Data Science Essentials

Week 1: Introduction to Data Science

• Understanding the role and significance of data science

• Key concepts in data science: data exploration, data cleaning, and data visualization

• Introduction to data analysis using Python and popular libraries (e.g., NumPy, Pandas)

Week 2: Data Preprocessing and Cleaning

• Data cleaning techniques: handling missing values, outliers, and duplicates

• Data preprocessing methods: feature scaling, normalization, and encoding categorical variables

• Exploratory data analysis (EDA) techniques: statistical summaries, data visualization, and correlation analysis

Week 3: Data Analysis and Modeling

• Supervised learning algorithms: linear regression, logistic regression, decision trees, and random forests

• Evaluation metrics for regression and classification models

• Model selection and hyperparameter tuning

• Model evaluation and validation techniques (e.g., cross-validation, train-test split)

Week 4: Unsupervised Learning and Clustering

• Introduction to unsupervised learning: clustering and dimensionality reduction

• Clustering algorithms: k-means, hierarchical clustering

• Dimensionality reduction techniques: principal component analysis (PCA), t-SNE

• Visualization of high-dimensional data

Week 5: Advanced Topics in Data Science

• Time series analysis: modeling and forecasting

• Text mining and natural language processing (NLP)

• Introduction to deep learning and neural networks

• Transfer learning and pre-trained models

Week 6: Big Data and Data Engineering

• Introduction to big data and distributed computing frameworks (e.g., Hadoop, Spark)

• Data extraction and processing from different data sources (e.g., databases, APIs)

• Introduction to SQL for data manipulation and retrieval

• Data pipeline development and data engineering best practices

Week 7: Data Visualization and Communication

• Effective data visualization principles and techniques

• Data visualization tools (e.g., Matplotlib, Seaborn, Tableau)

• Storytelling with data: creating compelling visual narratives

• Presenting and communicating data insights effectively

Week 8: Capstone Project and Practical Applications

• Application of data science techniques to a real-world project

• Data acquisition, cleaning, analysis, and modeling

• Presentation of project findings and insights