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📊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.

Content Summary

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Concepts
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Papers
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Problems
~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