Data Science with Python Table of Contents


Table of Content
 

Introduction to Data Science

  • Data Science Introduction and Use Cases
  • Data Science Roles and Lifecycle
  • Data Science Stages and Technologies
  • Data Science Technologies and Analytics
  • ML-Data and CRISP-DM

Statistical Techniques

  • Statistics and Experiments
  • Types of Data and Descriptive Statistics
  • Random Variables and Normal Distribution
  • Histograms and Normal Approximation
  • Central Limit Theorem
  • Probability Theory
  • Binomial Theory - Expected Value and Standard Error
  • Hypothesis Testing

Python for Data Science

  • Introduction to Python
  • Starting with Python with Jupyter Notebook
  • Python Variables and Conditions
  • Python Iterations 1
  • Python Iterations 2
  • Python Lists
  • Python Tuples
  • Python Dictionaries 1
  • Python Dictionaries 2
  • Python Sets 1
  • Python Sets 2
  • NumPy Arrays 1
  • NumPy Arrays 2
  • NumPy Arrays 3
  • Pandas Series 1
  • Pandas Series 2
  • Pandas Series 3
  • Pandas Series 4
  • Pandas DataFrame 1
  • Pandas DataFrame 2
  • Pandas DataFrame 3
  • Pandas DataFrame 4
  • Pandas DataFrame 5
  • Pandas DataFrame 6
  • Python User-Defined Functions
  • Python Lambda Functions
  • Python Lambda Functions and Date-Time Operations
  • Python String Operations

Exploratory Data Analysis (EDA)

  • Introduction to EDA
  • EDA Tools and Processes
  • EDA Project - 1
  • EDA Project - 2
  • EDA Project - 3
  • EDA Project - 4
  • EDA Project - 5
  • EDA Project - 6
  • EDA Project - 7

Machine Learning

  • Introduction to Machine Learning
  • Machine Learning Terminology
  • History of Machine Learning
  • Machine Learning Use Cases and Types
  • Role of Data in Machine Learning
  • Challenges in Machine Learning
  • Machine Learning Lifecycle and Pipelines
  • Regression Problems
  • Regression Models and Performance Metrics
  • Classification Problems and Performance Metrics
  • Optimizing Classification Metrics
  • Bias and Variance

Linear Regression

  • Linear Regression Introduction
  • Linear Regression - Training and Cost Function
  • Linear Regression - Cost Functions and Gradient Descent
  • Linear Regression - Practical Approach
  • Linear Regression - Feature Scaling and Cost Functions
  • Linear Regression OLS Assumptions and Testing
  • Linear Regression Car Price Prediction
  • Linear Regression Data Preparation and Analysis 1
  • Linear Regression Data Preparation and Analysis 2
  • Linear Regression Data Preparation and Analysis 3
  • Linear Regression Model Building
  • Linear Regression Model Evaluation and Optimization
  • Linear Regression Model Optimization

Logistic Regression

  • Logistic Regression Introduction
  • Logistic Regression - Logit Model
  • Logistic Regression - Telecom Churn Case Study
  • Logistic Regression - Data Analysis and Feature Engineering
  • Logistic Regression - Build the Logistic Model
  • Logistic Regression - Model Evaluation - AUC-ROC
  • Logistic Regression - Model Optimization
  • Logistic Regression - Model Optimization 2

Unsupervised Learning - K-Means Clustering

  • Unsupervised Learning - K-Means Clustering
  • K-Means Clustering Computation
  • K-Means Clustering Optimization
  • K-Means - Data Preparation and Modelling
  • K-Means - Model Optimization

Naive Bayes Probability Model

  • Naive Bayes Probability Model - Introduction
  • Naive Bayes Probability Computation
  • Naive Bayes - Employee Attrition Case Study
  • Naive Bayes - Model Building and Optimization

Classification using decision trees

  • Decision Tree - Model Concept
  • Decision Tree - Learning Steps
  • Decision Tree - Gini Index and Entropy Measures
  • Decision Tree - Hyperparameter Tuning
  • Decision Tree - Iris Dataset Case Study
  • Decision Tree - Model Optimization using Grid Search Cross Validation

Ensemble Methods – Random Forest

  • Random Forest - Ensemble Techniques Bagging and Random Forest
  • Random Forest Steps Pruning and Optimization
  • Random Forest - Model Building and Hyperparameter Tuning using Grid Search CV
  • Random Forest - Optimization Continued

Advanced Classification Techniques – Support Vector Machine

  • Support Vector Machine Concepts
  • Support Vector Machine Metrics and Polynomial SVM
  • Support Vector Machine Project 1
  • Support Vector Machine Predictions
  • Support Vector Machine - Classifying Polynomial Data

Dimensionality Reduction Using PCA

  • Principal Component Analysis - Concepts
  • Principal Component Analysis - Computations 1
  • Principal Component Analysis - Computations 2
  • Principal Component Analysis Practical

Dimensionality Reduction Using PCA

  • Principal Component Analysis - Concepts

Apply for certification

https://www.vskills.in/certification/data-science-with-python

 For Support