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Course Overview
Schedule of Classes
Course Curriculum
10 Subjects
SKT_DS_Theory_Anaconda Essentials
5 Learning Materials
Essential Programming Languages used for ML
Essential Programming Languages used for ML
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Anaconda
Introduction to Anaconda
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Installing Anaconda
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Anaconda Environment
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Jupyter Notebook
Jupyter Notebook
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SKT_DS_Theory_Pandas
50 Learning Materials
Introduction to Pandas
Introduction to Pandas
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Working with Pandas
Working with Pandas
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Pandas Series-1
Introduction to Series
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Creating Series from Lists
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Creating Series from Dictionary
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Creating Series from NumPy Array
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Creating a Series from Scalar Value
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Creating Series from NaN Values & Index Argument
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Pandas Series-2
Working with Series Indexing
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Accessing Data from Series
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Accessing Data from Series using Slicing
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Accessing Data from Series using loc & iloc
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Series Attributes-1
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Series Attributes-2
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Pandas Series-3
Sorting and Converting Type of Elements in Series
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Working with Null Values in Series
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Summarising Series-1
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Summarising Series-2
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Working with Duplicate Values
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Pandas Series - 4
Arithmetic Operations on Pandas Series
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Comparison / Relational Operations with Pandas Series - 1
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Comparison / Relational Operations with Pandas Series - 2
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Comparison / Relational Operations with Pandas Series - 3
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Logical Operators on Pandas Series
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Manipulating Pandas Series
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Pandas DataFrame - 1
Introduction to Pandas DataFrame
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Creating Pandas DataFrame
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DataFrame Attributes - 1
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DataFrame Attributes - 2
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Converting and Sorting Elements
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Working with Null Values in DataFrame
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Pandas DataFrame - 2
Summarising DataFrame - 1
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Summarising DataFrame - 2
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Working with Duplicates
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Arithmetic Operations on Pandas Dataframe - 1
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Arithmetic Operations on Pandas Dataframe - 2
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Pandas DataFrame - 3
Comparison Operations on Pandas Dataframe - 1
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Comparison Operations on Pandas Dataframe - 2
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Comparison Operations on Pandas Dataframe - 3
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Logical Operations on Pandas Dataframe - 1
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Logical Operations on Pandas Dataframe - 2
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Manipulating pandas Dataframe
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Pandas DataFrame - 4
Accessing Single Column in a DataFrame
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Accessing Multiple Columns
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Accessing Rows in a DataFrame
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Accessing Single Element in a DataFrame
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Reading Data from variuos types of files
Reading & Saving CSV File
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Reading & Saving Excel File
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Reading & Saving JSON File
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Reading Data from SQL Database
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SKT_DS_Lab L1_Pandas
2 Learning Materials
Basic Level Programming on Pandas Series-1
Write a code to create a Series from a NumPy array of random numbers and Print the Series.
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Create a Series with three NaN values, and specify index labels as 'A', 'B', and 'C'.
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Basic Level Programming on Pandas Series-2
Basic Level Programming on Pandas Series-3
Basic Level Programming on Pandas Series - 4
Basic Level Programming on Pandas DataFrame - 1
SKT_DS_Lab L2_Pandas
2 Learning Materials
Intermediate Level Programming on Pandas Series-1
Create a Series and set custom index labels in Pandas
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Create a Series with both positive and negative values and print its mean.
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Intermediate Level Programming on Pandas Series-2
Intermediate Level Programming on Pandas Series-3
Intermediate Level Programming on Pandas Series - 4
Intermediate Level Programming on Pandas DataFrame - 1
SKT_DS_Lab L3_Pandas
2 Learning Materials
Advance Level Programming on Pandas Series-1
Construct a Pandas Series from dictionary {'USA': 331, 'India': 1380, 'China': 1441, 'Brazil': 213}
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Python program using pandas library representing the marks of five students
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Advance Level Programming on Pandas Series-2
Advance Level Programming on Pandas Series-3
Advance Level Programming on Pandas Series - 4
Advance Level Programming on Pandas DataFrame - 1
SKT_DS_Theory_Data Analasys & Vis
143 Learning Materials
About Course
Course Overview
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Introduction to Data Analysis
What is Data Analysis?
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Why is Data Analysis important?
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Sources of Data
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Sampling Techniques
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Data Quality Issues
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Data Management & Storage
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Introduction to Data Visualization
Introduction to Data Visualization
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Data Wrangling
Introduction to Data Wrangling
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Subsetting a Dataset
Subsetting a Dataset
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Selecting Columns
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Selecting Rows
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Selecting Rows & Columns
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Slicing Columns
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Slicing Rows
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Slicing Rows & Columns
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Indexing
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Filtering
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Combining and Merging Data Sets
Database-Style Merging Datasets
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Merging on Index - Outer
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Merging on Index - Left
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Merging on Index - Right
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Concatenating Along an Axis
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Combining Data with Overlap
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Reshaping
Reshaping
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Data Transformation
Introduction for Data Transformation
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Removing Duplicates & Replacing Values
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Transforming Data Using a Function or Mapping
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Renaming Axis Indexes
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Data Discretization
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String Manipulation
String Manipulation
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Group By Operations
Introduction to Group By
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Exploring get_group
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Group by Syntax & Iterating Over Groups
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Grouping with Dicts and Series
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Grouping with Functions
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Grouping By Index Levels
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Data Aggregation
Introduction to Data Aggregation
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Syntax of Data Aggregation
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Column-wise and Multiple Function
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Returning Aggregated Data in "Unindexed" Form
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Group-wise Operations and Transformations
Apply: General split-apply-combine
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Quantile & Bucket Analysis
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Filling Missing Values with Group-specific Values
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Random Sampling & Permutation
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Random Sampling & Permutation using Group By
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Group Weighted Average and Correlation
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Pivot Tables and Cross-Tabulation
Pivot Table
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Cross Tabulation
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Data Preparation & Basic Models
Data Preparation
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Advanced Data Transformation Techniques
Linear Transformation
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Quadratic Transformation
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Non-polynomial Transformation
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Polynomial Transformation
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Rank Transformation
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Box-Cox Transformation
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Histogram
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Missing Values
Introduction to Data Cleaning
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Introduction to Missing Values
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Removing Missing Values
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Mean Imputation
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Median Imputation
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Mode Imputation
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Forward Fill Imputation
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Backward Fill Imputation
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Dealing with Noisy Data
Noisy Data
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Types of Noise Data
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Introduction to Noise Filtering
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Noise Filtering at Data Level
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Enhancing Data Analysis Strategies Against Noise
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Introduction to Outliers
Introduction to Outliers
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Detecting & Handling Outliers
Detecting outliers using Boxplot and IQR
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Detecting outliers using the Z-scores
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Detecting outliers using the percentile method
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Detecting outliers using Standard Deviation method
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Handling Categorical Data
Introduction to Categorical Data
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Categorical Encoding
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One Hot Encoding
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Label Encoding
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Ordinal Encoding
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Helmert Encoding
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Binary Encoding
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Frequency Encoding
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Mean Encoding
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Weight of Evidence Encoding
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Probability Ratio Encoding
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Hashing Encoding
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Backward Difference Encoding
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Leave One Out Encoding
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James Stein Encoding
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M-estimator Encoding
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Thermometer Encoder
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Which Encoding is best?
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Handling Numerical Data
Introduction to Numerical Data
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Introduction to Feature Scaling
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Min- Max Scaling
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Standardization
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Robust Scaling
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Max Abs Scaler
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Exploratory Data Analysis
Introduction to Exploratory Data Analysis
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Intoduction to Univariate Analysis
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Bar Plot for Univariate
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Pie Chart for Univariate
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Count Plot for Univariate
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Histogram for Univariate
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Box PLot for Univariate
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Density Plot for Univariate
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Line Plot for Univariate
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Intoduction to Bivariate Analysis
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Scatter Plot
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Line Plot
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Joint Plot
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Heatmap
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Stacked Bar Plot
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Grouped Bar Plot
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Clustered Heatmap
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Chi-Square Test
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Bar Plot
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Box Plot
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Violin Plot
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Point Plot
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Swarm Plot
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Introduction to Multivariate Analysis
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Heat Map for Multivariate
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Pair plot
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Feature Engineering
Introduction to Feature Engineering
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Feature Selection Techniques & Criteria
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Filter Feature Selection
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Wrapper Feature Selection
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Embedded Feature Selection
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Implications of Feature Selection Methods
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Representative Feature Selection Methods
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Leading and Recent Feature Selection Techniques
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Feature Extraction
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Feature Construction
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Experimental Comparative Analyses in Feature Selection
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Handling Imbalance Data
Introduction to Handle Imbalance Data
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Choose Proper Evaluation Metric
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Resampling
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SMOTE
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Balanced Bagging Classifier
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Threshold moving
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Augmentation
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SKT_DS_Lab L1_Data Analasys & Vis
4 Learning Materials
Basic Level Programming on Subsetting a Dataset
Generate the sample DataFrame and then choose any one of the Columns. Display its contents
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Creating and displaying Data. choose any one of the Row. Display its contents
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Basic Level Programming on Group By Operations
Group the DataFrame by a column and apply multiple aggregation functions
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Group the numbers in the num column they are even or odd and calculate the sum of each group data
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Basic Level Programming on Data Aggregation
Basic Level Programming on Group-wise Operations and Transformations
Basic Level Programming on Pivot Tables and Cross-Tabulation
SKT_DS_Lab L2_Data Analasys & Vis
2 Learning Materials
Intermediate Level Programming on Subsetting a Dataset
Construct a Sample DataFrame use the Slicing feature all columns whose names start with an ‘S’
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Intermediate Level Programming on Group By Operations
Given a list of dictionaries representing customers average order total for each city
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Intermediate Level Programming on Data Aggregation
Intermediate Level Programming on Group-wise Operations and Transformations
Intermediate Level Programming on Pivot Tables and Cross-Tabulation
SKT_DS_Lab L3_Data Analasys & Vis
3 Learning Materials
Advance Level Programming on Subsetting a Dataset
Write a Python Program to import any CSV file to Pandas DataFrame
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Advance Level Programming on Group By Operations
Load, explore a dataset , perform groupby() operations using pandas library and visualise data
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list of dictionaries representing employees group using Sorting higher salaries and longer.
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Advance Level Programming on Data Aggregation
Advance Level Programming on Group-wise Operations and Transformations
Advance Level Programming on Pivot Tables and Cross-Tabulation
SKT_DS_Theory_SQL for Data Science
52 Learning Materials
Introduction to Databases & RDBMS
Understanding Databases
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Database Management System
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Relational Database
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Non- Relational Database
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History
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Installing SQL
Setting Up MySQL Environment
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Database Processing Paradigms
OLAP vs. OLTP
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Introduction to SQL
SQL Fundamentals and Introduction
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SQL Syntax & Basic Queries
SQL Syntax & Basic Queries
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SQL Data Types
Introduction to Data Types
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Numeric Data Types
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String Data Type
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Date and Time Data Type
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Binary Data Type
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Miscellaneous Data Type
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SQL Operators
Introduction to Operators
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Arithmetic Operators
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Comparison operators
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Logical operators
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Bitwise Operators
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Compound Operators
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String Operators
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Set Operators
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SQL Basics and Language Components
SQL Commands
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Data Definition Language Commands
CREATE Database
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USE Database
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DROP Database
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ALTER Database
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CREATE Table
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RENAME Table
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DROP Table
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TRUNCATE Table
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Data Manipulation Language Commands
Retrieving data from a table
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Inserting data into a table
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Updating existing data into a table
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Deleting all records from a table
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Data Query Language Commands
Introduction to Clauses
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FROM Clause
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WHERE Clause
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WITH Clause
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ORDER BY Clause
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LIMIT Clause
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SQL Functions
Introduction to MYSQL Functions
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Numeric Functions
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String Functions
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Date Time Functions
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Conversion Functions
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User Defined Functions
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SQL GROUP BY , HAVING & Aggregate Functions
SQL Aggregation
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SQL Grouping - GROUP BY
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SQL Grouping - HAVING
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SQL Indexes
Creating Indexes
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Course Instructor
TBOCWWB
137 Courses • 108145 Students
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