The enterprise-grade process of solving business problems with data science and machine learning
Learning to achieve business objectives using data science enables you to become a strategic asset to your organization. We combine expert instructors, high-performance tools, and long-format courses that teach end-to-end data science projects with a focus on Return-On-Investment (ROI). This gets results.
Data Science is about learning the right tool for the job. Each have pros and cons, and you should have options. We offer multiple tracks depending on your programming language of choice.
(R Track)
The Virtual Workshop is an integrated collection of courses that teaches you 90% of the skills you will need to be successful as a data scientist within an enterprise. The central course teaches the critical analysis. The extension courses target critical skills such as web applications, communication, and package development.
(DS4B 201-R)
DS4B 201-R is a ground-breaking course that takes students from beginner to advanced in 10 weeks! The student evaluates one problem in-depth learning all of the critical aspects of applying data science and machine learning to business. Students consistently rate a 9 of 10 in course satisfaction!
(DS4B 201-P)
DS4B 201-P is the Python equivalent of our ground-breaking DS4B 201-R course. The student learns Python, Apache Spark (great for Big Data), and other tools taking them from beginner to advanced in 10 weeks! The course is under construction but targeted for Q4 2018.
...that show results!
Reducing Time-To-Deliver Data Science By 50%
"I was able to reduce my time-to-deliver projects by 50% after taking Data Science For Business With R (DS4B 201-R) and implementing the Business Science Problem Framework."
- Rodrigo Prado, Managing Partner Big Data Analytics & Strategy at Genesis Partners
Marketing data science can be used to better understand customers, measure marketing effectiveness, and increase marketing ROI. Our expert consultants have the skills to take your marketing efforts to the next level. We can help with:
Customer Journey Analysis: Using data science we can dive deep into understanding how your website vistors become customers and what touch-points and content are most critical to customer conversion.
Audience Segmentation: Understanding what groups exist within your customer-base can be critical to capitalizing on hidden trends and better serving your customers. We can use clustering techniques alongside supervised learning to group customers and make predictitions.
Data Management: We can help move and join data from Google Analytics, Google AdWords, Google BigQuery and internal offline data warehouses. Further, via webscraping we can collect data from public online sources.
Automated Analytics Reporting and Visualizations: We build web-based applications (shiny) and can setup automated reporting (cron).
Organizations are collecting volumes of data on their personnel, much of which is unstructured but very important to the business. Human resources data science can be used to better understand employee engagement, measure and forecast turnover, and measure the causal effect of HR decisions on the business. We can help with:
Employee Turnover Analysis: Characterizing the associated turnover risk of given populations.
Job Change Analysis: Investigating segments that are more/less likely to experience job changes and promotions.
Payroll and Labor Cost Analysis: Show the importance of Human Resources on the business.
Programmatic Structuring of Data: Collect, clean and manipulate data from textual files and other unstructured data sources.
Automated Analytics Reporting and Visualizations: Web-based applications (shiny) and automated reporting.
Process industries generate enormous volumes of data, but many have failed to make use of this mountain of potential intelligence. Manufacturing data science can be used to better understand process and quality control, detect anomalies and optimize manufacturing processes. We can help with:
Predictive Maintenance Analysis: Build models using the historical performance data of machines to forecast when one is likely to fail, limit the time it is out of service, and identify the root cause of the problem.
Profit-Per-Hour Maximization: Determine the effect of large numbers of parameters on sales and provide intelligence on how best to capitalize by leveraging critical parameters.
Yield Energy Throughput Analysis: Optimize yield, throughput, and material costs to maximize the profitability of each process step.
Automated Analytics Reporting and Visualizations: Web-based applications (shiny) and automated reporting.
Supply chain logistics and inventory management can be optimized to reduce costs while maintaining service levels. We can help with:
Demand Forecasting: Build forecast models that more accurately predict demand, which in turn enables better planning, supply chaing logistics, and inventory management.
Inventory Imbalance: Reduce instances of excess and insufficient inventory through optimization.
Automated Analytics Reporting and Visualizations: Web-based applications (shiny) and automated reporting.
Financial instutitions collect massive amounts of customer and transaction data from ATM’s, offline bank branches, mobile app, call centres, online banking,. We can help with:
Sentiment Analysis: Textual data can be combined with financial data to predict future performance. Topic clustering and topic classification are used in this area.
Transactional Analysis: Data science can add great value to financial institutions by helping them find attributes and patterns which have increased probability for fraud and can better understand customer behavior.
Pricing Analysis: Data science helps financial institutions forecast various profitability components such as charge-off accounts, delinquency and closure that help them make effective product and pricing decisions.
Automated Analytics Reporting and Visualizations: Web-based applications (shiny) and automated reporting.
Fraud analytics can reduce exposure to fraudulent activities, increase compliance with government regulations, and reduce costs associated with fraud both from fraud and fees from incompliance. We can help with:
Anti-Money Laundering (AML): Criminal networks that launder money exhibit statistically anomalous behavior that can be mined and detected via network analysis.
Credit Card Fraud: Text mining and machine learning can offer prediction, detection and prevention of fraud at an early stage.
Automated Analytics Reporting and Visualizations: Web-based applications (shiny) and automated reporting.
Matt is giving a workshop on Machine Learning with H2O and a talk on Data Science For Business With Applications In R And H2O. Come see the fun and learn our tools and systems for success!
Matt spoke with Kate Strachnyi about the crazy transition of going full time with Business Science.
Matt spoke with Kirill Eremenko, Founder of SuperDataScience, on the importance of giving back to the data science community! Listen to Episode 165 with Kirill and Matt!
Matt will give a talk on tibbletime and tidyquant including the project roadmap for tidy time series analysis and extendibilty!
Davis will be speaking on the future of time series in the tidyverse. See Davis for tidyquant stickers!!!
Matt spoke with Kirill Eremenko, founder of SuperDataScience, on how tidyquant came to be, how the Business Science blog is used to help data scientist learn, R vs Python in business, and more! Listen to the episode with Kirill and Matt!
Matt gave a talk on HR Analytics: Using Machine Learning to Predict Employee Turnover
Matt spoke with Mike Delgado about HR Analytics, R and more! Check out the Facebook LIVE video here!
Matt presented on time series analysis including R packages tidyquant, timetk, sweep, and tibbletime.
New tools for performing financial and time series analysis within the tidyverse
Data Science and Machine Learning in business begins with R. Why? R is the premier language that enables rapid exploration, modeling, and communication in a way that no other programming language can match: SPEED! This is why you need to learn R. Time is money, and, in a world where you are measured on productivity and skill, R is your best friend.
Artificial intelligence (AI) has the potential to change industries across the board, yet few organizations are able to capture its value and realize a real return-on-investment. The reality is that the transition to AI and data driven analysis is difficult and not well understood. The issue is twofold, first, the necessary technology to complete such a task has only recently become mainstream, and second, most data scientists are inexperienced in their respective industries. However, with all the uncertainty surrounding this topic, one hedge fund has managed to navigate through these challenges and accomplish what many companies are failing to do: building a high-performing data science team that achieves real return-on-investment (ROI).
Data Science is an amazing field of research that is under active development both from the academia and the industry. One of the saddest facts in the real-world is that most data science projects in organizations fail. Here I’ll present a new iteration of an agile framework called Business Science Problem Framework (Download PDF here) to implement data science in a way that enables decision making to follow a systematic process that connects the models you create to Return On Investment (ROI) and show the value that your improvements bring to the business. The end result is that the BSPF is an agile framework, and we are working to develop a new visualization (BSPF 2.0) that conveys this agility.
Model interpretability is critical to businesses. If you want to use high performance models (GLM, RF, GBM, Deep Learning, H2O, Keras, xgboost, etc), you need to learn how to explain them. With machine learning interpretability growing in importance, several R packages designed to provide this capability are gaining in popularity. We analyze the IML package in this article.
