Data Scientist Roadmap

Beginner

"Learning the language of data."

The Beginner level is the phase of building foundations. At this stage, the main focus is not creating Machine Learning models, but understanding how data is represented, processed, and analyzed.

Things that are usually learned include:

• Basics of statistics and probability

• Microsoft Excel for data analysis

• Basic Python (variables, functions, loops, simple OOP)

• Basic libraries such as NumPy and Pandas

• Data Visualization (Matplotlib, Seaborn)

• Basic SQL

• How to read datasets

• Concepts of data, feature, target, and label

Mindset that starts to form:

"I can start reading data."

Intermediate

"Learning to understand data."

At this level, someone begins to be able to process data independently and find answers to a business question. The focus shifts from learning tools to learning analytical thinking.

The material generally learned:

• Advanced SQL (JOIN, CTE, Window Function)

• Data Wrangling

• Exploratory Data Analysis (EDA)

• Basic Feature Engineering

• Dashboards and visualization

• Basic Machine Learning

• Supervised vs Unsupervised Learning

• Model validation

• Git and basic version control

Start getting used to working on end-to-end projects:

• Collecting data

• Cleaning data

• Analyzing

• Building a simple model

• Explaining the results

Mindset that starts to form:

"I can find patterns in data."

Advance

"Learning to solve real-world problems."

At this stage, someone is no longer just running algorithms, but understanding the reasons behind method selection and being able to build solutions that can be used by the company.

Skills that begin to be mastered:

• Advanced statistics

• Advanced Machine Learning

• Hyperparameter Tuning

• Cross Validation

• Feature Selection

• Advanced Feature Engineering

• Model Explainability (SHAP, LIME)

• Time Series

• NLP or Computer Vision (optional)

• APIs using FastAPI/Flask

• Docker

• Basic cloud

• Model deployment

• Model monitoring

Begin to be able to question:

• Why did the model fail?

• Why is this feature important?

• Why is precision more important than accuracy?

Mindset that starts to form:

"I can choose the best solution, not just the best model."

Expert

"Learning to create business impact."

At this level, a person's focus changes significantly. Machine Learning is no longer the main goal, but a tool for solving business problems.

Topics that are usually started to be mastered:

• Business Understanding

• Experiment Design (A/B Testing)

• Product Analytics

• MLOps

• CI/CD for Machine Learning

• Data Pipeline

• Model Monitoring

• Data Governance

• Cost Optimization

• Stakeholder Management

• Technical Leadership

• Mentoring

• AI Strategy

An Expert does not only ask:

"Which model is the most accurate?"

But also asks:

• Is Machine Learning actually needed?

• How much does it cost to run this model?

• Does this model provide ROI?

• Can this solution be maintained for five years?

• How will other teams use the results?

Mindset that starts to form:

"I use data to create business impact."