Data Science with Python – From Raw Data to Smart Decisions

What Is Data Science?

Data Science is the process of extracting useful knowledge from data using tools like:

 ✅ Programming (e.g., Python)
✅ Statistics & math
✅ Data cleaning and analysis
✅ Visualization
✅ Machine learning

In short: Turn data into decisions.

🐍 Why Use Python for Data Science?

 ✅ Easy to learn and read
✅ Large ecosystem of libraries
✅ Great for data wrangling and visualization
✅ Powerful for ML and AI
✅ Supported by global community and growing in Ethiopia

🧱 1. Key Python Libraries for Data Science

📥 2. Step-by-Step Data Science Workflow

✅ 1. Data Collection

From Excel, CSV, databases, APIs, or web scraping

import pandas as pd

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

✅ 2. Data Cleaning

Fix missing, duplicate, or incorrect values

df.dropna()

df['Age'] = df['Age'].fillna(20)

✅ 3. Data Exploration

Understand the shape, type, and basic stats

df.info()

df.describe()

df['Score'].value_counts()

✅ 4. Data Visualization

Use charts to see trends and patterns

import matplotlib.pyplot as plt

df['Score'].hist()

✅ 5. Feature Engineering

Create or select important columns for prediction

df['Pass'] = df['Score'] > 50

✅ 6. Modeling (with Scikit-learn)

Example: Predict if a student will pass

from sklearn.linear_model import LogisticRegression

model = LogisticRegression()

model.fit(X_train, y_train)

model.predict(X_test)

✅ 7. Evaluation

Check accuracy or performance of your model

from sklearn.metrics import accuracy_score

accuracy_score(y_test, predictions)

📊 3. Real-World Projects for Ethiopian Students

 ✅ Analyze NEAEA or Ministry of Education data
✅ Build dashboards for health or NGO impact
✅ Forecast business sales using linear regression
✅ Create a system to detect student dropout risk
✅ Classify social media sentiment for local brands

💼 Data Science Career Paths

 ✅ Data Analyst
✅ Machine Learning Engineer
✅ Data Scientist
✅ Business Intelligence Developer
✅ Health Data Officer
✅ Monitoring & Evaluation Specialist
✅ AI Engineer

🔗 Skills You’ll Learn

 ✅ Python coding
✅ Statistics & probability
✅ Data visualization
✅ Working with CSV, Excel, APIs
✅ Problem solving with data
✅ Model building and evaluation