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 Course 2: Data Science using Python(Syllabus)-Few classes include the application of pythons and opportunities in python.-The course includes the reading materials and many other resources to boost the learning.1. IntroductionThe Ascendance of DataWhat Is Data Science?Motivating Hypothetical: DataSciencesterFinding Key ConnectorsData Scientists You May KnowSalaries and ExperiencePaid Accounts2. A Crash Course in PythonThe BasicsGetting PythonThe Zen of PythonWhitespace FormattingModulesArithmeticFunctionsStringsExceptionsListsTuplesDictionariesSetsControl FlowTruthinessThe Not-So-BasicsSortingList ComprehensionsGenerators and IteratorsRandomnessRegular ExpressionsObject-Oriented ProgrammingFunctional Toolsenumeratezip and Argument Unpackingargs and kwargsWelcome to DataSciencester!3. Introductory dataCounting Time Zones in Pure PythonCounting Time Zones with pandasMovieLens 1M Data SetMeasuring rating disagreementUS Baby Names 1880-2010Analysing Naming TrendsSimulating Many Random Walks at Once4. StatisticsDescribing a Single Set of DataCentral TendenciesDispersionCorrelationSimpson’s ParadoxSome Other Correlational CaveatsCorrelation and CausationExamples5. ProbabilityDependence and IndependenceConditional ProbabilityBayes’s TheoremRandom VariablesContinuous DistributionsThe Normal DistributionThe Central Limit Theorem6. Hypothesis and InferenceStatistical Hypothesis TestingExample: Flipping a CoinConfidence IntervalsP-hackingExample: Running an A/B TestBayesian Inference7. NumPy Basics: Arrays and Vectorized ComputationThe NumPy ndarray: A Multidimensional Array ObjectCreating ndarraysData Types for ndarraysOperations between Arrays and ScalarsBasic Indexing and SlicingBoolean IndexingFancy IndexingTransposing Arrays and Swapping AxesUniversal Functions: Fast Element-wise Array FunctionsData Processing Using ArraysExpressing Conditional Logic as Array OperationsMathematical and Statistical MethodsMethods for Boolean ArraysSortingUnique and Other Set LogicFile Input and Output with ArraysStoring Arrays on Disk in Binary FormatSaving and Loading Text FilesLinear AlgebraRandom Number GenerationExample: Random Walks8. Getting Started with pandasIntroduction to pandas Data StructuresSeriesDataFrameIndex ObjectsEssential FunctionalityReindexingDropping entries from an axisIndexing, selection, and filteringArithmetic and data alignmentFunction application and mappingSorting and rankingAxis indexes with duplicate valuesSummarizing and Computing Descriptive StatisticsCorrelation and CovarianceUnique Values, Value Counts, and MembershipHandling Missing DataFiltering Out Missing DataFilling in Missing DataHierarchical IndexingReordering and Sorting LevelsSummary Statistics by LevelUsing a DataFrame’s ColumnsOther pandas TopicsInteger IndexingPanel Data9. Data Loading, Storage, and File FormatsReading and Writing Data in Text FormatReading Text Files in PiecesWriting Data Out to Text FormatManually Working with Delimited FormatsJSON DataXML and HTML: Web ScrapingBinary Data FormatsUsing HDF5 FormatReading Microsoft Excel FilesInteracting with HTML and Web APIsInteracting with DatabasesStoring and Loading Data in MongoDB10. Data Wrangling: Clean, Transform, Merge, ReshapeCombining and Merging Data SetsDatabase-style DataFrame MergesMerging on IndexConcatenating Along an AxisCombining Data with OverlapReshaping and PivotingReshaping with Hierarchical IndexingPivoting “long” to “wide” FormatData TransformationRemoving DuplicatesTransforming Data Using a Function or MappingReplacing ValuesRenaming Axis IndexesDiscretization and BinningDetecting and Filtering OutliersPermutation and Random SamplingComputing Indicator/Dummy VariablesString ManipulationString Object MethodsRegular expressionsVectorized string functions in pandasExample: USDA Food Database11. Plotting and VisualizationA Brief matplotlib API PrimerFigures and SubplotsColors, Markers, and Line StylesTicks, Labels, and LegendsAnnotations and Drawing on a SubplotSaving Plots to Filematplotlib ConfigurationPlotting Functions in pandasLine PlotsBar PlotsHistograms and Density PlotsScatter PlotsPlotting Maps: Visualizing Haiti Earthquake Crisis DataPython Visualization Tool EcosystemChacoMayaviOther PackagesThe Future of Visualization Tools?12. Data Aggregation and Group OperationsGroupBy MechanicsIterating Over GroupsSelecting a Column or Subset of ColumnsGrouping with Dicts and SeriesGrouping with FunctionsGrouping by Index LevelsData AggregationColumn-wise and Multiple Function ApplicationReturning Aggregated Data in “unindexed” FormGroup-wise Operations and TransformationsApply: General split-apply-combineQuantile and Bucket AnalysisExample: Filling Missing Values with Group-specific ValuesExample: Random Sampling and PermutationExample: Group Weighted Average and CorrelationExample: Group-wise Linear RegressionPivot Tables and Cross-TabulationCross-Tabulations: CrosstabExample: 2012 Federal Election Commission DatabaseDonation Statistics by Occupation and EmployerBucketing Donation AmountsDonation Statistics by State13. Time SeriesDate and Time Data Types and ToolsConverting between string and DateTimeTime Series BasicsIndexing, Selection, SubsettingTime Series with Duplicate IndicesDate Ranges, Frequencies, and ShiftingGenerating Date RangesFrequencies and Date OffsetsShifting (Leading and Lagging) DataTime Zone HandlingLocalization and ConversionOperations with TimeZone-aware Timestamp ObjectsOperations between Different Time ZonesPeriods and Period ArithmeticPeriod Frequency ConversionQuarterly Period FrequenciesConverting Timestamps to Periods (and Back)Creating a PeriodIndex from ArraysResampling and Frequency ConversionDownsamplingUpsampling and InterpolationResampling with PeriodsTime Series PlottingMoving Window FunctionsExponentially-weighted functionsBinary Moving Window FunctionsUser-Defined Moving Window FunctionsPerformance and Memory Usage Notes14. Financial and Economic Data ApplicationsData Munging TopicsTime Series and Cross-Section AlignmentOperations with Time Series of Different FrequenciesTime of Day and “as of” Data SelectionSplicing Together Data SourcesReturn Indexes and Cumulative ReturnsGroup Transforms and AnalysisGroup Factor ExposuresDecile and Quartile AnalysisMore Example ApplicationsSignal Frontier AnalysisFuture Contract RollingRolling Correlation and Linear Regression15. Advanced NumPyndarray Object InternalsNumPy dtype HierarchyAdvanced Array ManipulationReshaping ArraysC versus Fortran OrderConcatenating and Splitting ArraysRepeating Elements: Tile and RepeatFancy Indexing Equivalents: Take and PutBroadcastingBroadcasting Over Other AxesSetting Array Values by BroadcastingAdvanced ufunc Usageufunc Instance MethodsCustom ufuncsStructured and Record ArraysNested dtypes and Multidimensional FieldsWhy Use Structured Arrays?Structured Array Manipulations: numpy.lib.recfunctionsMore About SortingIndirect Sorts: argsort and lexsortAlternate Sort Algorithmsnumpy.searchsorted: Finding elements in a Sorted ArrayNumPy Matrix ClassAdvanced Array Input and OutputMemory-mapped FilesHDF5 and Other Array Storage OptionsPerformance TipsThe Importance of Contiguous MemoryOther Speed Options: Cython, f2py, C16. Regex(Introduction to NLP) and other application of PythonsThe Course includes the group as well as individual real-life projects1-Project 1: Individual Project2-Project 2: Group Project3-Project 3: Group ProjectOther things include course certificate and Experience letter ................
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