Data Science Roadmap – Step-by-Step Guide 🚀

1️⃣ Programming & Data Manipulation

Python (Pandas, NumPy, Matplotlib, Seaborn)

SQL (Joins, CTEs, Window Functions, Aggregations)

Data Wrangling & Cleaning (handling missing data, duplicates, normalization)


2️⃣ Statistics & Mathematics

Descriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation)

Probability Theory (Bayes' Theorem, Conditional Probability)

Hypothesis Testing (T-test, ANOVA, Chi-square test)

Linear Algebra & Calculus (Matrix operations, Differentiation)


3️⃣ Data Visualization

Matplotlib & Seaborn for static visualizations

Power BI & Tableau for interactive dashboards

ggplot (R) for advanced visualizations


4️⃣ Machine Learning Fundamentals

Supervised Learning (Linear Regression, Logistic Regression, Decision Trees)

Unsupervised Learning (Clustering, PCA, Anomaly Detection)

Model Evaluation (Confusion Matrix, Precision, Recall, F1-Score, AUC-ROC)


5️⃣ Advanced Machine Learning

Ensemble Methods (Random Forest, Gradient Boosting, XGBoost)

Hyperparameter Tuning (GridSearchCV, RandomizedSearchCV)

Deep Learning Basics (Neural Networks, TensorFlow, PyTorch)


6️⃣ Big Data & Cloud Computing

Distributed Computing (Hadoop, Spark)

Cloud Platforms (AWS, GCP, Azure)

Data Engineering Basics (ETL Pipelines, Apache Kafka, Airflow)


7️⃣ Natural Language Processing (NLP)

Text Preprocessing (Tokenization, Lemmatization, Stopword Removal)

Sentiment Analysis, Named Entity Recognition

Transformers & Large Language Models (BERT, GPT)


8️⃣ Deployment & Model Optimization

Flask & FastAPI for model deployment

Model monitoring & retraining

MLOps (CI/CD for Machine Learning)


9️⃣ Business Applications & Case Studies

A/B Testing & Experimentation

Customer Segmentation & Churn Prediction

Time Series Forecasting (ARIMA, LSTM)


🔟 Soft Skills & Career Growth

Data Storytelling & Communication

Resume & Portfolio Building (Kaggle Projects, GitHub Repos)

Networking & Job Applications (LinkedIn, Referrals)

Free Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

ENJOY LEARNING 👍👍

Want to learn machine learning without drowning in math or hype?

Start here:

5 ML algorithms every DIY data scientist should know 🧵👇

Day 1: Decision Trees

If you’ve ever asked, “What things can predict X?”

Decision trees are your best friend.

They split your data into rules like:

If age > 55 => Low risk
If call_count > 5 => Offer retention deal

Is your data in the form of a table?

(Hint - most data is).

Day 2: K-Means Clustering

The problem with predictive models like decision trees is that they need labeled data.

What if your data is unlabeled?

(Hint - most data is unlabeled)

K-means clustering discovers hidden groups - without needing labels.

Day 3: Logistic Regression

Logistic regression is a predictive modeling technique.

It predicts probabilities like:

Will this user churn?
Will this ad be clicked?
Will this customer convert?

Logistic regression is an excellent tool for explaining driving factors to business stakeholders.

Day 4: Random Forests

Random forests == a bunch of decision trees working together.

Each one is a bit different, and they vote on the outcome.

The result?

Better accuracy and stability than a single tree.

This is a production-quality ML algorithm.

Day 5: DBSCAN Clustering

K-means assumes groups are circular.

DBSCAN doesn’t.

It finds clusters of any shape and filters out noise automatically.

For example, you can use it for anomaly detection.

DBSCAN is the perfect complement to k-means in your DIY data science tool belt.

Free Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

ENJOY LEARNING 👍👍

Step-by-Step Approach to Learn Machine Learning

➊ Learn a Programming Language → Python or R
↓
➋ Mathematical Foundations → Linear Algebra, Probability, Statistics, Calculus
↓
➌ Data Preprocessing → Pandas, NumPy, Handling Missing Data, Feature Engineering
↓
➍ Exploratory Data Analysis (EDA) → Data Cleaning, Outliers, Visualization (Matplotlib, Seaborn)
↓
➎ Supervised Learning → Linear Regression, Logistic Regression, Decision Trees, Random Forest
↓
➏ Unsupervised Learning → Clustering (K-Means, DBSCAN), PCA, Association Rules
↓
➐ Model Evaluation & Optimization → Cross-Validation, Hyperparameter Tuning, Metrics
↓
➑ Deep Learning & Advanced ML → Neural Networks, NLP, Time Series, Reinforcement Learning

Like for detailed explanation ❤️

Free Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

ENJOY LEARNING 👍👍

Step-by-Step Approach to Learn Python for Data Science

➊ Learn Python Basics → Syntax, Variables, Data Types (int, float, string, boolean)
↓
➋ Control Flow & Functions → If-Else, Loops, Functions, List Comprehensions
↓
➌ Data Structures & File Handling → Lists, Tuples, Dictionaries, CSV, JSON
↓
➍ NumPy for Numerical Computing → Arrays, Indexing, Broadcasting, Mathematical Operations
↓
➎ Pandas for Data Manipulation → DataFrames, Series, Merging, GroupBy, Missing Data Handling
↓
➏ Data Visualization → Matplotlib, Seaborn, Plotly
↓
➐ Exploratory Data Analysis (EDA) → Outliers, Feature Engineering, Data Cleaning
↓
➑ Machine Learning Basics → Scikit-Learn, Regression, Classification, Clustering

Free Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

ENJOY LEARNING 👍👍