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Active Learning
Data Science Course at Vibrant Technology
Our comprehensive program is designed to equip you with the essential skills and knowledge required to analyze and interpret complex data, build predictive models, and derive actionable insights. Whether you're a beginner or an experienced professional, our course covers everything from basic data science concepts to advanced machine learning techniques.
- Overview of Data Science: Understand the role and importance of data science in various industries.
- Data Science Lifecycle: Learn the end-to-end process of a data science project, from data collection to model deployment.
- Data Collection: Techniques for collecting data from various sources.
- Data Cleaning: Methods for cleaning and preprocessing data to ensure accuracy and reliability.
- Data Transformation: Transform raw data into a structured format suitable for analysis.
- Descriptive Statistics: Understand measures of central tendency, variability, and data distribution.
- Data Visualization: Use tools like Matplotlib, Seaborn, and Plotly to create informative visualizations.
- Hypothesis Testing: Perform hypothesis testing to validate assumptions and insights.
- Probability Theory: Basics of probability, including probability distributions and Bayes' theorem.
- Statistical Inference: Learn about inferential statistics, including confidence intervals and hypothesis tests.
- Supervised Learning: Techniques like linear regression, logistic regression, decision trees, and support vector machines.
- Unsupervised Learning: Methods such as clustering (K-means, hierarchical) and dimensionality reduction (PCA, t-SNE).
- Reinforcement Learning:s Introduction to reinforcement learning concepts and algorithms.
- Excel for Data Analysis: Advanced Excel functions and features for data manipulation and analysis.
- Ensemble Methods: : Learn about bagging, boosting, and stacking techniques.
- Deep Learning: Introduction to neural networks, CNNs, RNNs, and popular frameworks like TensorFlow and PyTorch.
- Natural Language Processing (NLP): Techniques for text processing, sentiment analysis, and language modeling.
- Hadoop and Spark:
Introduction to big data frameworks for processing large datasets.
- NoSQL Databases: Learn about NoSQL databases like MongoDB and Cassandra.
- Distributed Computing: Techniques for distributed data processing and parallel computing.
- Python for Data Science:
Learn Python programming and libraries like Pandas, NumPy, and Scikit-Learn.
- R Programming: Introduction to R for statistical computing and graphics.
- SQL: Master SQL for database querying and management.
- Python for Data Science:
Learn Python programming and libraries like Pandas, NumPy, and Scikit-Learn.
- Real-World Projects: Work on practical projects to apply your data science skills and build a professional portfolio.
- Collaborative Development: Engage in group projects to simulate real-world work environments and collaboration..
