Data scientist roadmap

Hi

OSSU  Data Science: Data Science 

A specialized path focused on the math, statistics, and programming skills needed for data science, using the best free courses available online. This is a path for those of you who want to complete the Data Science undergraduate curriculum on your own time, for free, with courses from the best universities in the World.

🎬 Video Lessons
📒 Reading Materials + Assignments
⏰ Duration: Possible to finish within 2 years
🏃‍♂️Self Paced
Created by 👨‍🏫: OSSU (Open Source Society University)

https://github.com/ossu/data-science

Ever wondered what the difference is between a Data Analyst and a Data Scientist? Both roles are in high demand, but they tackle data in different ways.

✅10 Most Useful SQL Interview Queries (with Examples) 💼

1️⃣ Find the second highest salary:
SELECT MAX(salary) 
FROM employees 
WHERE salary < (SELECT MAX(salary) FROM employees);


2️⃣ Count employees in each department:
SELECT department, COUNT(*) 
FROM employees 
GROUP BY department;


3️⃣ Fetch duplicate emails:
SELECT email, COUNT(*) 
FROM users 
GROUP BY email 
HAVING COUNT(*) > 1;


4️⃣ Join orders with customer names:
SELECT c.name, o.order_date 
FROM customers c 
JOIN orders o ON c.id = o.customer_id;


5️⃣ Get top 3 highest salaries:
SELECT DISTINCT salary 
FROM employees 
ORDER BY salary DESC 
LIMIT 3;


6️⃣ Retrieve latest 5 logins:
SELECT * FROM logins 
ORDER BY login_time DESC 
LIMIT 5;


7️⃣ Employees with no manager:
SELECT name 
FROM employees 
WHERE manager_id IS NULL;


8️⃣ Search names starting with ‘S’:
SELECT * FROM employees 
WHERE name LIKE 'S%';


9️⃣ Total sales per month:
SELECT MONTH(order_date) AS month, SUM(amount) 
FROM sales 
GROUP BY MONTH(order_date);


🔟 Delete inactive users:
DELETE FROM users 
WHERE last_active < '2023-01-01';


✅ Tip: Master subqueries, joins, groupings & filters – they show up in nearly every interview!

💬 Tap ❤️ for more!

✅ Data Science Fundamental Concepts You Should Know 📊🧠

1️⃣ Data Collection
Gathering raw data from various sources like databases, APIs, or web scraping for analysis.

2️⃣ Data Cleaning & Preprocessing
Preparing data by handling missing values, removing duplicates, correcting errors, and formatting for analysis.

3️⃣ Exploratory Data Analysis (EDA)
Using statistics and visualization to understand data patterns, trends, and detect outliers.

4️⃣ Statistical Inference
Drawing conclusions about populations using sample data through hypothesis testing, confidence intervals, and p-values.

5️⃣ Data Visualization
Creating charts and graphs (bar, line, scatter, histograms) to communicate insights clearly using tools like Matplotlib, Seaborn, or Tableau.

6️⃣ Feature Engineering
Transforming raw data into meaningful features that improve model performance, such as scaling, encoding and creating new variables.

7️⃣ Machine Learning Basics
Building predictive models by training algorithms on data:
⦁ Supervised Learning (regression, classification)
⦁ Unsupervised Learning (clustering, dimensionality reduction)

8️⃣ Model Evaluation
Assessing model accuracy using metrics like accuracy, precision, recall, F1 score (classification) and RMSE, MAE (regression).

9️⃣ Model Deployment
Putting your trained model into production so it can make real-time predictions or support decision-making.

🔟 Big Data & Tools
Handling large datasets using technologies like Hadoop, Spark, and databases such as SQL/NoSQL.

1️⃣1️⃣ Programming & Libraries
Essential coding skills in Python or R, with libraries like Pandas, NumPy, Scikit-learn for analysis and modeling.

1️⃣2️⃣ Data Ethics & Privacy
Ensuring responsible use of data, respecting privacy laws (GDPR), and avoiding biases in models.

💡 Tap ❤️ for more!

Data Science Roadmap
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus
| | |-- Probability and Statistics
| |
| |-- Programming
| | |-- Python
| | |-- R
| | |-- SQL
|
|-- Data Collection and Cleaning
| |-- Data Sources
| | |-- APIs
| | |-- Web Scraping
| | |-- Databases
| |
| |-- Data Cleaning
| | |-- Missing Values
| | |-- Data Transformation
| | |-- Data Normalization
|
|-- Data Analysis
| |-- Exploratory Data Analysis (EDA)
| | |-- Denoscriptive Statistics
| | |-- Data Visualization
| | |-- Hypothesis Testing
| |
| |-- Data Wrangling
| | |-- Pandas
| | |-- NumPy
| | |-- dplyr (R)
|
|-- Machine Learning
| |-- Supervised Learning
| | |-- Regression
| | |-- Classification
| |
| |-- Unsupervised Learning
| | |-- Clustering
| | |-- Dimensionality Reduction
| |
| |-- Reinforcement Learning
| | |-- Q-Learning
| | |-- Policy Gradient Methods
| |
| |-- Model Evaluation
| | |-- Cross-Validation
| | |-- Performance Metrics
| | |-- Hyperparameter Tuning
|
|-- Deep Learning
| |-- Neural Networks
| | |-- Feedforward Networks
| | |-- Backpropagation
| |
| |-- Advanced Architectures
| | |-- Convolutional Neural Networks (CNN)
| | |-- Recurrent Neural Networks (RNN)
| | |-- Transformers
| |
| |-- Tools and Frameworks
| | |-- TensorFlow
| | |-- PyTorch
|
|-- Natural Language Processing (NLP)
| |-- Text Preprocessing
| | |-- Tokenization
| | |-- Stop Words Removal
| | |-- Stemming and Lemmatization
| |
| |-- NLP Techniques
| | |-- Word Embeddings
| | |-- Sentiment Analysis
| | |-- Named Entity Recognition (NER)
|
|-- Data Visualization
| |-- Basic Plotting
| | |-- Matplotlib
| | |-- Seaborn
| | |-- ggplot2 (R)
| |
| |-- Interactive Visualization
| | |-- Plotly
| | |-- Bokeh
| | |-- Dash
|
|-- Big Data
| |-- Tools and Frameworks
| | |-- Hadoop
| | |-- Spark
| |
| |-- NoSQL Databases
| |-- MongoDB
| |-- Cassandra
|
|-- Cloud Computing
| |-- Cloud Platforms
| | |-- AWS
| | |-- Google Cloud
| | |-- Azure
| |
| |-- Data Services
| |-- Data Storage (S3, Google Cloud Storage)
| |-- Data Pipelines (Dataflow, AWS Data Pipeline)
|
|-- Model Deployment
| |-- Serving Models
| | |-- Flask/Django
| | |-- FastAPI
| |
| |-- Model Monitoring
| |-- Performance Tracking
| |-- A/B Testing
|
|-- Domain Knowledge
| |-- Industry-Specific Applications
| | |-- Finance
| | |-- Healthcare
| | |-- Retail
|
|-- Ethical and Responsible AI
| |-- Bias and Fairness
| |-- Privacy and Security
| |-- Interpretability and Explainability
|
|-- Communication and Storytelling
| |-- Reporting
| |-- Dashboarding
| |-- Presentation Skills
|
|-- Advanced Topics
| |-- Time Series Analysis
| |-- Anomaly Detection
| |-- Graph Analytics
| |-- *PH4N745M*
└-- Comments
|-- # Single-line comment (Python)
└-- /* Multi-line comment (Python/R) */

Python book for machine learning

What is the difference between data scientist, data engineer, data analyst and business intelligence?

🧑🔬 Data Scientist
Focus: Using data to build models, make predictions, and solve complex problems.
Cleans and analyzes data
Builds machine learning models
Answers “Why is this happening?” and “What will happen next?”
Works with statistics, algorithms, and coding (Python, R)
Example: Predict which customers are likely to cancel next month

🛠️ Data Engineer
Focus: Building and maintaining the systems that move and store data.
Designs and builds data pipelines (ETL/ELT)
Manages databases, data lakes, and warehouses
Ensures data is clean, reliable, and ready for others to use
Uses tools like SQL, Airflow, Spark, and cloud platforms (AWS, Azure, GCP)
Example: Create a system that collects app data every hour and stores it in a warehouse

📊 Data Analyst
Focus: Exploring data and finding insights to answer business questions.
Pulls and visualizes data (dashboards, reports)
Answers “What happened?” or “What’s going on right now?”
Works with SQL, Excel, and tools like Tableau or Power BI
Less coding and modeling than a data scientist
Example: Analyze monthly sales and show trends by region

📈 Business Intelligence (BI) Professional
Focus: Helping teams and leadership understand data through reports and dashboards.
Designs dashboards and KPIs (key performance indicators)
Translates data into stories for non-technical users
Often overlaps with data analyst role but more focused on reporting
Tools: Power BI, Looker, Tableau, Qlik
Example: Build a dashboard showing company performance by department

🧩 Summary Table
Data Scientist - What will happen? Tools: Python, R, ML tools, predictions & models
Data Engineer - How does the data move and get stored? Tools: SQL, Spark, cloud tools, infrastructure & pipelines
Data Analyst - What happened? Tools: SQL, Excel, BI tools, reports & exploration
BI Professional - How can we see business performance clearly? Tools: Power BI, Tableau, dashboards & insights for decision-makers

🎯 In short:
Data Engineers build the roads.
Data Scientists drive smart cars to predict traffic.
Data Analysts look at traffic data to see patterns.
BI Professionals show everyone the traffic report on a screen.

Data Science Techniques