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- Machine Learning: A subset of AI that involves training algorithms on data to enable them to learn and make predictions or decisions without being explicitly programmed. Techniques include supervised learning, unsupervised learning, and reinforcement learning.
- Natural Language Processing (NLP): The ability of machines to understand, interpret, and respond to human language in a way that is both meaningful and useful. Applications include chatbots, language translation, and sentiment analysis.
- Computer Vision: AI's capability to interpret and understand visual information from the world, such as images and videos. This technology is used in facial recognition, object detection, and autonomous vehicles.
- Robotics: AI applications in robots that allow them to perform tasks in the real world, often in environments that are dangerous or difficult for humans. This includes industrial robots, drones, and robotic assistants.
- Expert Systems: AI systems that emulate the decision-making ability of a human expert. They use a knowledge base and a set of rules to analyze information and provide recommendations.
- Deep Learning: A subset of machine learning that uses neural networks with multiple layers to analyze various factors of data. It is particularly effective in tasks like image and speech recognition.
- Data Connectivity: Power BI can connect to a wide range of data sources, including databases (SQL Server, Oracle), cloud services (Azure, Salesforce), Excel spreadsheets, and even web APIs, allowing users to consolidate data from multiple sources.
- Data Transformation: The Power Query Editor in Power BI enables users to clean, reshape, and transform data without requiring extensive programming knowledge. Users can perform tasks such as filtering, aggregating, and merging data.
- Interactive Visualizations: Power BI offers a variety of visualization options, including charts, graphs, maps, and tables. Users can create interactive dashboards that allow stakeholders to drill down into the data for more detailed insights.
- Natural Language Queries: Users can ask questions about their data using natural language, and Power BI will generate visualizations based on the query. This feature makes data exploration more accessible to non-technical users.
- DAX (Data Analysis Expressions): Power BI utilizes DAX, a powerful formula language, for data modeling and creating calculated columns and measures, enabling users to perform complex calculations and data analysis.
- Collaboration and Sharing: Users can publish reports and dashboards to the Power BI service, making it easy to share insights with colleagues or stakeholders. Power BI also supports role-based access control for secure sharing.
- Mobile Access: Power BI provides mobile applications for iOS and Android, allowing users to access and interact with reports and dashboards on the go.
- Business Intelligence: Organizations use Tableau to visualize sales, marketing, and operational data, helping to uncover trends and make informed decisions.
- Performance Tracking: Tableau dashboards can be designed to track key performance indicators (KPIs) and metrics in real-time, assisting businesses in monitoring their performance.
- Data Storytelling: Tableau allows users to create visually compelling narratives around data, making it easier to communicate insights and findings to various stakeholders.
- Nested Functions: Combining multiple functions within a single formula (e.g., using IF with VLOOKUP).
- Array Formulas: Performing calculations on multiple values at once.
- Logical Functions: Using functions like IF, AND, OR, and NOT for conditional analysis.
- Data Analysis Tools: Advanced Excel includes tools for data analysis
- Introduction to data sources (structured and unstructured data)
- Data collection methods (APIs, web scraping, databases)
- Data cleaning and preprocessing techniques
- Handling missing values and outliers
- Data transformation and normalization
- Descriptive and inferential statistics
- Probability distributions
- Hypothesis testing and confidence intervals
- Regression analysis (linear and logistic regression)
- Regression techniques
- Classification techniques
- Model evaluation metrics (accuracy, precision, recall, F1 score)
- Overfitting and underfitting
- Cross-validation techniques
- Natural Language Processing (NLP) basics
- Time series analysis
- Introduction to deep learning (neural networks)
- Introduction to big data technologies (Hadoop, Spark)
- Programming languages (Python, R)
- Data manipulation libraries (Pandas, NumPy)
- Visualization tools (Tableau, Power BI)
- Version control systems (Git)
- Applying learned concepts to a real-world data science project
- Data collection, analysis, modeling, and presentation
- Building a portfolio showcasing project work
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