Courses / Data Science

Data Science Course in Noida

This course provides a step-by-step journey into Data Science, starting from the basics and moving toward advanced, career-focused skills. Students will learn data collection, cleaning, and analysis, build a strong foundation 

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Data Science Course

Module 1: Data Management

1.1: Introduction to Data Science
  1. Overview of Data Science
  2. Applications of Data Science
  3. Data Science vs. Business Intelligence
  4. Data Science vs. Data Analytics
1.2: Data Collection and Cleaning
  1. Types of data: structured, unstructured, semi-structured
  2. Data sources: databases, web scraping, APIs
  3. Handling missing data
  4. Data normalization and standardization
  5. Data transformation techniques
1.3: Data Analysis
  1. NoSQL Databases
  2. SQL Databases & Writing Query
  3. Descriptive Statistics
  4. Data Visualization
  5. Identifying patterns and trends
  6. Correlation analysis
1.4: Statistical Analysis
  1. Probability Theory
  2. Inferential Statistics
  3. Hypothesis Testing

Module 2: Data Processing & Machine Learning

2.1: Data Manipulation with Python
  1. Introduction to Python for Data Science
  2. Data Manipulation with Pandas (Python)
  3. Data manipulation using cloud tools
  4. Python libraries for Big data manipulation
  5. Sharding and other optimization strategy
2.2: Data Engineering
  1. Data Warehousing and ETL Processes
  2. Building Data Pipelines
  3. Using SQL and NoSQL Databases
2.3: Machine Learning
  1. Introduction to Machine Learning
  2. Supervised Learning Algorithms (Linear Regression, Decision Trees, etc.)
  3. Unsupervised Learning Algorithms (Clustering, PCA, etc.)
  4. Model Evaluation and Validation
2.4: Advanced Machine Learning
  1. Ensemble Methods (Random Forest, Gradient Boosting)
  2. Neural Networks and Deep Learning
  3. Natural Language Processing (NLP)
  4. Time Series Analysis
2.5: Model Deployment and Product Development
  1. Model deployment strategies
  2. Introduction to cloud platforms: AWS, Google Cloud, Azure
  3. Using Docker for model deployment
  4. Monitoring and maintaining models in production

Module 3: Data Visualization and Data Ethics

3.1: Tools for Data Visualization and Presentation
  1. Principles of Effective Data Visualization
  2. Tools for Data Visualization (Matplotlib, Seaborn, ggplot2, Tableau, Cloud-Based tools)
  3. Creating Dashboards
  4. Communicating Data Insights
3.2: Data Ethics and Privacy
  1. Ethical considerations in data science
  2. Data Privacy laws and regulations (GDPR, DPDPA)
  3. Bias and fairness in Algorithms

Module 4: Applied Data Science

4.1: Case Studies and Applications
  1. Real-World Data Science Case Studies
  2. Industry-Specific Applications (Finance, Healthcare, Retail, etc.)
4.2: Data Science Project
  1. Project Selection and Planning
  2. Data Collection and Preprocessing
  3. Model Building and Evaluation
  4. Presenting Results

Turn raw data into a strategic asset. Learn the complete data lifecycle, from collection and cleaning to analysis and visualization using powerful statistical tools. This course equips you to uncover hidden patterns, trends, and actionable insights that inform critical business decisions. Become the bridge between data and strategy in a data-driven world.

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TuxAcademy

With our Noida branch, we support students from all fields to build strong skills and grow their careers. Our team provides endless placement support to ensure you get the job you’ve been aiming for. Are you ready to learn new skills and shape your future?

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Program Highlights and Reasons to Enroll in Data Science Course at TuxAcademy!

✅ Practical labs with real business datasets.

✅ Project-based approach for portfolio building.

✅ Exposure to Python, SQL, and Power BI/Tableau for analytics.

✅ Statistical foundations and predictive modeling explained clearly.

✅ End-to-end training in data collection, cleaning, visualization, and modeling.

✅ Learn job-ready tools and techniques from day one.

✅ Master the ability to turn raw data into actionable insights.

✅ Build strong analytical skills that complement any technical career.

✅ High demand for data scientists across IT, finance, healthcare, and retail.

Classroom Training

  • Learn directly from experienced industry professionals through classroom sessions at TuxAcademy’s Noida branch.
  • Don’t delay your career growth—kickstart your journey in Data Science today with our expert-led course!
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Instructor-Led Online Training

  • Learn from expert instructors with live interactive online classes and access recorded videos of every session for revision.
  • No need to worry about travel—build your Data Science career from home with our comprehensive online Data Science course!
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The Best Beneficial Side
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Artificial Intelligence Module

Master Artificial Intelligence with modules on Generative AI, Machine Learning, NLP, Computer Vision, Python, and cloud-based AI solutions

  • Module 1: Introduction to AI
  • Module 2: Building Blocks of AI
  • Module 3: Tools and Technologies
  • Module 4: Practical AI Projects
1.1 What is AI?
  1. Definition and scope
  2. History and evolution of AI
  3. AI vs. Human Intelligence
1.2 Applications of AI
  • Customer Support/CRM
  • Healthcare
  • Finance
  • Transportation
  • Entertainment
  • Robotics
  • Other industries
2.1 Machine Learning (ML)
  1. Supervised Learning
  2. Unsupervised Learning
  3. Reinforcement Learning
  4. Common Algorithms (e.g., Linear Regression, Decision Trees, Neural Networks)
2.2 Deep Learning (DL)
  1. Neural Networks
  2. Convolutional Neural Networks (CNNs)
  3. Recurrent Neural Networks (RNNs)
  4. Applications of Deep Learning
2.3 Natural Language Processing (NLP)
  1. Text Processing
  2. Sentiment Analysis
  3. Language Translation
  4. Chatbots
2.4 Computer Vision
  1. Image Recognition
  2. Object Detection
  3. Image Generation
3.1 Programming Languages
  1. Python
3.2 AI Frameworks and Libraries
  1. TensorFlow
  2. PyTorch
  3. Scikit-Learn
3.3 Data Handling and Preprocessing
  1. Data Collection
  2. Data Cleaning
  3. Feature Engineering
2.4 Computer Vision
  1. Image Recognition
  2. Object Detection
  3. Image Generation
4.1 Project 1: Sentiment Analysis
  1. Building a business application using Generative AI
4.2 Project 2: Image Classification
  1. Use AI to classify images and tag images as a real-world object.
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