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Data Science Course Syllabus For Beginners
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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:
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Week 1-2: Introduction to Data Science
Overview of Data Science and its applications
Introduction to Python programming language
Basics of data types, variables, and operators in Python
Introduction to Jupyter Notebooks for data analysis and coding exercises
Week 3-4: Data Manipulation and Analysis with Python
Introduction to libraries such as NumPy and Pandas for data manipulation
Data cleaning techniques: handling missing data, removing duplicates, etc.
Data visualization using Matplotlib and Seaborn libraries
Week 5-6: Introduction to Statistics for Data Science
Basic concepts of statistics: mean, median, mode, standard deviation, etc.
Probability theory and distributions (e.g., normal, binomial)
Statistical inference: hypothesis testing, confidence intervals
Week 7-8: Introduction to Machine Learning
Overview of machine learning concepts and types of machine learning algorithms
Supervised learning: regression and classification
Model evaluation techniques: cross-validation, confusion matrix, metrics like accuracy, precision, recall
Week 9-10: Unsupervised Learning and Dimensionality Reduction
Clustering algorithms: K-means, hierarchical clustering
Dimensionality reduction techniques: Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE)
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Week 11-12: Introduction to Big Data and Data Visualization
Introduction to Big Data technologies: Hadoop, Spark
Basics of SQL for querying relational databases
Advanced data visualization techniques using Plotly and interactive dashboards
Week 13-14: Real-world Data Science Projects
Working on small-scale data science projects or case studies
Applying the concepts learned throughout the course to analyze datasets and draw insights
Presenting findings and insights to peers
Week 15: Capstone Project
Collaborative capstone project where students work in teams to solve a real-world data science problem
Applying all the skills and techniques learned throughout the course
Presentation of the capstone project to instructors and peers
Additional Resources:
Online tutorials and documentation for Python, NumPy, Pandas, Matplotlib, Seaborn
Online courses and tutorials on platforms like Coursera, Udemy, and DataCamp
Books such as "Python for Data Analysis" by Wes McKinney, "An Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
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