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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:

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) 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 This syllabus provides a structured approach to learning data science for beginners, covering essential programming skills, statistical concepts, machine learning algorithms, and practical applications. Adjustments can be made based on the pace of learning and specific interests of the learners. Visit-https://www.sevenmentor.com/data-science-classes-in-nagpur

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: 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) 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 This syllabus provides a structured approach to learning data science for beginners, covering essential programming skills, statistical concepts, machine learning algorithms, and practical applications. Adjustments can be made based on the pace of learning and specific interests of the learners. Visit-https://www.sevenmentor.com/data-science-classes-in-nagpur

#8 Data Science Course Syllabus For Beginners