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Data Science

Transform Data into Actionable Insights

Master the complete data science pipeline from data collection to machine learning model deployment. Learn Python, statistics, ML algorithms, and become a certified Data Scientist.

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Duration

9 Months

Minimum Training Period

Certification

Industry

Recognized Certificate

Batch Size

20-25

Students per Batch

Placement

95%

Success Rate

Course Overview

Data Science is one of the most sought-after career paths in the modern technology landscape. This comprehensive program covers the entire data science workflow including data collection, cleaning, exploratory analysis, statistical modeling, machine learning, and model deployment.

You'll master Python programming, statistics, data visualization, machine learning algorithms, deep learning, and real-world project implementation. Our hands-on approach ensures you're job-ready with a strong portfolio of data science projects.

What You'll Learn

  • Python for Data Science

    NumPy, Pandas, Matplotlib, Seaborn for data manipulation

  • Statistics & Probability

    Descriptive and inferential statistics, hypothesis testing

  • Data Visualization

    Create compelling visualizations with Tableau and Power BI

  • Machine Learning

    Supervised and unsupervised learning algorithms

  • Deep Learning

    Neural networks, CNN, RNN with TensorFlow and Keras

  • Natural Language Processing

    Text analysis, sentiment analysis, and language models

  • Model Deployment

    Deploy ML models using Flask, Docker, and cloud platforms

  • Big Data Tools

    Spark, Hadoop for large-scale data processing

Course Highlights

  • Live interactive classes
  • 15+ Real-world projects
  • Kaggle competitions
  • Industry expert instructors
  • Lifetime access to materials
  • Placement assistance
  • Resume building support
  • 24/7 doubt resolution
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Course Curriculum

Detailed Syllabus

Comprehensive module-wise breakdown of the course content

01

Python Fundamentals

  • • Python Basics & Syntax
  • • Data Types & Variables
  • • Control Flow & Loops
  • • Functions & Modules
  • • Object-Oriented Programming
  • • File Handling
  • • Exception Handling
  • • Python Libraries Overview
02

Data Analysis Libraries

  • • NumPy for Numerical Computing
  • • Pandas for Data Manipulation
  • • Data Cleaning Techniques
  • • Data Transformation
  • • Handling Missing Data
  • • Data Aggregation & Grouping
  • • Time Series Analysis
  • • Data Import/Export
03

Statistics & Probability

  • • Descriptive Statistics
  • • Probability Theory
  • • Probability Distributions
  • • Inferential Statistics
  • • Hypothesis Testing
  • • Confidence Intervals
  • • Correlation & Causation
  • • Statistical Significance
04

Data Visualization

  • • Matplotlib Fundamentals
  • • Seaborn for Statistical Plots
  • • Plotly for Interactive Viz
  • • Chart Types & Selection
  • • Tableau Desktop
  • • Power BI Dashboards
  • • Data Storytelling
  • • Dashboard Best Practices
05

Machine Learning

  • • Introduction to ML
  • • Supervised Learning
  • • Linear & Logistic Regression
  • • Decision Trees & Random Forest
  • • Support Vector Machines
  • • K-Nearest Neighbors
  • • Naive Bayes Classifier
  • • Ensemble Methods
06

Advanced ML & Unsupervised Learning

  • • Clustering Algorithms
  • • K-Means & Hierarchical Clustering
  • • Principal Component Analysis (PCA)
  • • Dimensionality Reduction
  • • Anomaly Detection
  • • Association Rule Learning
  • • Model Evaluation Metrics
  • • Cross-Validation Techniques
07

Deep Learning

  • • Neural Networks Fundamentals
  • • TensorFlow & Keras
  • • Convolutional Neural Networks (CNN)
  • • Recurrent Neural Networks (RNN)
  • • LSTM & GRU Networks
  • • Transfer Learning
  • • Computer Vision Applications
  • • Model Optimization
08

NLP & Model Deployment

  • • Natural Language Processing
  • • Text Preprocessing & Tokenization
  • • Sentiment Analysis
  • • Named Entity Recognition
  • • Model Deployment with Flask
  • • Docker Containerization
  • • Cloud Deployment (AWS/Azure)
  • • Capstone Projects

Prerequisites

To get the most out of this course:

  • Basic Programming

    Any programming language experience is helpful

  • Mathematics

    Basic algebra and statistics knowledge

  • Analytical Thinking

    Problem-solving and logical reasoning skills

  • Dedication

    25-30 hours per week for 9 months

Career Opportunities

Data Scientists are among the highest-paid professionals:

Data Scientist

₹8-18 LPA

ML Engineer

₹9-20 LPA

Data Analyst

₹6-14 LPA

AI Specialist

₹10-22 LPA

Start Your Journey

Enroll in Data Science Course

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