Data science technical seminar of cse dept

Tech seminar

1. SRI INDU COLLEGE OF ENGINEERING AND TECHNOLOGY DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING ACADEMIC YEAR : 2020-2024 TECHNICAL SEMINAR ON DATA SCIENCE Submitted by: G. Pavani (20D41A0569 )

2. Introduction to Data Science Data science is an interdisciplinary field that utilizes scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It combines techniques from mathematics, statistics, computer science, and domain expertise to analyze and interpret complex datasets. Data science is used in various industries, such as healthcare, finance, marketing, and technology, to make data-driven decisions and solve real-world problems.

3. Key Skills in Data Science Strong programming skills are essential for data scientists, with proficiency in languages such as Python, R, or SQL. A solid foundation in mathematics and statistics is crucial to understand and apply various algorithms and models in data analysis. Data visualization skills are important to effectively communicate findings and insights to non-technical stakeholders.

4. Data Science Process The data science process typically includes steps such as data collection, data cleaning, data exploration, feature engineering, model selection, model training, model evaluation, and deployment. It involves iterative cycles of experimenting, refining, and optimizing models to improve accuracy and performance. The process also emphasizes the importance of ethical considerations, privacy, and data security.

5. Data Collection Data collection involves gathering relevant data from various sources, such as databases, APIs, web scraping, or sensor networks. It is important to ensure data quality, completeness, and accuracy for reliable analysis. Data collection methods may vary depending on the specific project requirements and objectives.

6. Data Cleaning Data cleaning, also known as data preprocessing, involves handling missing values, dealing with outliers, and resolving inconsistencies in the dataset. It may also include transforming and normalizing data to make it suitable for analysis. Proper data cleaning is critical to ensure the accuracy and integrity of subsequent analysis.

7. Exploratory Data Analysis Exploratory Data Analysis (EDA) involves analyzing and visualizing data to understand its characteristics, patterns, and relationships. EDA helps in identifying trends, outliers, and potential issues in the dataset. Techniques used in EDA include summary statistics, data visualization, and correlation analysis.

8. Machine Learning Machine learning is a subset of data science that focuses on developing algorithms and models that can learn and make predictions or decisions without being explicitly programmed. Supervised learning, unsupervised learning, and reinforcement learning are common types of machine learning. Machine learning algorithms, such as linear regression, decision trees, support vector machines, and neural networks, are applied to analyze and make predictions from data.

9. Model Evaluation and Selection Model evaluation involves assessing the performance and accuracy of different models using appropriate metrics and techniques. Model selection is the process of choosing the best-performing model based on evaluation results and specific project requirements. Common evaluation techniques include cross-validation, confusion matrix, precision, recall, and F1 score.

10. Data Visualization Data visualization is the graphical representation of data to communicate information, patterns, and insights effectively. Visualizations can include charts, plots, graphs, and interactive dashboards. Well-designed visualizations enhance understanding, facilitate storytelling, and aid in decision-making.

11. Conclusion Data science plays a crucial role in extracting insights and making data-driven decisions in various industries. It combines programming, mathematics, statistics, and domain expertise to analyze and interpret complex datasets. Data science involves a structured process, including data collection, cleaning, exploration, modeling, and visualization, to derive actionable insights from data.

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