Course description
Data science has emerged as a critical discipline driving innovation, efficiency, and decisionmaking across various industries. Here are key reasons why data science is essential in the real world:
- Informed Decision Making
- Business Efficiency and Optimization
- Personalized Customer Experiences
- Healthcare and Medical Advancements
- Urban Planning and Smart Cities
- Scientific Research and Innovation
- Financial and Risk Management
- Education and Learning
- Data science has significantly influenced and expanded the scope of computer science, leveraging advanced algorithms, computational power, and statistical techniques to extract insights and knowledge from data.
- Here's an overview of how data science intersects with various domains within computer science:
- Data science is at the forefront of driving innovation and transformation within computer science, enhancing capabilities in machine learning, big data analytics, and artificial intelligence.
- As data becomes increasingly abundant and complex, the integration of data science principles and techniques is crucial for solving challenging problems and advancing technology across various domains.
- At Stellaraa, we offer specialized courses to equip individuals with the skills and knowledge needed to excel in data science and its applications within computer science.
- Overview of Data Science
- Importance and Applications of Data Science
- Data Science Workflow
- Tools and Technologies in Data Science
- Setting Up the Development Environment (Python, Jupyter Notebooks, Anaconda)
- Types of Data: Structured, Unstructured, Semi-Structured
- Data Collection Methods
- Data Cleaning and Handling Missing Values
- Exploratory Data Analysis (EDA)
- Data Visualization Techniques
- Feature Engineering and Selection
- Python for Data Science
- Introduction to Python
- Data Structures and Functions
- Libraries: NumPy, Pandas, Matplotlib, Seaborn
- R for Data Science (Optional)
- Introduction to R
- Data Manipulation with dplyr
- Data Visualization with ggplot2
- Descriptive Statistics
- Inferential Statistics
- Probability Theory
- Hypothesis Testing
- Statistical Distributions
- Sampling Techniques
- Importance of Data Visualization
- Data Visualization Tools and Libraries
- Matplotlib and Seaborn in Python
- Tableau and Power BI
- Creating Effective Data Visualizations
- Dashboard Design and Storytelling with Data
- Supervised Learning
- Regression Algorithms (Linear Regression, Ridge, Lasso)
- Classification Algorithms (Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, SVM)
- Evaluation Metrics (MSE, RMSE, R², Accuracy, Precision, Recall, F1 Score, ROC-AUC)
- Unsupervised Learning
- Clustering Algorithms (K-Means, Hierarchical Clustering, DBSCAN)
- Dimensionality Reduction (PCA, LDA, t-SNE)
- Model Selection and Evaluation
- Cross-Validation
- Hyperparameter Tuning (Grid Search, Random Search)
- Ensemble Methods
- Bagging, Boosting (AdaBoost, Gradient Boosting, XGBoost)
- Stacking and Blending
- Deep Learning Basics
- Introduction to Neural Networks
- Using TensorFlow and Keras
- Natural Language Processing (NLP)
- Text Preprocessing
- Text Classification and Sentiment Analysis
- Word Embeddings (Word2Vec, GloVe)
- Time Series Analysis
- Introduction to Time Series Data
- ARIMA and SARIMA Models
- Forecasting Techniques
- Introduction to Big Data
- Hadoop Ecosystem
- Working with Apache Spark
- Big Data Storage and Processing
- Use Cases of Big Data in Data Science
- Introduction to Data Engineering
- ETL Processes
- Data Warehousing Concepts
- Working with SQL and NoSQL Databases
- Data Pipeline Tools (Apache Airflow, Luigi)
- Introduction to Cloud Computing
- Using AWS for Data Science (S3, Redshift, SageMaker)
- Using Google Cloud Platform (BigQuery, Cloud ML Engine)
- Using Microsoft Azure (Azure ML, Cosmos DB)
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Python with Data Science Libraries
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R
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Jupyter Notebooks
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Tableau
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Apache Spark
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