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)

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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.

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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

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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

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A-Z of essential data science concepts

A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.

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Data Science Learning Plan

Step 1: Mathematics for Data Science (Statistics, Probability, Linear Algebra)

Step 2: Python for Data Science (Basics and Libraries)

Step 3: Data Manipulation and Analysis (Pandas, NumPy)

Step 4: Data Visualization (Matplotlib, Seaborn, Plotly)

Step 5: Databases and SQL for Data Retrieval

Step 6: Introduction to Machine Learning (Supervised and Unsupervised Learning)

Step 7: Data Cleaning and Preprocessing

Step 8: Feature Engineering and Selection

Step 9: Model Evaluation and Tuning

Step 10: Deep Learning (Neural Networks, TensorFlow, Keras)

Step 11: Working with Big Data (Hadoop, Spark)

Step 12: Building Data Science Projects and Portfolio

Data Science Resources
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