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Data Science

Who this course is for

  • Beginners & Students: Those who want to start a career in Data Science with no prior experience. College students looking to build expertise in Python, Statistics, and Machine Learning.
  • Data Analysts & Business Analysts: Analysts looking to upgrade from Excel & SQL to Python, Machine Learning, and AI. Professionals in marketing, finance, and operations who want to use data for better business insights.
  • Software Developers & IT Professionals: Developers who want to transition into Data Science and work on AI-driven applications. Backend, Full Stack, and Python developers aiming to analyze and visualize data effectively.
  • Freelancers & AI Enthusiasts: Freelancers looking to take on high-paying data science projects. AI enthusiasts who want to build data-driven models & automation tools.

Why take this course

  • Data Science is one of the most in-demand fields across industries like IT, healthcare, finance, e-commerce, and more. There is a huge skill gap, meaning skilled professionals are highly sought after. Lucrative career opportunities with high salary packages.
  • Data Science is not limited to IT; it is used in banking, healthcare, marketing, government, and even sports analytics.
  • Data Science is at the core of Artificial Intelligence, Machine Learning, and Automation, which are shaping the future.

Course Content

Module 1: Mathematics for AI & ML
  • Linear Algebra
  • Probability and Statistics
  • Calculus
Module 2: Programming for Data Science
  • Python for Data Science
  • R for Data Science (Optional)
  • Libraries: NumPy, Pandas, Matplotlib, Seaborn
Module 3: Data Collection and Preprocessing
  • Data Cleaning
  • Handling Missing Values
  • Feature Engineering and Feature Scaling
Module 4: Exploratory Data Analysis (EDA)
  • Data Visualization Techniques
  • Statistical Analysis
Module 5: Data Wrangling
  • Working with Structured and Unstructured Data
  • Handling Big Data (Hadoop, Spark Basics)
Module 6: Supervised Learning
  • Regression Analysis
  • Classification Techniques
  • Model Evaluation and Metrics
Module 7: Unsupervised Learning
  • Clustering Techniques
  • Dimensionality Reduction
Module 8: Advanced Machine Learning
  • Ensemble Methods (Bagging, Boosting)
  • Support Vector Machines
  • Recommender Systems
Module 9: Deep Learning
  • Neural Networks
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Transfer Learning
Module 10: Natural Language Processing (NLP)
  • Text Preprocessing
  • Sentiment Analysis
  • Topic Modeling
Module 11: Time Series Analysis
  • Forecasting Methods
  • ARIMA, SARIMA Models
Module 12: Big Data Tools for Data Science
  • Hadoop Basics
  • Apache Spark for Data Processing
Module 13: Data Science Tools and Technologies
  • Jupyter Notebooks
  • Tableau/Power BI
  • SQL for Data Science
Module 14: Data Science in the Cloud
  • AWS Data Science Tools
  • Google Cloud AI Tools
  • Azure Machine Learning Studio
Module 15: Model Deployment
  • Deploying Models with Flask/Django
  • Deployment on Cloud Platforms (AWS, Azure, GCP)
Module 16: Ethics and Privacy in Data Science
  • Data Bias and Fairness
  • Data Privacy Laws (GDPR, CCPA)
Module 17: Capstone Projects
  • End-to-End Real-World Data Science Projects
  • Data Analysis, Modeling, and Deployment
Module 18: Advanced Topics
  • AutoML and Hyperparameter Tuning
  • AI Integration in Data Science
  • Edge Analytics and IoT Data Processing