Data Science
Introduction to Data Science
Understand the fundamentals of data science and its importance in analyzing and interpreting complex data. Learn about the data science workflow and key concepts such as data exploration, visualization, and modeling.
Python for Data Science
Learn how to use Python for data science tasks. Explore essential Python libraries such as NumPy, pandas, Matplotlib, and Seaborn for data manipulation, analysis, and visualization.
Data Cleaning and Preparation
Study techniques for data cleaning and preparation. Learn how to handle missing data, outliers, and data transformations to prepare datasets for analysis and modeling.
Exploratory Data Analysis (EDA)
Explore methods for exploratory data analysis. Learn how to use statistical techniques and visualization tools to uncover patterns, relationships, and insights in your data.
Statistical Analysis and Hypothesis Testing
Understand statistical analysis techniques and hypothesis testing. Learn about key concepts such as probability distributions, confidence intervals, p-values, and statistical tests.
Machine Learning with Python
Dive into machine learning using Python. Study algorithms for classification, regression, clustering, and dimensionality reduction. Learn how to implement and evaluate machine learning models using libraries such as scikit-learn.
Data Visualization
Learn techniques for effective data visualization. Explore how to create informative and visually appealing charts, graphs, and plots using Python visualization libraries.
Advanced Data Science Topics
Explore advanced topics in data science, including deep learning, natural language processing, and big data analytics. Learn how to apply these techniques to solve complex data problems.
Case Studies and Practical Exercises
Engage in case studies and practical exercises to apply data science concepts using Python. Practice analyzing real-world datasets, building models, and deriving actionable insights.
Exam Preparation and Certification
Prepare for data science certifications with study tips, practice exams, and review materials. Familiarize yourself with exam formats, question types, and strategies for success.
Data Science Syllabus
Introduction to Data Science
- Overview of Data Science: Definition, importance, and applications
- Data Science Life Cycle: From data collection to deployment
- Roles and Responsibilities in Data Science Teams
Programming Fundamentals for Data Science
- Introduction to Python and R: Basics of programming languages
- Data Structures and Algorithms: Arrays, lists, dictionaries, and algorithms for data manipulation
- Libraries and Packages: NumPy, Pandas, Matplotlib (Python), ggplot2 (R)
Data Wrangling and Preprocessing
- Data Cleaning Techniques: Handling missing data, outliers, and duplicates
- Data Transformation: Normalization, scaling, and encoding categorical variables
- Feature Engineering: Creating new features from existing data
Exploratory Data Analysis (EDA)
- Statistical Analysis: Descriptive statistics, inferential statistics
- Data Visualization: Plotting with Matplotlib, Seaborn, ggplot2
- EDA Techniques: Univariate, bivariate, and multivariate analysis
Machine Learning Fundamentals
- Introduction to Machine Learning: Supervised vs. unsupervised learning
- Model Selection and Evaluation: Cross-validation, bias-variance tradeoff
- Performance Metrics: Accuracy, precision, recall, F1-score, ROC curve
Supervised Learning Algorithms
- Linear Regression and Logistic Regression
- Decision Trees and Random Forests
- Support Vector Machines (SVM)
- Naive Bayes Classifier
Unsupervised Learning Algorithms
- K-means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Association Rule Mining: Apriori algorithm
Advanced Machine Learning Techniques
- Ensemble Learning: Bagging, boosting (AdaBoost, Gradient Boosting), stacking
- Neural Networks and Deep Learning: Introduction to artificial neural networks (ANN)
- Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN)
- Natural Language Processing (NLP) and Text Mining
Model Deployment and Optimization
- Model Deployment Strategies: Flask (Python), Shiny (R)
- Model Optimization Techniques: Hyperparameter tuning, feature selection
- Monitoring and Maintaining Models: Model performance tracking and retraining
Big Data and Data Science
- Introduction to Big Data: Characteristics, challenges, and opportunities
- Distributed Computing: Hadoop, Spark
- Scalable Machine Learning: Machine learning on big data platforms
Data Science Tools and Platforms
- Data Science Tools Overview: Jupyter Notebook, RStudio, Anaconda
- Cloud Platforms for Data Science: AWS, Google Cloud Platform, Azure
- Version Control and Collaboration Tools: Git, GitHub, GitLab
Ethics and Privacy in Data Science
- Data Privacy and Security: GDPR, CCPA, data anonymization techniques
- Ethical Considerations: Bias, fairness, interpretability in machine learning models
- Responsible AI: Guidelines for ethical AI development and deployment
Capstone Project in Data Science
- Real-world Data Science Project: From problem formulation to model deployment
- Presentation of Findings: Communicating results to stakeholders
- Peer Review and Feedback: Evaluation and improvement based on feedback
Training
Basic Level Training
Duration : 1 Month
Advanced Level Training
Duration : 1 Month
Project Level Training
Duration : 1 Month
Total Training Period
Duration : 3 Months
Course Mode :
Available Online / Offline
Course Fees :
Please contact the office for details