📅 45-Day Data Analyst + Data Science Roadmap (with Projects)

This fast-track roadmap is designed for focused learners who want job-ready Data Analyst + Data Science skills in just 1.5 months. It blends theory with hands-on mini and major projects to build a strong portfolio.

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🔰 Week 1: Foundations (Days 1–7)

Days	Focus Area	Topics / Tools	Resources / Task	Project
1–2	Python Basics	Syntax, Loops, Lists, Functions	Python	🧪 CLI Calculator
3–4	Python Intermediate	OOP, File Handling	Write Python scripts	🧪 CSV Data Cleaner
5–6	SQL Basics	SELECT, JOIN, Aggregations	SQL	🧪 Employee DB Queries
7	Linux CLI + Math	Bash Commands, Maths, Probability, Stats	Linux & Maths Basics	🧪 Linux Automation Script

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📊 Week 2: Analysis & EDA (Days 8–14)

Days	Focus Area	Topics / Tools	Resources / Task	Project
8–9	NumPy	Arrays, Math, Indexing	NumPy	🧪 Weather Matrix
10–11	Pandas	DataFrames, Merging	Pandas	🧪 EDA on Titanic
12–13	Data Cleaning	Missing values, Outliers	Clean Housing Dataset	🧪 Clean Housing Data
14	Feature Engg.	Encoding, Scaling	sklearn.preprocessing	🧪 Feature Engg. for ML

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📈 Week 3: Visualization + Dashboard (Days 15–21)

Days	Focus Area	Topics / Tools	Resources / Task	Project
15–16	Matplotlib/Seaborn	Bar Charts, Heatmaps, etc.	Matplotlib	🧪 Student Score Heatmap
17–18	Power BI / Tableau	Data modeling, Filters, Cards	Power BI	🧪 Sales Dashboard
19–21	Final Viz Project	Combine EDA + Viz	Build Presentation	✅ Retail Sales Insights Dashboard

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🤖 Week 4: Machine Learning (Days 22–28)

Days	Focus Area	Topics / Tools	Resources / Task	Project
22–23	Regression	Linear, Logistic	ML Course	🧪 House Price Prediction
24–25	Classification	KNN, Decision Tree	scikit-learn	🧪 Iris Classifier
26–27	Clustering	K-Means	Unsupervised Learning	🧪 Customer Segmentation
28	Eval & Tuning	Accuracy, ROC, GridSearchCV	Model Evaluation, Cross Validation	🧪 Model Evaluation on Fraud Dataset

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🧠 Week 5: Deep Learning & GenAI (Days 29–35)

Days	Focus Area	Topics / Tools	Resources / Task	Project
29–30	Neural Nets	Activation, Layers, Forward	DL	🧪 Custom 3-layer NN
31–32	CNN	Image Classification	TensorFlow	🧪 MNIST Digit Classifier
33–34	LLMs/Transformers	Prompting, Tokenization	HuggingFace	🧪 Mini Q&A Chatbot
35	Final ML Project	Fraud Detection w/ Autoenc.	Build end-to-end model	✅ Credit Card Fraud Detection

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🚀 Week 6: Deployment + Portfolio (Days 36–45)

Days	Focus Area	Topics / Tools	Resources / Task	Project
36–37	Flask	Create ML API	Flask	🧪 Flask App for Classifier
38–39	Docker	Build + Containerize	Docker	🧪 Dockerized ML App
40–42	MLOps + Cloud	MLFlow, AWS / GCP	MLOps	🧪 Track Model with MLFlow
43–45	Capstone Project	Domain: Retail/Health	Full E2E Pipeline	✅ Final Capstone Project Deployed

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✅ Final Deliverables

Type	Count
Mini Projects	7–9
Major Projects	3
Dashboards (BI)	1
Deployed Projects	2

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More Projects:

1. Credit Card Fraud Detection using Autoencoders in Keras | TensorFlow for Hackers Assess credit risk by applying probability distributions and statistical analysis on credit card data.This project employs autoencoders—a type of neural network—to detect fraudulent credit card transactions. By learning the patterns of normal transactions, the autoencoder can identify anomalies that may indicate fraud. This approach leverages unsupervised learning, making it effective even when labeled fraudulent data is scarce.

2. Predictive Maintenance using Machine Learning by Medini Kumar Bora This project focuses on predicting equipment failures before they occur, allowing for timely maintenance and reduced downtime. By analyzing historical sensor data and operational metrics, machine learning models can forecast potential issues, optimizing maintenance schedules and extending equipment lifespan.

3. Customer Segmentation using K Means: A Step by Step Guide by Leonardo A This project utilizes K-Means clustering to segment customers based on shared characteristics. By identifying distinct customer groups, businesses can tailor their marketing strategies, improve customer satisfaction, and enhance overall profitability. The iterative process of training, testing, and tweaking ensures the model's effectiveness.

4. Harvestify Github Harvestify is a machine learning-based website that assists farmers by recommending the best crops to cultivate, suitable fertilizers, and diagnosing crop diseases. By inputting specific parameters, users receive data-driven suggestions, promoting efficient farming practices and potentially increasing yields.

5. Machine Learning for Retail Demand Forecasting by Samir Saci This project compares different methods for forecasting retail demand, specifically contrasting XGBoost models with rolling mean approaches. Accurate demand forecasting enables retailers to optimize inventory levels, reduce stockouts, and enhance customer satisfaction. The study provides insights into the effectiveness of machine learning techniques in retail settings.

More Projects

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📌 How to Use This Roadmap?

• Follow the roadmap step by step 🔥
• Work on real-world projects & open source
• Deploy your models on cloud platforms
• Keep learning MLOps & scaling techniques

🚀 Happy Learning!