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Data Science has emerged as a pivotal field in today’s technology-driven world, offering valuable insights and enabling data-driven decision-making across various industries. This course aims to equip learners with a comprehensive understanding of data science fundamentals, covering essential topics such as data exploration, statistical analysis, machine learning, and data visualization. By learning these core skills, participants will be prepared to solve real-world problems and gain proficiency in using popular tools and technologies.
The Data Science course syllabus is structured to provide a balanced mix of theory, practical exercises, and hands-on projects. It is ideal for aspiring data scientists, working professionals looking to enhance their analytical skills, and anyone interested in pursuing a career in data analysis. Whether you are just beginning or want to advance your knowledge, this course offers a step-by-step approach to mastering the art and science of data.
Module 1: Introduction to Data Science
- Definition and scope of Data Science.
- Importance and applications of Data Science.
- Data Science process: from data collection to insights.
- Role of a Data Scientist in various industries.
- Ethical considerations in Data Science.
- Tools and technologies used in Data Science.
- Overview of real-world Data Science case studies.
- Introduction to the course structure and expectations.
Module 2 : Data Acquisition and CleaningÂ
- Sources of data: structured, semi-structured, and unstructured.
- Web scraping and APIs for data collection.
- Data quality assessment and common issues.
- Data preprocessing techniques.
- Handling missing data and outliers.
- Data transformation and normalization.
- Data integration and feature engineering.
- Hands-on exercises using popular data manipulation libraries.
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Module 3: Exploratory Data Analysis (EDA)
- Importance of EDA in understanding data.
- Summary statistics and data visualization.
- Distribution analysis and histogram plotting.
- Correlation and scatter plots.
- Box plots and violin plots.
- Time series analysis.
- Interactive data visualization tools.
- Applying EDA to real datasets.
Module 4:Â Machine Learning Fundamentals
- Basics of machine learning and its types.
- Supervised vs. unsupervised learning.
- Model representation and evaluation.
- Training and testing datasets.
- Cross-validation techniques.
- Overfitting and underfitting.
- Performance metrics: accuracy, precision, recall, F1-score, etc.
- Implementing simple machine learning algorithms.
Module 5:Â Regression and Classification
- Linear regression and its assumptions.
- Polynomial regression and regularization.
- Logistic regression for classification.
- Decision trees and random forests.
- Support Vector Machines (SVM).
- Naive Bayes classifier.
- Model selection and hyperparameter tuning.
- Hands-on projects involving regression and classification.
Module 6: Clustering and Dimenionality Reduction
- K-means and hierarchical clustering.
- Clustering evaluation metrics.
- Principal Component Analysis (PCA).
- t-SNE (t-distributed Stochastic Neighbor Embedding).
- Manifold learning techniques.
- Applications of clustering and dimensionality reduction.
- Visualizing high-dimensional data.
- Case studies using clustering and dimensionality reduction.
Module 7: Natural Language Processing (NLP)
- Introduction to NLP and its challenges.
- Text preprocessing: tokenization, stemming, lemmatization.
- Bag-of-words and TF-IDF representations.
- Sentiment analysis and text classification.
- Named Entity Recognition (NER).
- Word embeddings: Word2Vec, GloVe.
- Seq2Seq models and machine translation.
- Hands-on NLP projects using libraries like NLTK and spaCy.
Module 8: Neural Networks and Deep Learning
- Basics of artificial neural networks.
- Activation functions and backpropagation.
- Building deep neural networks.
- Convolutional Neural Networks (CNN) for image data.
- Recurrent Neural Networks (RNN) for sequential data.
- Transfer learning and pre-trained models.
- Regularization techniques in deep learning.
- Implementing deep learning projects using TensorFlow or PyTorch.
Module 9:Â Big Data and Distributed Computing
- Introduction to Big Data concepts.
- Hadoop and MapReduce framework.
- Apache Spark for large-scale data processing.
- Working with distributed file systems.
- Data streaming and real-time processing.
- Handling big data challenges: scalability, reliability.
- Cloud computing and data science.
- Practical exercises with big data tools.
Module 10 :Â Data Science Ethics and Privacy
- Ethical considerations in data collection and usage.
- Bias and fairness in machine learning.
- Privacy issues and data anonymization.
- GDPR and other data protection regulations.
- Responsible AI and algorithmic transparency.
- Case studies of ethical dilemmas in Data Science.
- Implementing ethical practices in Data Science projects.
- Class discussions on ethical challenges.
Module 11:Â Capstone Project
- Forming teams and selecting project topics.
- Project proposal and scope definition.
- Data acquisition and preprocessing for the project.
- Exploratory data analysis for insights.
- Model selection and development.
- Implementation of machine learning or deep learning techniques.
- Evaluation metrics and performance analysis.
- Project documentation and presentation.
Module 12: Future Trends in Data Science
- Emerging trends in Data Science and AI.
- Reinforcement learning and its applications.
- Generative Adversarial Networks (GANs).
- Explainable AI and model interpretability.
- Quantum computing and its impact on Data Science.
- AI ethics and regulation advancements.
- Industry-specific applications and case studies.
- Preparing for a career in Data Science: job roles, skills, and certifications.
Conclusion
The Data Science course in Bangalore is crafted to provide a deep and practical understanding of data analysis, machine learning, and statistical methods, equipping students with the skills needed to excel in today’s data-driven world. By the end of this course, you will have gained hands-on experience with various data science tools and techniques, enabling you to solve real-world challenges confidently. Bangalore, being the hub for technology and innovation, offers abundant opportunities for networking, internships, and career growth in the data science domain. Upon completing this course, you will be well-prepared to step into a rewarding career as a data scientist or analyst, ready to make impactful contributions in the field.