📊Data ScientistIntermediate
Data Scientist Path
Master the complete data science toolkit from statistics to machine learning to communication. Learn to extract insights from data and drive business decisions. Covers the essential 95% of what data scientists do daily.
16 weeks
11 milestones
0 items
Skills You Will Gain
Python for Data ScienceSQL MasteryStatistics & ProbabilityMachine LearningData VisualizationFeature EngineeringA/B TestingCommunication Skills
Prerequisites
- →Basic programming knowledge
- →High school mathematics
- →Curiosity about data and analytics
- →Basic spreadsheet skills
Learning Milestones
1
Python for Data Science
Master Python and its data science ecosystem.
~20h•0 items
Learning Objectives
- ✓Write Pythonic code efficiently
- ✓Master NumPy for numerical computing
- ✓Use Pandas for data manipulation
- ✓Handle missing data and data cleaning
- ✓Work with dates, strings, and categorical data
- ✓Optimize Python code for performance
Content coming soon
2
SQL Mastery
Become proficient in SQL - used in 95% of data science roles.
~18h•0 items
Learning Objectives
- ✓Write complex queries with JOINs and subqueries
- ✓Use window functions for advanced analytics
- ✓Optimize query performance
- ✓Work with different database systems
- ✓Handle data aggregation and grouping
- ✓Build ETL queries for data pipelines
Content coming soon
3
Statistics & Probability
Build strong statistical foundations for data analysis.
~22h•0 items
Learning Objectives
- ✓Master descriptive statistics and distributions
- ✓Understand probability theory fundamentals
- ✓Perform hypothesis testing correctly
- ✓Calculate confidence intervals
- ✓Apply Bayesian thinking to problems
- ✓Avoid common statistical mistakes
Content coming soon
4
Data Visualization & Storytelling
Create compelling visualizations that drive decisions.
~15h•0 items
Learning Objectives
- ✓Master Matplotlib and Seaborn
- ✓Create interactive visualizations with Plotly
- ✓Design effective dashboards
- ✓Choose the right chart for the data
- ✓Tell stories with data
- ✓Present to technical and non-technical audiences
Content coming soon
5
Exploratory Data Analysis
Master the art of understanding data through exploration.
~15h•0 items
Learning Objectives
- ✓Develop systematic EDA workflows
- ✓Identify patterns and anomalies
- ✓Handle outliers appropriately
- ✓Understand feature distributions
- ✓Detect and handle data quality issues
- ✓Document and communicate findings
Content coming soon
6
Machine Learning Fundamentals
Master core ML algorithms used in industry.
~25h•0 items
Learning Objectives
- ✓Implement linear and logistic regression
- ✓Use decision trees and random forests
- ✓Apply gradient boosting (XGBoost, LightGBM)
- ✓Understand clustering algorithms
- ✓Handle imbalanced datasets
- ✓Perform proper train/validation/test splits
Content coming soon
7
Feature Engineering
Learn the art that often determines model success.
~18h•0 items
Learning Objectives
- ✓Create features from raw data
- ✓Handle categorical variables effectively
- ✓Engineer time-series features
- ✓Apply feature scaling and normalization
- ✓Perform feature selection
- ✓Build automated feature pipelines
Content coming soon
8
Model Evaluation & Selection
Learn to properly evaluate and select models.
~15h•0 items
Learning Objectives
- ✓Choose appropriate evaluation metrics
- ✓Implement cross-validation correctly
- ✓Perform hyperparameter tuning
- ✓Handle overfitting and underfitting
- ✓Compare models fairly
- ✓Build model selection pipelines
Content coming soon
9
A/B Testing & Experimentation
Design and analyze experiments to drive product decisions.
~15h•0 items
Learning Objectives
- ✓Design A/B tests properly
- ✓Calculate required sample sizes
- ✓Analyze test results correctly
- ✓Avoid common experimentation pitfalls
- ✓Handle multiple comparisons
- ✓Communicate experiment results
Content coming soon
10
Deep Learning for Data Scientists
Apply deep learning to real-world data science problems.
~20h•0 items
Learning Objectives
- ✓Build neural networks with PyTorch or TensorFlow
- ✓Apply CNNs for image problems
- ✓Use transfer learning effectively
- ✓Implement NLP with transformers
- ✓Handle tabular data with deep learning
- ✓Know when (not) to use deep learning
Content coming soon
11
GenAI for Data Scientists
Leverage LLMs and generative AI in data science workflows.
~15h•0 items
Learning Objectives
- ✓Use LLMs for data analysis tasks
- ✓Automate EDA with AI assistants
- ✓Generate synthetic data appropriately
- ✓Build AI-powered analytics tools
- ✓Understand limitations and risks
- ✓Stay current with GenAI developments
Content coming soon
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