Data Science

Course Content

1. Basic Python

Python Introduction & Installation (Anaconda)

Basic Operation:

• Addition
  • Subtraction
  • Multiplication
  • Division. etc

Types of Data & Manipulation:

• List
  • Tuple
    • Set
    • Dictionaries
    • String
    • Numeric
    • Float
    • Boolean. Etc

Loops:

• For
  • While
  • If-else-if

NumPy Library & Operation:

• Introduction to NumPy
  • Array Creation
  • Array Indexing
  • Array Slicing
  • Array Manipulation

Pandas Library & Operation:

• Introduction to Pandas
  • Read Different Type of Dataset
  • DataFrames
  • Groupby, Merging, joining, & Concatenating.
  • Missing Data. etc

Matplot & Seaborn Library & Operation:

• Line, bar chart
  • Scatter chart
  • Pie chart
  • Box plot
  • Countplot
  • Jointplot
  • Pairplot Etc.

2. Statistics & Probability:

• Types of Data,
• Random Variable.
• Mean, Mode, MedianVariance,
• range & Std Deviation
• Central Limit Theorem

Probability & Probability Distribution: 

• Introduction of Probability.
  • Conditional Probability.
  • Binomial Distribution.
  • Poisson Distribution.
  • Normal Distribution.

Hypothesis Testing:

• Introduction of Hypothesis (Null Hypothesis & Alternative Hypothesis)
  • Type 1 Error & Type 2 Error
  • T-test
  • Z-test
  • ANOVA
  • F-test
  • Chi- Square Test.
  • P-value

Correlation & Covariance:

• Introduction of Correlation.       
  • Positive & Negative Correlation & Covariance.
  • Multi-Collinearity
  • VIF

3. Machine Learning:

Feature Engineering

• Handling Missing Value.
• Handling Outliers
• Handling Imbalance Data
• SMOTE
• Feature Scaling
• Standardization
• Normalization.
• Min-Max Scaling
• Data Encoding.
• PCA (Principle Component Analysis)

Feature Selection:

• Feature Selection.
• Forward Elimination.
• Backward Elimination

Exploratory Data Analysis

Regression:

• Linear Regression.
• Gradient Descent.
• Multiple Linear Regression.
• Polynomial Regression.
• R Square & Adjusted R Square.
• RMSE, MSE, MAE.
• Ridge Regression.
• Lasso Regression.
• Concept of Overfitting & Underfitting.

Logistic Regression:

• Introduction to Logistic Regression.
• Confusion Matrix.
• Precision, Recall, F1 Score, ROC Curve, AUC Curve.

Support Vector Machines:

• Introduction to Support Vector Machine.
• Confusion Matrix.
• Precision, Recall, F1 Score, ROC Curve, AUC Curve.

Naïve Bayes:

• Introduction to Support Vector Machine.
• Confusion Matrix.
• Precision, Recall, F1 Score, ROC Curve, AUC Curve.

KNN-Classification:

• Introduction to KNN Classification.
• Confusion Matrix.
• Precision, Recall, F1 Score, ROC Curve, AUC Curve.

Decision Tree:

• Introduction to Decision Tree.
• Confusion Matrix.
• Precision, Recall, F1 Score, ROC Curve, AUC Curve.

Random Forest:

• Introduction to Decision Tree.
• Confusion Matrix.
• Precision, Recall, F1 Score, ROC Curve, AUC Curve.

Clustering:

• K-means Clustering.
• K-Means++.
• Hierarchical Clustering.