Data Science with Python Training

Data Science with Python Training Program

Course Overview

This comprehensive Data Science training program provides a strong foundation in Python programming, statistics, machine learning, and advanced analytics techniques. The course emphasizes hands-on learning, real-world case studies, and model deployment.

Course Duration & Mode
  • Total Duration: 40–50 Hours (customizable)
  • Mode: Instructor-led / Online / Classroom
Learning Outcomes

By the end of this course, participants will be able to:

  • Understand core data science concepts and workflows
  • Perform data preprocessing and exploratory data analysis (EDA)
  • Apply statistical and machine learning algorithms
  • Build predictive models and evaluate performance
  • Work with NLP, time series, and deep learning concepts
  • Deploy machine learning models using Flask
Course Modules

Introduction to Python

  • Why Python for Data Science
  • Python Basics and Environment Setup
  • Python 2 vs Python 3
  • Data Types and Variables
  • Control Statements and Loops
  • List Comprehensions
  • Functions and Methods
  • Python Libraries Overview
  • Git & GitHub Basics
  • Hands-on Practice

Statistics for Data Science

  • Introduction to Statistics
  • Types and Areas of Statistics
  • Sampling Techniques
  • Measures of Central Tendency & Dispersion
  • Probability Concepts
  • Covariance and Correlation
  • Collinearity
  • Hypothesis Testing
  • Chebyshev Theorem

Introduction to Data Science

  • What is Data Science
  • Data Science Lifecycle
  • Types of Data Science Problems
  • Case Studies
  • Exploratory Data Analysis (EDA)
  • Missing Value Analysis
  • Outlier Detection
  • Confusion Matrix
  • Precision, Recall, and F1 Score
  • Mean Absolute Error & Mean Squared Error

Exploratory Data Analysis (EDA) & Feature Engineering

  • Feature Selection
  • Importance of Feature Selection
  • Feature Selection Techniques
  • Correlation Matrix
  • Feature Importance Methods
  • Dimensionality Reduction Techniques
  • Principal Component Analysis (PCA)
  • Feature Scaling (Normalization & Standardization)
  • When and why to scale features

Introduction to Machine Learning

  • What is Machine Learning
  • Types of Machine Learning
    • Supervised Learning
    • Unsupervised Learning
  • Decision Trees and ID3 Algorithm
  • Random Forests
  • Model Building and Evaluation

Advanced Machine Learning

  • Statistical Models vs ML Models
  • Regression Techniques
    • Linear Regression
    • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Naïve Bayes
  • Model Evaluation Metrics:
    • Accuracy
    • Log Loss
    • Confusion Matrix
    • AUC (Area Under Curve)

Natural Language Processing (NLP)

  • Introduction to NLP
  • Components and Benefits of NLP
  • NLP Libraries (NLTK)
  • Text Processing Techniques:
    • Tokenization
    • Stemming & Lemmatization
    • Synonyms & Antonyms
  • Word Embeddings
  • Part-of-Speech Tagging
  • Named Entity Recognition
  • Sentiment Analysis
  • Semantic Similarity
  • Text Summarization
  • Language Detection

Time Series Analysis

  • Introduction to Time Series
  • Applications of Time Series
  • Components of Time Series
  • Stationarity
  • ARIMA Model
  • Forecasting Techniques
  • Practical Demo: Future Prediction

Deploying Machine Learning Models

  • Introduction to Model Deployment
  • Deploying Models using Flask
  • Flask Setup and Libraries
  • Data Preprocessing for Deployment
  • Model Integration
  • Creating HTML Forms
  • Running Flask Applications
  • Folder Structure and Best Practices
  • End-to-End Deployment Demo

Introduction to Deep Learning

  • Fundamentals of Deep Learning
  • Limitations of Traditional Machine Learning
  • Deep Learning Use Cases
  • Artificial Neural Networks (ANN)
  • Activation Functions
  • Perceptron Model
  • Hands-on Practice