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
The culmination of the previous weeks has been preparation for machine learning modeling. At this stage, we have defined the business problem, we’ve explored and understood how the data relates to the business problem, and we’ve preprocessed the data in preparation for modeling.
This week in the Data Science With R Course Series we’ll cover Data Preparation, where we structure the data in preparation for modeling. This week’s modules will teach you:
From Executive Business Leadership to Data Scientists, we all agree on one thing: A data-driven transformation is happening. Artificial Intelligence (AI) and more specifically, Data Science, are redefining how organizations extract insights from their core business(es). We’re experiencing a fundamental shift in organizations in which “approximately 90% of large global organizations with have a Chief Data Officer by 2019”. Why? Because, when the ingredients of a “high performance data science team” are present (refer to this Case Study), organizations are able to generate massive return on investment (ROI). However, data science teams tend to get hung up on a “battle” waged between the two leading programming languages for data science: R versus Python.
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 machine-learning powered productivity booster.
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 machine-learning powered productivity booster.
