Data Science
Master in Data Science
At Skill Elevate, our Data Science training is designed to cover every aspect of the field, from data analysis and statistical modeling to machine learning and data visualization. We ensure our courses meet industry standards, empowering our students with essential, in-demand skills for a successful career in data science.
Course Curriculum
Introduction to Data Science
- What is Data Science?
- Data Science Lifecycle
- Roles and Responsibilities of a Data Scientist
Python for Data Science
- Introduction to Python
- Essential Python Libraries: NumPy, Pandas, Matplotlib, Seaborn
- Data Manipulation with Pandas
- Data Visualization with Matplotlib and Seaborn
Statistics and Probability
- Descriptive Statistics
- Inferential Statistics
- Probability Theory
- Hypothesis Testing
Data Wrangling
- Data Cleaning
- Handling Missing Values
- Data Transformation
- Feature Engineering
Exploratory Data Analysis (EDA)
- Introduction to EDA
- Univariate, Bivariate, and Multivariate Analysis
- Data Visualization Techniques
- Correlation and Causation
Machine Learning Basics
- Introduction to Machine Learning
- Supervised vs Unsupervised Learning
- Model Evaluation Metrics
Supervised Learning Algorithms
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forests
- Support Vector Machines
- Model Evaluation and
- Hyperparameter Tuning
Unsupervised Learning Algorithms
- Clustering: K-Means, Hierarchical
- Dimensionality Reduction: PCA, LDA
- Association Rules
Advanced Machine Learning
- Ensemble Methods: Boosting, Bagging
- Time Series Analysis
- Natural Language Processing (NLP)
- Neural Networks and Deep Learning
Model Deployment and Production
- Introduction to Model Deployment
- Model Serving with Flask/Django
- Using Docker for Deployment
- Introduction to MLOps
Big Data Technologies
- Introduction to Big Data
- Hadoop Ecosystem
- Spark for Big Data Processing
Data Engineering
- Data Pipelines
- ETL Processes
- Data Warehousing
- Working with Cloud Data Platforms (AWS, GCP, Azure)
Data Science Project Management
- Project Lifecycle and Methodologies
- CRISP-DM Framework
- Agile for Data Science Projects
Case Studies and Real-World Projects
- Hands-on Projects
- Case Studies from Different Industries
- End-to-End Data Science Project
Ethics and Best Practices in Data Science
- Data Privacy and Security
- Ethical Considerations in Data Science
- Best Practices for Data Science Workflows
Conclusion and Future Trends
- Summary of Key Concepts
- Future Trends in Data Science
- Resources for Further Learning
