Data Science Using Python

Data Science Using Python

Become a professional Data Scientist

 Features of Data Science Using Python

Data Science using Python offers powerful libraries like Pandas, NumPy, and Scikit-learn for data analysis, visualization, and machine learning. Its simplicity and versatility make it ideal for handling large datasets and building predictive models efficiently.

Course Pre-Requisites

Basic computer knowledge is required for this course.

Creativity & Novelty

Data Science with Python brings creativity and novelty by transforming raw data into smart, innovative insights.

Data Efficiency & Adaptability

Data Science with Python ensures efficiency and adaptability by processing large datasets quickly while seamlessly adjusting to diverse analytical needs.

Automation

Data Science with Python automates data processing and analysis, speeding up accurate insights with minimal effort.

CURRICULLUM – MODULES

Data Analytics Foundations

Module 1
  • Python for data analysis review
  • NumPy fundamentals
  • Statistical thinking
  • Basic data structures for analysis
  • Business metrics introduction

Pandas & Data Manipulation

Module 2
  • Advanced DataFrame operations
  • Data cleaning strategies
  • Missing data handling
  • Data validation techniques
  • Performance optimization

Exploratory Data Analytics

Module 3
  • Statistical measures
  • Distribution analysis
  • Correlation studies
  • Outlier detection
  • Initial data insights

Advanced Data Visualization

Module 4
  • Matplotlib mastery
  • Interactive Plotly dashboards
  • Seaborn for statistical visualization
  • Dashboard design principles
  • Storytelling with data

Business Statistics

Module 5
  • Hypothesis testing
  • A/B testing
  • Statistical significance
  • Regression analysis
  • Business metrics & KPIs

Time Series Analysis

Module 6
  • Time series components
  • Seasonal decomposition
  • Trend analysis
  • Forecasting methods
  • Financial analysis

Advanced Analytics & Reporting Automation

Module 7
  • Segmentation techniques
  • Clustering analysis
  • Dimensionality reduction
  • Feature engineering
  • Predictive analytics basics
  • Automated reporting
  • Data pipelines
  • ETL processes
  • Scheduling and monitoring
  • Quality assurance

Machine Learning Foundations

Module 8
  • ML ecosystem overview
  • Supervised vs Unsupervised learning
  • Scikit-learn fundamentals
  • Model evaluation metrics
  • ML workflow and pipelines

Advanced Feature Engineering

Module 9
  • Feature transformation
  • Feature scaling methods
  • Categorical encoding techniques
  • Automated feature selection
  • Dimensionality reduction advanced

Supervised Learning: Classification

Module 10
  • Logistic regression deep-dive
  • Decision trees and Random forests
  • Support Vector Machines
  • Gradient Boosting methods
  • Multi-class classification strategies

Supervised Learning: Regression

Module 11
  • Linear regression advanced concepts
  • Polynomial regression
  • Regularization techniques
  • Tree-based regression models
  • Time series regression

Ensemble Methods

Module 12
  • Bagging and boosting
  • Random forest optimization
  • XGBoost mastery
  • LightGBM and CatBoost
  • Stacking and blending

Unsupervised Learning

Module 13
  • Advanced clustering algorithms
  • Hierarchical clustering
  • Density-based clustering
  • Anomaly detection
  • Association rule learning

Deep Learning Fundamentals

Module 14
  • Neural network architecture
  • TensorFlow and Keras
  • CNNs and RNNs basics
  • Transfer learning
  • Model optimization

Model Deployment & MLOps

Module 15
  • Model serialization
  • API development (Flask/FastAPI)
  • Docker containers for ML
  • Model monitoring
  • CI/CD for ML projects

Course Includes

LIVE Interactive Sessions

Quizzes, Assignments & Projects

Study Materials & Recordings

Certificate

Skills You Will Learn

  • Perform advanced statistical analysis - apply hypothesis testing, regression analysis, and probabilistic modeling to extract meaningful insights from complex datasets and drive data-driven decisions:
  • Build predictive models using machine learning - develop classification and regression models, implement ensemble methods, and use techniques like cross-validation to ensure model reliability:
  • Create compelling data visualizations - master tools like Tableau, Plotly, and D3.js to build interactive dashboards and create impactful visual stories that communicate insights to stakeholders:
  • Design and analyze A/B tests - develop experimentation frameworks, calculate sample sizes, implement statistical tests, and derive actionable insights from experimental data:
  • Develop end-to-end data science solutions - from data collection and cleaning to model deployment and monitoring, build complete solutions that solve real business problems and deliver measurable ROI: