DATA SCIENCE-1. Enginnering course .pdf
- 1. DATA SCIENCE Designed for skill proficiency
- 2. PROGRAM HIGHLIGHITS ● Accredited certificates Program approved ISO Certificate ● Internships Opportunities will be provided ● Placement Guidance Assistance from industrial EXPERTS ● Basic-Advanced Level Training By Experienced Mentors ● Live & Recorded Lectures At Your Flexible Schedule ● Real Time Projects Minor & Major Projects
- 3. ABOUT US OUR MOTIVE Skill Intern is a leading EdTech company dedicated to empowering engineering students with the skills and knowledge necessary to excel in today’s competitive job market. Our mission is to bridge the gap between theoretical learning and practical application, enabling students to develop a strong foundation and enhance their employability. UPSKILL Empowering Minds For Tomorrow ENHANCE Discover Your Next Ambition MOTIVATE Empowering Minds, Igniting Futures
- 4. WHY DS ? ❖ Data-Driven Decision Making ❖ Competitive Advantage ❖ Operational Efficiency ❖ Personalization ❖ Risk Management ❖ Innovation and Research Data Science has emerged as one of the most critical fields in the modern digital age due to its profound impact on various aspects of business, technology, and everyday life. Here are several key reasons why Data Science is important ❖ Improving Quality of Life ❖ Enhancing Business Strategies ❖ Handling Big Data ❖ Societal Impact ❖ Job Opportunities ❖ Enhanced Decision-Making Capabilities
- 5. LEARNING PATH ❖ Introduction to Data Science ❖ Python for Data Science ❖ Data Handling and Manipulation ❖ Data Visualization ❖ Statistics and Probability ❖ Machine Learning Fundamentals ❖ Advanced Machine Learning ❖ Time Series Analysis ❖ Deep Learning ❖ Natural Language Processing (NLP) ❖ Big Data and Cloud Computing ❖ Data Science Project Management
- 6. Module 1: Introduction to Data Science ● Definition and Overview of Data Science ● The Data Science Lifecycle ● Roles and Responsibilities of a Data Scientist ● Applications of Data Science in Various Industries Module 2: Python for Data Science ● Introduction to Python Programming ● Python Basics: Variables, Data Types, Operators ● Control Structures: Conditionals and Loops ● Functions and Modules ● Introduction to Jupyter Notebooks Module 3: Data Handling and Manipulation ● Importing Data: CSV, Excel, JSON, SQL ● Data Manipulation with Pandas ● Data Cleaning: Handling Missing Values, Duplicates ● Data Transformation: Aggregation, Grouping, Merging ● Exploratory Data Analysis (EDA)
- 7. Module 4: Data Handling and Preprocessing ● Types of Data: Structured and Unstructured ● Data Collection and Cleaning ● Exploratory DataAnalysis (EDA) Feature Engineering and Selection Module 5: Machine Learning Basics ● Introduction to Machine Learning ● Supervised vs. Unsupervised Learning ● ClassificationAlgorithms (K-Nearest Neighbors, Decision Trees) ● RegressionAlgorithms (Linear Regression, Polynomial Regression) Module 6: Advanced Machine Learning ● Ensemble Methods (Random Forest, Gradient Boosting) ● ClusteringAlgorithms (K-Means, Hierarchical Clustering) ● Dimensionality Reduction (PCA, LDA) ● Model Evaluation and Validation (Cross-Validation, ROC Curve)
- 8. Module 7: Neural Networks and Deep Learning ● Introduction to Neural Networks ● Perceptrons and Multi-Layer Perceptrons ● Backpropagation and Gradient Descent ● Introduction to Deep Learning Frameworks (TensorFlow, Keras) Module 8: Deep Learning Architectures ● Convolutional Neural Networks (CNNs) ● Recurrent Neural Networks (RNNs) ● Long Short-Term Memory Networks (LSTMs) ● Autoencoders and GenerativeAdversarial Networks (GANs) Module 9: Natural Language Processing (NLP) ● Introduction to NLP ● Text Preprocessing (Tokenization, Stemming, Lemmatization) ● Bag of Words and TF-IDF ● Advanced NLP Techniques (Word Embeddings, Transformers)
- 9. Module 10: Natural Language Processing (NLP) ● Introduction to NLP ● Text Preprocessing: Tokenization, Stemming, Lemmatization ● Bag of Words and TF-IDF ● Advanced NLP Techniques: Word Embeddings, Transformers ● Sentiment Analysis and Text Classification Module 11: Big Data and Cloud Computing ● Introduction to Big Data Technologies ● Hadoop Ecosystem: HDFS, MapReduce, Hive ● Introduction to Spark and PySpark ● Cloud Platforms for Data Science: AWS, Azure, Google Cloud ● Data Storage and Management in the Cloud Module 12: Data Science Project Management ● Project Planning and Execution ● Data Science Workflow and Best Practices ● Version Control with Git and GitHub ● Collaboration Tools and Techniques ● Presentation and Communication of Results
- 10. Assignments & Assessments ● Weekly assignments based on module topics ● Mid-term project: Wireframing and prototyping a small application ● Final project: Comprehensive DATA SCIENCE project ● Participation in class discussions and activities Recommended Reading ● "Python for Data Analysis" by Wes McKinney ● "Data Science from Scratch: First Principles with Python" by Joel Grus ● "An Introduction to Statistical Learning: with Applications in R" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
- 11. FRAME WORKS
- 12. TOOLS USED DOCKER *In case of additional tools used, It will be discussed in live class JUPTER NOTEBOOK