Statistics, Probability Distributions

1️⃣ Descriptive Statistics with Pandas
Quick way to summarize datasets.

import pandas as pd

data = {"Marks": [85, 92, 78, 88, 90]}
df = pd.DataFrame(data)

print(df.describe())      # count, mean, std, min, max, etc.
print(df["Marks"].mean()) # Average
print(df["Marks"].median()) # Middle value
print(df["Marks"].mode()) # Most frequent value

2️⃣ Probability Basics
Chances of an event occurring (0 to 1)

Tossing a coin

prob_heads = 1 / 2
print(prob_heads)  # 0.5

Multiple outcomes example:

from itertools import product

outcomes = list(product(["H", "T"], repeat=2))
print(outcomes)  # [('H', 'H'), ('H', 'T'), ('T', 'H'), ('T', 'T')]

3️⃣ Normal Distribution using NumPy Seaborn

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

data = np.random.normal(loc=0, scale=1, size=1000)

sns.histplot(data, kde=True)
plt.title("Normal Distribution")
plt.show()

4️⃣ Other Distributions
• Binomial → pass/fail outcomes
• Poisson → rare event frequency
• Uniform → all outcomes equally likely

Binomial Example:

from scipy.stats import binom

# 10 trials, p = 0.5
print(binom.pmf(k=5, n=10, p=0.5))  # Probability of 5 successes

🎯 Why This Matters
• Descriptive stats help understand data quickly
• Distributions help model real-world situations
• Probability supports prediction and risk analysis

Practice Task:
• Generate a normal distribution
• Calculate mean, median, std
• Plot binomial probability of success

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✅ Data Science Resume Tips 📊💼

To land data science roles, your resume should highlight problem-solving, tools, and real insights.

1️⃣ Contact Info (Top)
• Name, email, GitHub, LinkedIn, portfolio/Kaggle
• Optional: location, phone

2️⃣ Summary (2–3 lines)
Brief overview showing your skills + value
➡ “Data scientist with strong Python, ML & SQL skills. Built projects in healthcare & finance. Proven ability to turn data into insights.”

3️⃣ Skills Section
Group by type:
• Languages: Python, R, SQL
• Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
• Tools: Jupyter, Git, Tableau, Power BI
• ML/Stats: Regression, Classification, Clustering, A/B testing

4️⃣ Projects (Most Important)
List 3–4 impactful projects:
• Clear title
• Dataset used
• What you did (EDA, model, visualizations)
• Tools used
• GitHub + live dashboard (if any)

Example:
Loan Default Prediction – Used logistic regression + feature engineering on Kaggle dataset to predict defaults. 82% accuracy.
GitHub: [link]

5️⃣ Work Experience / Internships
Show how you used data to create value:
• “Built churn prediction model → reduced churn by 15%”
• “Automated Excel reports using Python, saving 6 hrs/week”

6️⃣ Education
• Degree or certifications
• Mention bootcamps, if relevant

7️⃣ Certifications (Optional)
• Google Data Analytics
• IBM Data Science
• Coursera/edX Machine Learning

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• Use real datasets
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✅ GitHub Profile Tips for Data Scientists 🧠📊

Your GitHub = your portfolio. Make it show skills, tools, and thinking.

1️⃣ Profile README
• Who you are & what you work on
• Mention tools (Python, Pandas, SQL, Scikit-learn, Power BI)
• Add project links & contact info
✅ Example:
“Aspiring Data Scientist skilled in Python, ML & visualization. Love solving business problems with data.”

2️⃣ Highlight 3–6 Strong Projects
Each repo must have:
• Clear README:
– What problem you solved
– Dataset used
– Key steps (EDA → Model → Results)
– Tools & libraries
• Jupyter notebooks (cleaned + explained)
• Charts & results with conclusions
✅ Tip: Include PDF/report or dashboard screenshots

3️⃣ Project Ideas to Include
• Sales insights dashboard (Power BI or Tableau)
• ML model (churn, fraud, sentiment)
• NLP app (text summarizer, topic model)
• EDA project on Kaggle dataset
• SQL project with queries & joins

4️⃣ Show Real Workflows
• Use .py scripts + .ipynb notebooks
• Add data cleaning + preprocessing steps
• Track experiments (metrics, models tried)

5️⃣ Regular Commits
• Update notebooks
• Push improvements
• Show learning progress over time

📌 Practice Task:
Pick 1 project → Write full README → Push to GitHub today

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✅ Data Science Mistakes Beginners Should Avoid ⚠️📉

1️⃣ Skipping the Basics
• Jumping into ML without Python, Stats, or Pandas
✅ Build strong foundations in math, programming & EDA first

2️⃣ Not Understanding the Problem
• Applying models blindly
• Irrelevant features and metrics
✅ Always clarify business goals before coding

3️⃣ Treating Data Cleaning as Optional
• Training on dirty/incomplete data
✅ Spend time on preprocessing — it’s 70% of real work

4️⃣ Using Complex Models Too Early
• Overfitting small datasets
• Ignoring simpler, interpretable models
✅ Start with baseline models (Logistic Regression, Decision Trees)

5️⃣ No Evaluation Strategy
• Relying only on accuracy
✅ Use proper metrics (F1, AUC, MAE) based on problem type

6️⃣ Not Visualizing Data
• Missed outliers and patterns
✅ Use Seaborn, Matplotlib, Plotly for EDA

7️⃣ Poor Feature Engineering
• Feeding raw data into models
✅ Create meaningful features that boost performance

8️⃣ Ignoring Domain Knowledge
• Features don’t align with real-world logic
✅ Talk to stakeholders or do research before modeling

9️⃣ No Practice with Real Datasets
• Kaggle-only learning
✅ Work with messy, real-world data (open data portals, APIs)

🔟 Not Documenting or Sharing Work
• No GitHub, no portfolio
✅ Document notebooks, write blogs, push projects online

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✅ Python Libraries & Tools You Should Know 🐍💼

Mastering the right Python libraries helps you work faster, smarter, and more effectively in any data role.

🔷 1️⃣ For Data Analytics 📊
Useful for cleaning, analyzing, and visualizing data
• pandas – Handle and manipulate structured data (tables)
• numpy – Fast numerical operations, arrays, math
• matplotlib – Basic data visualizations (charts, plots)
• seaborn – Statistical plots, easier visuals with pandas
• openpyxl – Read/write Excel files
• plotly – Interactive visualizations and dashboards

🔷 2️⃣ For Data Science 🧠
Used for statistics, experimentation, and storytelling
• scipy – Scientific computing, probability, optimization
• statsmodels – Statistical testing, linear models
• sklearn – Preprocessing + classic ML algorithms
• sqlalchemy – Work with databases using Python
• Jupyter – Interactive notebooks for code, text, charts
• dash – Create dashboard apps with Python

🔷 3️⃣ For Machine Learning 🤖
Build and train predictive and deep learning models
• scikit-learn – Core ML: regression, classification, clustering
• TensorFlow – Deep learning by Google
• PyTorch – Deep learning by Meta, flexible and research-friendly
• XGBoost – Popular for gradient boosting models
• LightGBM – Fast boosting by Microsoft
• Keras – High-level neural network API (runs on TensorFlow)

💡 Tip:
• Learn pandas + matplotlib + sklearn first
• Add ML/DL libraries based on your goals

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