Faster Data Integration Pipeline Execution Using Spark-Jobserver
Optimize data integration pipelines using Spark-Jobserver for faster execution, context reuse, and efficient resource management. Learn configuration, security, and API integration for improved performance.
Encryption and Masking for Sensitive Apache Spark Analytics Addressing CCPA and Governance
Practical methods for balancing data encryption and analytics in Apache Spark, addressing CCPA and governance requirements while maintaining analytical value.
Running Apache Spark Jobs Using Kubernetes
Learn to run Apache Spark jobs on Kubernetes, unifying analytics and data science on a cloud-native architecture. Discover how this approach simplifies infrastructure, enables on-demand deployment, and eliminates big data cluster overhead.
Tackling Scaling Challenges of Apache Spark at LinkedIn - Infrastructure Optimization and User Productivity
Explore LinkedIn's journey scaling Apache Spark, addressing infrastructure challenges, optimizing resource management, and enhancing user productivity through automated analysis and efficiency improvements.
Change Seminar - Josh Hug - UC Berkeley
Embedding social impact awareness in CS education: integrating ethics, career reflection, and professional development into technical courses at UC Berkeley.
Do Owls Randomly Sample Local Small Mammal Populations?
Explore small mammal populations in Drumheller through owl sampling, comparing 20-year data to understand local ecosystem changes and wildlife diversity.
Time Series Forecasting - ARIMA Models and Regression Analysis
Learn to predict future values using ARIMA, covering AutoReg, data handling, and model creation. Gain practical skills in time series forecasting with real-world examples and comprehensive explanations.
Time Series Analysis Visualizations with Matplotlib, Seaborn, and Plotly
Explore time series visualization techniques using Matplotlib, Seaborn, and Plotly. Learn to style charts and analyze trends, seasonality, and moving averages in data science applications.
Securing the Spark Fire Hose - 2016
Learn to secure Apache Spark in big data analytics. Explore code and configuration security, data protection strategies, and integration with common technologies to safeguard critical information throughout its lifecycle.
How to Invest with Data Science - Technical Analysis and Portfolio Optimization
Learn data science techniques for investing, covering stock analysis, portfolio optimization, and advanced indicators to make informed investment decisions.
Testing Stock Trading Strategies with Ichimoku Analysis
Learn to test stock trading strategies using Python, focusing on Ichimoku analysis with real-time data, customizable charts, and performance evaluation techniques.
Finding the Best Investments Using Technical Analysis
Learn technical analysis techniques to identify top-performing securities across sectors, create diversified portfolios, and use the Ichimoku Cloud for momentum trading. Includes hands-on Python coding for data analysis and visualization.
Getting Started with Technical Analysis 2 - Bollinger Bands and Advanced Indicators
Learn advanced technical analysis techniques including Bollinger Bands, moving averages, support/resistance lines, and candlestick patterns using Python and data science libraries for stock market analysis.
Getting Started with Technical Analysis Using Python
Learn Python-based technical analysis for stock market predictions, covering data retrieval, calculations, and visualization using libraries like Pandas and Plotly. Ideal for beginners in financial data science.
Technical Analysis - Ichimoku Indicator Tutorial
Learn to use the Ichimoku indicator for comprehensive technical analysis, covering momentum, support, and resistance through its 5 lines. Gain insights into 11 analysis methods using Python and real-time data visualization.
Technical Analysis: RSI and Bollinger Bands Using Python
Learn to perform technical analysis using Python, focusing on RSI and Bollinger Bands. Create customizable charts with real-time data for stock market predictions using various data science libraries.
Technical Analysis: MACD and Stochastic Oscillator Using Python
Learn to use Python for stock market technical analysis, focusing on MACD and Stochastic Oscillator. Covers data retrieval, chart creation, and multiple technical indicators using various data science libraries.
Technical Analysis with Python for Stock Market Predictions
Learn Python-based technical analysis for stock market predictions, covering data retrieval, chart patterns, moving averages, and advanced visualization techniques using data science libraries.
Python for Data Science - Mastering NumPy, Pandas, and Plotly
Comprehensive tutorial on using NumPy, Pandas, and Plotly for data science in Python. Covers core concepts and practical applications with hands-on examples and free resources.
Investment Analysis: Python for Finance - Part 9
Learn investment analysis using Python: calculate Alpha, Beta, CAPM, expected returns, and Sharpe Ratio. Practical coding examples provided for hands-on experience in financial data analysis and portfolio management.
Investment Portfolio Management with Python - Maximizing Return and Minimizing Risk
Learn to maximize returns and minimize risk using Markowitz Portfolio Optimization and Sharpe Ratio. Master efficient frontier, volatility analysis, and advanced data science techniques for investment portfolio management.