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Important questions
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UNIT 1
1.Comparing EDA with classical and Bayesian analysis
2.reshaping and pivoting, Grouping Datasets, data aggregation ,Pivot tables and cross-tabulations.
UNIT 2
1.Simple scatter plots,Line plots, three dimensional plotting
2.density and contour plots, Geographic Data with Basemap,Visualization with Seaborn
UNIT 3
1.Distributions and Variables
2.Scaling and Standardizing ,Inequality
UNIT 4
1.Relationships between Two Variables Percentage Tables
2.Scatterplots and Resistant Lines
UNIT 5
1.Fundamentals of TSA,characteristics od TSD
2.Visualizing , Grouping, Resampling
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Share it as link alone . don't share it as screenshot or any text material if u found this anywhere kindly report me . Don't #Admin :+6587090802*These questions are expected for the exams This may or may not be asked for exams .
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Thanks for your love and support guys keep supporting and share let the Engineers know about Us and leave a comment below for better improvements If there is any doubt feel free to ask me I will clear if I can or-else I will say some solutions ..get me through WhatsApp for instant updates ~$tuff$£ctorUNIT IEXPLORATORY DATA ANALYSISEDA fundamentals - Understanding data science - Significance of EDA - Making sense of data - Comparing EDA with classical and Bayesian analysis - Software tools for EDA - Visual Aids for EDA- Data transformation techniques-merging database, reshaping and pivoting. Transformation techniques - Grouping Datasets - data aggregation - Pivot tables and cross-tabulations.UNIT IIVISUALIZING USING MATPLOTLIBImporting Maplotlib - Simple line plots - Simple scatter plots - visualizing errors - density and contour plots - Histograms - legends - colors - subplots - text and annotation - customization - three dimensional plotting - Geographic Data with Basemap. Visualization with Seaborn.UNIT III UNIVARIATE ANALYSISIntroduction to Single variable: Distributions and Variables - Numerical Summaries of Level and Spread - Scaling and Standardizing - Inequality - Smoothing Time Series.UNIT IV BIVARIATE ANALYSISRelationships between Two VariablesPercentage Tables - Analyzing Contingency TablesHandling Several Batches - Scatterplots and Resistant Lines - Transformations.UNIT V MULTIVARIATE AND TIME SERIES ANALYSISIntroducing a Third Variable • Causal Explanations • Three-Variable Contingency Tables and Beyond - Longitudinal Data - Fundamentals of TSA - Characteristics of time series data - DataCleaning - Time-based indexing - Visualizing - Grouping - Resampling.
Thanks for your love and support guys keep supporting and share let the Engineers know about Us and leave a comment below for better improvements
If there is any doubt feel free to ask me I will clear if I can or-else I will say some solutions ..get me through WhatsApp for instant updates ~$tuff$£ctor
UNIT I
EXPLORATORY DATA ANALYSIS
EDA fundamentals - Understanding data science - Significance of EDA - Making sense of data - Comparing EDA with classical and Bayesian analysis - Software tools for EDA - Visual Aids for EDA- Data transformation techniques-merging database, reshaping and pivoting. Transformation techniques - Grouping Datasets - data aggregation - Pivot tables and cross-tabulations.
UNIT IIVISUALIZING USING MATPLOTLIBImporting Maplotlib - Simple line plots - Simple scatter plots - visualizing errors - density and contour plots - Histograms - legends - colors - subplots - text and annotation - customization - three dimensional plotting - Geographic Data with Basemap. Visualization with Seaborn.
UNIT III UNIVARIATE ANALYSIS
Introduction to Single variable: Distributions and Variables - Numerical Summaries of Level and Spread - Scaling and Standardizing - Inequality - Smoothing Time Series.
UNIT IV BIVARIATE ANALYSIS
Relationships between Two Variables
Percentage Tables - Analyzing Contingency Tables
Handling Several Batches - Scatterplots and Resistant Lines - Transformations.
UNIT V MULTIVARIATE AND TIME SERIES ANALYSIS
Introducing a Third Variable • Causal Explanations • Three-Variable Contingency Tables and Beyond - Longitudinal Data - Fundamentals of TSA - Characteristics of time series data - Data
Cleaning - Time-based indexing - Visualizing - Grouping - Resampling.