Lesson 1 — Python Basics for Data Science (Animated) - Synkoc
Data Science Internship Lesson 1 — Python Basics for Data Science (Animated)
Data Science Internship
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Week 1 — Python & Data Wrangling
Lesson 1 — Python Basics for Data Science (Animated)
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Lesson 1 — Python Basics for Data Science (Theory)
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Lesson 1 — Python Basics for Data Science (Lab)
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Lesson 2 — NumPy for Numerical Computing (Animated)
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Lesson 2 — NumPy for Numerical Computing (Theory)
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Lesson 2 — NumPy for Numerical Computing (Lab)
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Lesson 3 — pandas for Data Manipulation (Animated)
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Lesson 3 — pandas for Data Manipulation (Theory)
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Lesson 3 — pandas for Data Manipulation (Lab)
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Lesson 4 — Data Cleaning & Preparation (Animated)
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Lesson 4 — Data Cleaning & Preparation (Theory)
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Lesson 4 — Data Cleaning & Preparation (Lab)
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Week 2 — Statistics & Exploratory Data Analysis
Lesson 1 — Descriptive Statistics (Animated)
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Lesson 1 — Descriptive Statistics (Theory)
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Lesson 1 — Descriptive Statistics (Lab)
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Lesson 2 — Data Visualisation with matplotlib & seaborn (Animated)
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Lesson 2 — Data Visualisation with matplotlib & seaborn (Theory)
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Lesson 2 — Data Visualisation with matplotlib & seaborn (Lab)
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Lesson 3 — Hypothesis Testing & A/B Tests (Animated)
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Lesson 3 — Hypothesis Testing & A/B Tests (Theory)
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Lesson 3 — Hypothesis Testing & A/B Tests (Lab)
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Lesson 4 — EDA Walkthrough on Real Data (Animated)
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Lesson 4 — EDA Walkthrough on Real Data (Theory)
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Lesson 4 — EDA Walkthrough on Real Data (Lab)
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Week 3 — Supervised ML I
Lesson 1 — ML Workflow & Train/Test Split (Animated)
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Lesson 1 — ML Workflow & Train/Test Split (Theory)
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Lesson 1 — ML Workflow & Train/Test Split (Lab)
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Lesson 2 — Linear Regression (Animated)
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Lesson 2 — Linear Regression (Theory)
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Lesson 2 — Linear Regression (Lab)
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Lesson 3 — Logistic Regression (Animated)
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Lesson 3 — Logistic Regression (Theory)
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Lesson 3 — Logistic Regression (Lab)
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Lesson 4 — Decision Trees (Animated)
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Lesson 4 — Decision Trees (Theory)
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Lesson 4 — Decision Trees (Lab)
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Week 4 — Supervised ML II
Lesson 1 — Decision Trees Deep Dive (Animated)
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Lesson 1 — Decision Trees Deep Dive (Theory)
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Lesson 1 — Decision Trees Deep Dive (Lab)
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Lesson 2 — Random Forests (Animated)
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Lesson 2 — Random Forests (Theory)
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Lesson 2 — Random Forests (Lab)
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Lesson 3 — Feature Engineering (Animated)
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Lesson 3 — Feature Engineering (Theory)
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Lesson 3 — Feature Engineering (Lab)
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Lesson 4 — Regularisation & Hyperparameter Tuning (Animated)
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Lesson 4 — Regularisation & Hyperparameter Tuning (Theory)
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Lesson 4 — Regularisation & Hyperparameter Tuning (Lab)
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Week 5 — Unsupervised & Deep Learning
Lesson 1 — K-Means Clustering (Animated)
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Lesson 1 — K-Means Clustering (Theory)
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Lesson 1 — K-Means Clustering (Lab)
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Lesson 2 — PCA — Principal Component Analysis (Animated)
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Lesson 2 — PCA — Principal Component Analysis (Theory)
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Lesson 2 — PCA — Principal Component Analysis (Lab)
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Lesson 3 — Introduction to Neural Networks (Animated)
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Lesson 3 — Introduction to Neural Networks (Theory)
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Lesson 3 — Introduction to Neural Networks (Lab)
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Lesson 4 — End-to-End ML Pipeline (Animated)
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Lesson 4 — End-to-End ML Pipeline (Theory)
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Lesson 4 — End-to-End ML Pipeline (Lab)
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Week 6 — Capstone Project
Lesson 1 — Customer Churn Capstone — End-to-End (Animated)
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Lesson 1 — Customer Churn Capstone — End-to-End (Theory)
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Lesson 1 — Customer Churn Capstone — End-to-End (Lab)
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Bonus — Career Awareness
Lesson 1 — Data Science Resume Writing (Animated)
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Lesson 1 — Data Science Resume Writing (Theory)
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Lesson 1 — Data Science Resume Writing (Lab)
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Lesson 2 — GitHub Portfolio Polish (Animated)
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Lesson 2 — GitHub Portfolio Polish (Theory)
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Lesson 2 — GitHub Portfolio Polish (Lab)
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Lesson 3 — ML Interview Preparation (Animated)
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Lesson 3 — ML Interview Preparation (Theory)
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Lesson 3 — ML Interview Preparation (Lab)
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