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Data Science Essentials



12 Weeks


About the Course

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

Your Instructor

Sarthak Biswas

5 Years Experience in Business Intelligence Modeling

Sarthak Biswas
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