Data Science Learning Journey

A structured 24-day hands-on data science learning programme covering the full spectrum from Python fundamentals to machine learning, built through daily exercises, real-world datasets, and progressive projects.

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Topics Covered

Day	Topic	Notebook
2	Python Fundamentals, conditionals, loops, functions	DAY2_ASSIGNMENT.ipynb
3	Python Practice	DAY3.ipynb
6	Python Intermediate	DAY6.ipynb
7	Python Advanced	DAY7.ipynb
9	SQL Basics	day9.sql
11	NumPy & Pandas Introduction	numpy_pandas_day11.ipynb
12	Pandas Data Manipulation	pandas_day12.ipynb
13	Exploratory Data Analysis	DAY13.ipynb
14	In-depth & Index Analysis	DAY 14 indept analysis.ipynb
15	Data Visualisation (Matplotlib, Seaborn, Plotly)	DataVisualizationDay15.ipynb
16	Advanced Visualisation with Excel Integration	day16.ipynb
22	Tableau Visualisation	day22 tableau visualization class.twb
23	Linear Regression, House Price Prediction	LINEAR_REGRESSIONDAY23.ipynb
24	Classification, Credit Customer Prediction	classificationDay24.ipynb
,	Natural Language Processing & Sentiment Analysis	NATURAL_LANG_PROCESSOR.ipynb
,	Unsupervised ML, K-Means Clustering	unsupervied_machine_learning.ipynb
,	Practice Exercises	PRACTICE.ipynb

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Datasets Used

Dataset	Domain
house_data.csv	Real estate pricing
credit_customers.csv	Financial risk
Mall_Customers.csv	Retail customer segmentation
16k_Movies.csv	Entertainment analytics
Impact_of_Remote_Work_on_Mental_Health.csv	Wellbeing research
sentimentdataset.csv	NLP / sentiment
Tweets.csv	Social media text
Exchange_Rate.csv / RealGDP20092024.csv	Macroeconomics
user_behavior_dataset.csv	Digital behaviour

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Tech Stack

• Language: Python 3
• Core Libraries: NumPy, pandas, Matplotlib, Seaborn, Plotly
• Machine Learning: scikit-learn (Linear Regression, Classification, K-Means)
• NLP: NLTK / TextBlob
• Other: SQL, Tableau, Excel (openpyxl)

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How to Run

git clone https://github.com/Momahmoses/DataScience_Class1.git
cd DataScience_Class1
pip install numpy pandas matplotlib seaborn plotly scikit-learn nltk
jupyter notebook

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Author

Momah Moses, Geospatial AI Engineer & Data Scientist | GitHub