WQU Data Science - Module 1
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1
WQU DS Module Introductory Video
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Unit I: 1.1.1 Basic Programming and Variables
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Unit I: 1.1.2 Functions
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Unit I: 1.1.3 Logic & Conditional Expressions
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Unit I: 1.1.4 Iteration & Conclusion
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Unit I: 1.2.1 Data Structures (list)
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Unit I: 1.2.2 Data Structures (tuple)
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Unit I: 1.2.3 Data Structures (set)
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Unit I: 1.2.4 Data Structures (dict)
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Unit I: 1.2.5 Data Structures (collections module)
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Unit I: 2.1.1 Algorithms and Complexity Analysis
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Unit I: 2.1.2 Computational/Time Complexity
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Unit I: 2.1.3 Recursion & Memoization
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Unit I: 2.1.4 Memory Complexity & Data Structures
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Unit I: 2.2.1 What is an Object?
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Unit I: 2.2.2 Introduction to Classes
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Unit I: 2.2.3 Defining Useful Classes
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Unit I: 2.2.4 Object-Oriented Programming & Inheritance
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Unit I: 2.3.1 VC Miniproject Intro & Hackerrank Coding Challenges
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Unit I: 3.1.1 Pythonic Programming
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Unit I: 3.1.2 Python Modules & Imports
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Unit I: 3.1.3 Exceptions & Debugging
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Unit I: 3.2.1 Basic Python IO
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Unit I: 3.2.2 File System Operations in Python
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Unit I: 3.2.3 CSV Files
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Unit I: 3.2.4 JSON Files
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Unit I: 3.2.5 Compression & GZip Files
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Unit I: 3.2.6 Serialization & Pickle Files
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Unit I: 3.3.1 PW Miniproject Intro
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Unit I: 4.1.1 Intro to NumPy
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Unit I: 4.1.2 Creating NumPy Arrays
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Unit I: 4.1.3 Advantages of NumPy
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Unit I: 4.1.4 Computation with NumPy
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Unit I: 4.1.5 Mutating NumPy Arrays
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Unit I: 4.1.6 Plotting with Matplotlib
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Unit I: 4.1.7 Intro to Pandas
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Unit I: 5.1.1 Pandas Overview
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Unit I: 5.1.2 Pandas IO
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Unit I: 5.1.3 Filtering DataFrames
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Unit I: 5.1.4 Data Transformations
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Unit I: 5.1.6 Joins & Multiple DataFrames
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Unit I: 5.1.5 Data Aggregation
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Unit I: 5.1.7 Time Series Data
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Unit I: 5.2.1 Intro to Statistics
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Unit I: 5.2.2 Estimating Means
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Unit I: 5.2.3 Standard Error of the Mean
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Unit I: 5.2.4 Hypothesis Testing & Z-Scores
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Unit I: 5.2.5 Parameter Estimation & Confidence Intervals
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Unit I: 5.3.1 DW Miniproject Intro
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Unit I: 6.1.1 Data Munging & Web Scraping
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Unit I: 6.1.2 Cleaning & Structing Data
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Unit I: 6.1.3 Dealing with Multiple Data Sources
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Unit I: 6.1.4 Data Munging in Pandas
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