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Data Science Course Syllabus For Beginners

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  1. For beginners in data science, it's essential to start with the fundamentals and gradually build up to more advanced topics. Here's a suggested syllabus for a beginner-level data science course:
  2.  
  3. Week 1-2: Introduction to Data Science
  4. Overview of Data Science and its applications
  5. Introduction to Python programming language
  6. Basics of data types, variables, and operators in Python
  7. Introduction to Jupyter Notebooks for data analysis and coding exercises
  8. Week 3-4: Data Manipulation and Analysis with Python
  9. Introduction to libraries such as NumPy and Pandas for data manipulation
  10. Data cleaning techniques: handling missing data, removing duplicates, etc.
  11. Data visualization using Matplotlib and Seaborn libraries
  12. Week 5-6: Introduction to Statistics for Data Science
  13. Basic concepts of statistics: mean, median, mode, standard deviation, etc.
  14. Probability theory and distributions (e.g., normal, binomial)
  15. Statistical inference: hypothesis testing, confidence intervals
  16. Week 7-8: Introduction to Machine Learning
  17. Overview of machine learning concepts and types of machine learning algorithms
  18. Supervised learning: regression and classification
  19. Model evaluation techniques: cross-validation, confusion matrix, metrics like accuracy, precision, recall
  20. Week 9-10: Unsupervised Learning and Dimensionality Reduction
  21. Clustering algorithms: K-means, hierarchical clustering
  22. Dimensionality reduction techniques: Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE)
  23. Week 11-12: Introduction to Big Data and Data Visualization
  24. Introduction to Big Data technologies: Hadoop, Spark
  25. Basics of SQL for querying relational databases
  26. Advanced data visualization techniques using Plotly and interactive dashboards
  27. Week 13-14: Real-world Data Science Projects
  28. Working on small-scale data science projects or case studies
  29. Applying the concepts learned throughout the course to analyze datasets and draw insights
  30. Presenting findings and insights to peers
  31. Week 15: Capstone Project
  32. Collaborative capstone project where students work in teams to solve a real-world data science problem
  33. Applying all the skills and techniques learned throughout the course
  34. Presentation of the capstone project to instructors and peers
  35. Visit Website-https://www.sevenmentor.com/data-science-classes-in-nagpur

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