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Basic Python
27-
Introduction to PythonLecture1.1
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Python – The Universal LanguageLecture1.2
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Installing PythonLecture1.3
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Python – *Hello World*Lecture1.4
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Using the InterpreterLecture1.5
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iPython – a better Python interpreterLecture1.6
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Types – Dynamic v/s Static Typing – tstrong v/s Weak TypingLecture1.7
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NumbersLecture1.8
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StringsLecture1.9
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UnicodeLecture1.10
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Complex TypesLecture1.11
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Operators – Operator OverloadingLecture1.12
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VariablesLecture1.13
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Scopping And ExpressionsLecture1.14
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Use of tabs and whitespaces as indentLecture1.15
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Conditionals – for…elseLecture1.16
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The general syntaxLecture1.17
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Default values for argumentsLecture1.18
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Returning and receiving multiple valuesLecture1.19
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Variable number of arguments – args, kwargsLecture1.20
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Scope revisitedLecture1.21
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ListsLecture1.22
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TuplesLecture1.23
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Maps (or Dictionaries)Lecture1.24
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SetsLecture1.25
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EnumsLecture1.26
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Looping TechniquesLecture1.27
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Data Analysis with Python
26-
Installing Anaconda and Jupyter NotebookLecture2.1
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Jupyter Notebook overviewLecture2.2
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Importing data from a csv, text file or another databaseLecture2.3
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Overview of various methods and attributes of NumpyLecture2.4
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ArrayLecture2.5
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Creating 1 dimensional arraysLecture2.6
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Creating 2 dimensional arraysLecture2.7
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Array indexingLecture2.8
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Accessing array elementsLecture2.9
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Concatenating Numpy arraysLecture2.10
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Arithmetic operations with arraysLecture2.11
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CovarianceLecture2.12
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CorrelationLecture2.13
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Linear RegressionLecture2.14
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Overview of various methods and attributes of PandasLecture2.15
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IntroductionLecture2.16
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Working with various Series attributesLecture2.17
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IntroductionLecture2.18
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PivotingLecture2.19
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SortingLecture2.20
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AggregationLecture2.21
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Descriptive statistical analysis with PandasLecture2.22
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Introduction to MatplotlibLecture2.23
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Various Matplotlib methodsLecture2.24
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Creating Line, Scatter, Bar, etc. chartsLecture2.25
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Customising charts using: X and Y labels, Limits, Ticks, LegendsLecture2.26
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