Quant Science

Building the future of quantitative finance education.

Co-Founder

Business Science

I help data scientists learn how to apply enterprise-grade machine learning in business & finance.

Identified gap in the market for data science courses that teach applied skills to solve real world business problems that led to multimillion dollar business

Scaled business to 3000+ students in first 3-years

Increased revenue 20% Year-over-Year

Used NLP and predictive lead scoring to segment email list leading to 30% subscriber growth

Sample Project: Pricing Recommendation Application (R, Shiny) 

And I teach all of these skills in my R-Track data science program: https://university.business-science.io/p/5-course-bundle-machine-learning-web-apps-time-series

Founder

Bonney Forge

Used predictive analytics to grow sales from $3M/Year-Over-Year to $15M/Year-Over-Year (500% increase)

Created a SQL database that stored leads

Trained technical sales staff to use the database for logging leads and requests for quotation

Applied predictive analytics to score leads & focus on highest likelihood of purchase (LOP)
Resulted in 5X increase in sales revenue

Director of Product Engineering & Market Development

Uncovered unmet demand through analysis of Technical Sales quotations leading to $6M Year-Over-Year business

Reviewed sales inquiry data on missed projects

Uncovered a pattern on projects with short lead times needing specialized products

Developed plans for Fast-Track Program & pitched program to CEO & VPs for buy-in
In first year, Fast Track generated $6M in revenue

Manager of Sales Engineering

Manages the Product Engineering Department, a team of engineers, designers, and document control personnel.
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HIGHLIGHTS:

► Led cross-functional project on key design change to improve fugitive emissions as a result of changing industry and EPA requirements. Design change affected core forged steel valve product line. Led team of senior managers in Operations, Purchasing, and Sales/Marketing. Transition occurred seamlessly with zero impact to customers. (ref. Valve Project Database and Analytics Implementation Project)

► Transitioned group from a support role within the organization to a project management-focused role. Increased efficiency of support functions through process improvement. Re-engineered processes, and developed standard operating procedures (SOPs).

► Aligned projects with strategic and organizational goals. Measured project success based on schedule and budget goals. Held individuals accountable for success of projects, which translated into exceptional performance.

► Oversees Document Control & CAD Management (Adept software) and Document Creation (product literature, videos, etc). Leveraged team's skills with SolidWorks and Adobe Creative Suite to develop media in-house.

Manager of Product Engineering

Bechtel Plant Machinery, Inc

Increased project management responsibilities within the Valves & Auxiliary Equipment department. Received two strategic business awards for leading a high-profile engineering project and response to a highly visible field issue.

Engineer II - Valves & Auxiliary Equipment

Project manager for engineering and design of manually-operated gate, globe, and check valves used within the primary propulsion system of Navy nuclear aircraft carriers and submarines. Primary responsibilities were to manage engineering projects and respond to field issues. Held a DOE L security clearance.

Engineer I - Valves & Auxiliary Equipment

I love data science. But, my story is a bit different. 

I began as an Excel-Guru / Mechanical Engineer. Not a traditional background for a data scientist by any means. 

I used Excel until I reached its limits. 

I found R, and the rest is history:

- I began learning R, which led to applying data science to business 

- By applying data science, my team went from $3M/year to $15,000,000 per year

- I began consulting, which led to trainings all over the world

- I found my passion for teaching, and made a company out of it (Business Science!)

Since finding data science, I haven't worked a day in my life. It's been amazing. 

Let me know if you'd like to learn R, Python, or just want to chat data science.

The 3 types of machine learning (that every data scientist should know). Here's 3 months of research in 3 minutes. Let's go!

1. The 3 Fundamental Types of Machine Learning: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Let's break them down. 

2. Supervised Learning: Supervised Learning maps a set of inputs (features) to an output (target). There are 2 types: Classification and Regression. 

3. Classification: Identifying the category that something belongs to. Often I use Binary Classification for lead scoring to get a class probability (the probability from 0 to 1 of how likely the rowwise observation belongs to a class). Think non-buyer or buyer. 0 or 1. Binary Classification. 

4. Regression: Predicting a continuous value. I commonly use Regression for predicting future values of sales demand. It's a special type of regression called Forecasting. 

5. Unsupervised Learning: Learning from unlabelled data. 2 main types I use are clustering and dimensionality reduction. K-means is the most common clustering algorithm I use, often for clustering customers based on their similarities. I use PCA to reduce the number of columns so other supervised machine learning algorithms run more efficiently and to visualize clusters. 

6. Reinforcement Learning: The idea is that the software learns to take actions based on accumulation of reward. This is the underlying concept of "AI" or Artificial Intelligence, where the software learns to think. 

7. Learning Roadmap (based on real life): I highly recommend learning how to apply concepts 1 to 5 to business applications. I use these all day every day. Number 6 Reinforcement Learning I have never used Reinforcement Learning, but it's a powerful concept. Down the road I may take a stab at it and report back. Skip it until I know more (unless there is a specific application you need it for).

===

Need help improving your data science skills?

I'd like to help. Here's how:

👉 My Free 10 Skills Webinar: I put together a free on-demand workshop that covers the 10 skills that helped me make the transition to Data Scientist: https://lnkd.in/gbEBVf5f

👉 Learn ChatGPT for 10X Faster DS Projects: I have a live workshop where I'll share how to use ChatGPT for Data Science (so you can complete projects 10X faster): https://lnkd.in/gCvh6UAy

If you like this post, please reshare ♻️ it so others can get value (follow me, 🔥 Matt Dancho 🔥 for more data science concepts).

🔥 Matt Dancho 🔥

A big mistake I see data scientists making: Learning tools they never use. I did this. And here’s how to fix it.

A classic case of learning everything is mean, median, and mode.

Data scientists learn how to use all 3 of these. 

Median and mean I use almost every day.

But I can count on one hand how often I have used mode.

The same applies to machine learning, data wrangling, and visualization.

There are 1000's of things you could learn. But what should you spend your time learning?

That’s the difference between a data scientist that takes 6 years and a data scientist that takes 6 months.

Now if you’d like to streamline your learning and take 5.5 years less…

Then I’d like to help.

I have a free 40-minute training where I explain which 10 skills helped me move my career forward.

Why is this super valuable?

Because you can focus on these and it will help give you a plan for going from 6 years to 6 months.

P.S. Here's a link to my free 40-minute data science training: https://lnkd.in/g2kbcRH6

It took me 6 weeks to learn overfitting. I'll share in 6 minutes (business case study included). Let's dive in:

1. Overfitting is a common issue in machine learning and statistical modeling. It occurs when a model is too complex and captures not only the underlying pattern in the data but also the noise.

2. Key Characteristics of Overfitting: High Performance on Training Data, Poor Performance on Test Data, Overly Complex with many parameters, Sensitive to minor fluctuations in training data (not robust).

3. How to Avoid Overfitting (and Underfitting): The goal is to get a model trained to the point where it's robust (not overly sensitive) and generalizes well to new data (unseen during model training). How we do this is to balance bias and variance tradeoff. Common techniques: K-Fold Cross Validation, Regularization (penalizing features), and even simplifying the model. 

4. How I learned about overfitting (business case): I was making a forecast model using linear regression. The model had dozens of features: lags, external regressors, economic features, calendar features... You name it, I included it. And the model did well (on the training data). The problem came when I put my first forecast model into production...

5. Lack of Stability (is a nice way to put it): My model went out-of-wack. The linear regression predicted demand for certain products 100X more than it's recent trends. And luckily the demand planner called me out on it before the purchase orders went into effect. 

6. I learned a lot from this: Linear regression models can be highly sensitive. I switched to penalized regression (elastic net) and the model became much more stable. Luckily my organization knew I was onto something, and I was given more chances to improve. 

7. The end result: We actually called the end of the Oil Recession of 2016 with my model, and workforce planning was ready to meet the increased demand. This saved us 3 months of inventory time and put us in a competitive advantage when orders began ramping up. 

Estimated savings: 10% of sales x 3 months = $6,000,000. 

Pretty shocking what a couple data science skills can do for a business. 

===

Need help applying data science to business?

I'd like to help. Here's how:

👉 My Free 10 Skills Webinar: I put together a free on-demand workshop that covers the 10 skills that helped me make the transition to Data Scientist: https://lnkd.in/gbEBVf5f

If you like this post, please reshare ♻️ it so others can get value (follow me, 🔥 Matt Dancho 🔥 for more data science concepts).

Helping 7,000+ learn Data Science for Business | Marketing Analytics | Time Series Forecasting | Quantitative Finance || @mdancho84 on Twitter

Explaining black box machine learning models is critical to gaining leadership's buy-in and trust. Here's 6 months of research on Explainable ML in 6 minutes (Business Case included). Let's go!

1. Explainable Machine Learning (ML): Refers to techniques that make the outputs and operations of machine learning models understandable to humans. Traditional machine learning models, especially complex ones like deep neural networks, are often seen as "black boxes" because their internal workings are not easily interpretable. 

2. Black-Box Problem: People don't trust what they don't understand. It's that simple. With Explainable ML, you gain: Transparency, Interpretability, Accountability, Fairness and Bias Detection, and Trust. This builds confidence among stakeholders, which is especially important in domains like marketing, finance, and healthcare.  

3. The 2 Types of Explainability Approaches: Model-Specific and Model Agnostic. Let's break them down. 

4. Model-Specific Explainability: Some models are explainable without any added processing. These tend to be simpler models. Linear Regression Coefficients: In linear models, the coefficients indicate the importance and direction of the influence of each feature. Decision Tree Rules: Decision trees provide a clear set of rules and thresholds for decision-making, making them inherently interpretable.

5. Model-Agnostic Explainability: These are methods that can be applied to ANY model. Examples include Feature Importance Scores, SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Partial Dependence Plots (PDP).

6. Why I use Explainable ML? In my $15,000,000 lead scoring model, it initially started out as a Linear/Logistic Regression. This was a simple model. But eventually I upgraded. I went to Random Forest, then XGBoost, then an Ensemble of multiple ML models. With each iteration, predictions (lead scores) became more accurate. But, I could no longer understand why the model was predicting (e.g. Unlike Linear Models, XGBoost has no coefficients).

7. Business Solution: I began using LIME and SHAP. Both offered local explanations (e.g. Why is this specific lead expected to buy at 95% class probability)? The LIME and SHAP visualizations helped me understand my models, trust them, and MOST IMPORTANT explain them to management. 

And that's how I got my management team to trust my $15,000,000 lead scoring model. 

===

Need help applying data science to business?

I'd like to help. Here's how:

👉 My Free 10 Skills Webinar: I put together a free on-demand workshop that covers the 10 skills that helped me make the transition to Data Scientist: https://lnkd.in/gbEBVf5f

If you like this post, please reshare ♻️ it so others can get value (follow me, 🔥 Matt Dancho 🔥 for more data science concepts).

It took me 10 years to master all 24 of these statistics concepts. In the next 24 days, I'll teach them to you one by one (with examples of how I've used them in business cases). Here's what's coming:

1. Probability Distribution
2. Regression
3. Hypothesis Testing
4. Central Tendency
5. Bayesian Statistics
6. Descriptive Statistics
7. Distribution
8. Sampling
9. Confidence Interval
10. Correlation
11. Covariance
12. Dimensionality Reduction
13. Central Limit Theorem
14. Normal Distribution
15. Population and Sample
16. Skewness
17. Variance
18. Data Types
19. Probability
20. Conditional Probability
21. P Values and Level of Significance
22. Variability
23. Over and Under-Sampling
24. Estimation

===

Want help applying data science and statistics to business?

I'd like to help. Here's how:

👉 My Free 10 Skills Webinar: I put together a free on-demand workshop that covers the 10 skills that helped me make the transition to Data Scientist: https://lnkd.in/gbEBVf5f

👉 Learn ChatGPT for 10X Faster DS Projects: I have a live workshop where I'll share how to use ChatGPT for Data Science (so you can complete projects 10X faster): https://lnkd.in/gCvh6UAy

If you like this post, please reshare ♻️ it so others can get value (follow me, 🔥 Matt Dancho 🔥 for more data science concepts).

The 5 best forecasting techniques: My experience of 6-years of demand prediction and statistical competitions put into a 6-min read:

 Given the myriad factors that influence forecasting performance—from the granularity of data and seasonal patterns to the inherent nature of demand (be it volatile, smooth, or intermittent - please see my recent post for a deep dive)—it's clear that no single method can claim the top spot across all scenarios: (if you like it -> scmsync.com)

Based on my experience, I recommend the following methods to align especially well with the following demand clusters:

Suited for Volatile Demand (but not only)
➡ Transformers & Time Series Foundation Models
Method Intuition: Leverages attention mechanisms or large-scale pre-trained models to capture complex patterns across time series data.
Article: "Attention Is All You Need" by Vaswani et al., foundational for Transformers.
Example: Revolutionizing retail by forecasting product demand, thereby optimizing inventory levels.

General Applicability
➡ DeepLearning Ensemble (GluonTS)
Method Intuition: Combines the strengths of multiple deep learning models to enhance forecasting accuracy, adept at handling uncertainties.
Article: "GluonTS: Probabilistic Time Series Models in Python" by Alexandrov et al.
Example: Elevating e-commerce demand forecasting, from overarching categories to specific SKUs.

For Smooth Demand (but not only)
➡ LightGBM & XGBoost
Method Intuition: Utilizes gradient boosting models known for their efficiency with structured data, capturing both linear and non-linear relationships.
Article: "LightGBM: A Highly Efficient Gradient Boosting Decision Tree" by Ke et al.
Example: Streamlining the fashion industry's demand forecasting for new collections.

For Intermittent Demand
➡ Croston's Method & ➡ ATA Method
Method Intuition: Specifically designed for intermittent demand, these methods uniquely forecast the occurrence and size of demand.
Example: Transforming aerospace industry's spare parts demand forecasting.

➡ Time State Modelling
Method Intuition: Captures the dynamics of time series data by modeling the transitions between underlying states, offering insights into the generative process of the data.
Ex.: Applied in automotive supply chains to forecast parts demand, accommodating dynamic market trends and regulatory changes.

Stay tuned for a follow-up post delving into the latest advancements (2023 and beyond) in demand forecasting techniques, venturing beyond the scope of this discussion.

🎁 Bonus: Subscribe to my weekly newsletter about forecasting and risk management for supply chains (scmsync.com) and receive a complimentary Python code for demand forecasting using GluonTS!

Follow me for more insights, and don't hesitate to share this post to spread the knowledge!

#Forecasting #Kaggle #M5 #DataScience #Analytics #BusinessIntelligence #supplychain #demand #planning #inventory #products #timeseries #GluonTS #timestate #prediction 🔥 Matt Dancho 🔥

Why Machine Learning in Finance? This is why.

In traditional buy-and-hold, it's very difficult to get 10X returns in 4 years. But with machine learning, we can let algorithms do the work. Let me explain.

An algorithm has the advantage of being able to learn from many important features.

1. Momentum features
2. Risk features
3. Technical indicators
4. Time Series features

From these features, we get a more complete view. We can develop trading strategies based on the model's predictions.

This is more accurate than a simple momentum indicator. And the results can be very profitable.

ML is not without risk:

1. Universe selection is critical
2. Volatility can be a double-edge sword
3. Overfitting can kill profitability

That's why Time Series Cross Validation is essential.

Want to learn how to get started with algorithmic trading with Python?

Then join us on Wednesday for a live webinar, how to Build Algorithmic Trading Strategies (that actually get results)

👉 Register here (995+ registered): https://lnkd.in/gsWHzQ-W

50% of data scientists who do customer segmentation overlook this step (BIG MISTAKE). Here’s how to fix your customer segmentation models in 30 seconds.

Customer segmentation is one of the most valuable skills a company can gain from their Data Scientists. 

Shocker. Over half of data scientists skip the most important step. 

When you do this right, you expose valuable customer segments. And that makes it as easy as shooting fish in a barrel. 

That’s right- Your company can print money. 💰

The problem is half of data scientists completely overlook feature engineering AND go straight to using tools like PCA, K-Means, and DBSCAN. 

Why? Because they don’t know how (and no one ever taught them the right way).

I for one won’t stand for this. 

Listen, I’m 100% certain that, with simple tweaks to your process, you can do better customer segmentations in 1/10th of the time!

And I want to share it LIVE on Wednesday, April 10th.

What's inside this Learning Lab? You will discover:

✅ Discover how to make large-scale clustering models with 1MM+ customers (that saves the company $1,000,000 per year)! *** THIS IS A GAME CHANGER ***

✅ Gain a skill that can earn you $150,000 or more (and become the customer segmentation expert for your company).

✅ Uncover the insider tricks to fill your data science portfolio with data science projects companies need RIGHT NOW. (Instantly become in-demand)

What's your next step?

Just register & attend live.

Seats are limited.

👉Register Here: https://lnkd.in/ePrPhbB2

Businesses sit on mountains of unstructured data. Tomorrow I'll share how to extract data from PDF documents in R. 

Register for this R-Tip (Drops Sunday at 11AM EST): https://lnkd.in/ef6NsWuu

Did you know that you can make Microsoft Word Reports from R?

You can. And here's a short tutorial to help. 

Article: https://buff.ly/49YBWFJ 

#datascience #rstats

I've been studying Python and AI for business applications for 4 months. One of the most powerful concepts is the idea of RAG (Retrieval-Augmented Generation). Here's what RAG is in 3 minutes (business case included).

1. What is RAG? RAG blends the strengths of two major components in natural language processing: retrieval systems and generative models. RAG enhances a Generative AI model (e.g. LLM) by providing access to external knowledge in real-time. A RAG system has 5 major components.

2. Embedding Model: Vector embedding converts text into a high-dimensional vector space. This allows the system to measure the similarity between pieces of text based on their positions in this vector space. The similarity is used to perform fast retrieval in real-time.

3. Vector Database: The vector database stores the embeddings of documents from domain-specific knowledge bases. Every company has PDF documents, emails, and video conference calls. These can be converted into stored knowledge documents inside of the Vector DBs.

4. Retrieved Contexts: Once the embedding model processes the user's query into a vector, this vector is used to search the vector database for the most semantically similar vectors (documents or text snippets). These contexts improve the Generative AI model's ability to produce a response by providing specific domain information related to the query.

5. Large Language Model (LLM): LLMs like GPT (Generative Pre-trained Transformer) are capable of generating human-like text based on the input they receive. In the RAG framework, the LLM receives two inputs: the original query and the retrieved contexts.

6. Response: The Response is generated by the LLM, which integrates the information from the retrieved contexts with its pre-existing knowledge and the nuances of the query to produce a detailed, accurate, and contextually relevant answer.

7. Business Use Cases: As I build my upcoming Python for Generative AI Course, I have several business prototypes that use RAG:

Project 1:  "Expert in a Box" AI Marketing Assistant: This AI application uses documents on marketing strategy to help improve the business user's ability to develop marketing documents (emails and pitch decks) aligned with the companies product and sales initiatives.

Project 2: AI Agents to Advise Critical Business Actions: Combining RAG and AI Agents, we can create both Business Analyst (SQL + Machine Learning Experts) and Marketing Expert AI's that are knowledgable on different aspects of the business. They can chat with each other to develop holistic business strategies.

===

Want to learn more about Generative AI for business applications?

I am working on a new course, Python for AI Production. There are 3 projects under development.

👉 Join my new AI course waitlist here: https://lnkd.in/ePcscx4k

Did you know that R has a killer forecasting ecosystem?

Here's everything you need to know in under 2 minutes. 

R's forecasting ecosystem is ABSOLUTELY a thing of beauty. 

You get:

1. Time Series Analysis
2. Forecasting
3. Ensembles
4. Resampling & Backtesting
5. AutoML
6. Deep Learning

(full disclosure - I created all the R packages I'm about to cover)

1. Timetk for Time Series Analysis

What it does: 
I made this package to be a 1-stop shop for time series analysis. 

Best Parts:
- Plot time series function
- Summarise by time function

Where to learn more? https://buff.ly/4aoM6zg  

2. Modeltime for Forecasting

What it does:
Forecasting used to take me 10,000 lines of code. 

With modeltime, my workflow is 200.

Best Parts:
- Integrates Tidymodels ecosystem
- Any tidymodel becomes a forecaster

Where to learn more? https://buff.ly/4aoM7Dk  

3. Modeltime Ensemble for Forecast Ensembles

What it does:
"5 bad models makes 1 good model"
- Kaggle 2nd place winner, 2012 

Best parts:
- 1 extra line of code, the modeltime table from 2 becomes an ensemble

Learn more: https://buff.ly/4aoM8am  

4. Backtesting with Modeltime Resample 

What it does:
"Your forecast is only as good as your backtest"

Best parts:
- Resample Accuracy Plots by Time Slices

Learn more:
https://buff.ly/4cm2tyb  

5. Automatic machine learning for time series 

What it does:
H2O AutoML makes insanely powerful machine learning models

Now they can become AutoML forecasters with Modeltime H2O 

Best parts: Make 100 ML forecast models automatically

Learn More: https://buff.ly/49cvtG0   

7. Deep learning for time series with Modeltime GluonTS

What it does:
Connects Modeltime to the GluonTS Deep Learning Forecasters

Best parts:
DeepAR makes 1000s of forecasts at scale

Learn more: https://buff.ly/4cqx9hD  
There you have it. 

The beauty of the time series forecasting ecosystem. 

About 10 years of my work...

In 2 minutes. 
And if you'd like to learn more about time series (+ 100 more R packages)...

👉  Steal my R cheat sheet here: https://buff.ly/4cqx9OF  

Page 3 has all the R packages I mentioned in this thread...
And if you'd like to become a time series forecasting expert for your company (+ land a $100,000+ data science job)...

...Then watch my free 40-minute class where I break down the 10 secrets to becoming a data scientist in under 6 months. 

👉 Watch here: https://buff.ly/49ZGATS 

Did you like this thread?

Follow me (Matt Dancho) for more insane tips, secrets, & hacks that will cut your data science journey in half. 

Enjoy!

What is an AI Agent? And how can data scientists use them in business? This is how:

I'm building a course on Generative AI for data scientists, and one of the biggest upsides is the concept of decision-making with AI agents.

An AI agent refers to a system or software that is capable of acting autonomously in an environment to achieve its designed objectives. These objectives can vary widely, from simple tasks like sorting data, to complex ones like what business strategy should we take to increase revenue or managing an entire business intelligence system.

Key components of an AI Agent:

1. Sensing: AI agents have the ability to perceive their environment, often through data inputs that can include sensors for physical agents (like robots) or data streams for software agents (like web crawlers).

2. Processing: They can process the information gathered to make decisions. This involves some level of artificial intelligence, ranging from basic rule-based systems to advanced machine learning models that can improve with experience.

3. Acting: Based on the processed information, AI agents can take actions to achieve their goals. These actions could be physical (like a robot moving an object) or digital (like an AI program executing a trade on the stock market).

4. Goals: AI agents are goal-oriented, meaning they perform actions with the intention of achieving specific objectives. These goals are defined by the creators of the AI system and can be simple or complex.

5. Adaptability: Many AI agents are designed with the ability to learn from their experiences. This means they can adapt to new information or changes in their environment, improving their performance over time.

Example of AI Agents in Action:
One of the most powerful open-source use cases I've found for AI Agents is the software Devika (an open-source version of Devin). Devika can make complex software by breaking the user's request down into steps and tackling each step with AI agents. 

GitHub Repo: https://lnkd.in/eeqz29CS

Want to learn AI for Data Science and Business Applications?

I'm building a new course. You'll learn the secrets of AI Engineers with a focus on building powerful business solutions. And you'll see how you can integrate what you already know: Machine Learning and Data Science into AI solutions. 

👉 Join the course waitlist here: https://lnkd.in/eFUma7Qi

K-means clustering is essential for Customer Segmentation. 

Learn K-means in under 5 minutes.

Article: https://buff.ly/3J04Wkl 

---

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If I could go back in time (knowing what I know now), this book is what I'd do. 

I wrote it to speed me up. And I've aptly titled my book, "Everything You Should Already Know About Data Science"

You can pick up your copy here: https://buff.ly/3TZ8Xfe

Businesses are sitting on a mountain of unstructured data. The biggest culprit is PDF Documents. This is how to fix it. How to PDF Scrape text and use OpenAI’s API to summarize it in R.

Article: https://lnkd.in/eDP6UDxd

Trading is an unfair game. We have machine learning and algorithms. But Hedge funds and the Wall Street elite have an edge: Better tools, more information, automated execution. That ends tomorrow.  

Tomorrow Jason Strimpel and I will reveal Omega during our live algorithmic training and course launch event. Here's a taste of Jason's proprietary Omega Hedge fund in a box demo:

1. Omega is a hedge fund in a box. 

Omega solves the toughest problem "the last mile". In data science, this is the equivalent of the ML Ops part. Omega is how you take your algorithmic trading strategy into production. 

2. Omega makes execution easy. 

Need to execute 100 orders? No problem. 
Need to log trades in your trade log SQL database? No problem. 
Need to watch your live portfolio and manage risk? No problem. 

3. Omega is designed for data scientists and coders.

It's built in Python. And if you're a data scientist who wants to trade with algorithms, then you're going to feel right at home with Omega. 

If you want to see Jason and I share what we've been working on for 12 months (and see the app that Jason poured 1000+ hours of development into)...

👉 Then register here (seats are limited): https://lnkd.in/gsWHzQ-W

5 overlooked ChatGPT prompts that R data scientists can use to save 10+ hours per week:

1. Generating SQL Queries: 

Prompt: "Given a dataset description with columns such as customer_id, purchase_date, and amount_spent, can you help me construct a SQL query to find the total amount spent by each customer in the last year?"

2. Creating Regular Expressions for Data Cleaning:

Prompt: "I have a dataset with phone numbers in various formats. Can you provide a regular expression to normalize them to a standard format?"

3. Writing Data Processing Scripts in R:

Prompt: "I need to clean a CSV file containing dates in different formats and convert them all to YYYY-MM-DD format. Can you provide a R script using pandas to do this?"

4. Automating Data Validation Checks:

Prompt: "I'm repeatedly receiving datasets from different sources. Can you help me create a checklist or a script to automate the validation of data, such as checking for missing values, duplicate rows, and outliers?"

5. Identifying and Correcting Data Inconsistencies:

Prompt: "I have a dataset where the same categories are spelled differently in various rows (e.g., 'USA' vs. 'U.S.A.'). How can I standardize these values?"

🔥 ​​BONUS PROMPT: Designing and Troubleshooting an R Shiny Web Application:

Prompt: "I'm building an R Shiny web application to visualize time series data from various sensors. I need to allow users to select a sensor from a dropdown menu, choose a date range, and then display the corresponding time series plot. Can you provide guidance on structuring the UI and server logic in R Shiny, and how to troubleshoot common issues such as reactive element updates?"

There you have it- My 5 overlooked chatgpt prompts for R data scientists to save you 10+ hours per week.

Want to learn how to use ChatGPT to solve business problems?

I have a live workshop next Wednesday where you will learn how to do a Customer Churn project entirely using ChatGPT.

👉 Register here (seats are limited): https://lnkd.in/gCvh6UAy

How to make awesome tables in R from Excel.

I made a short tutorial. ❤️ 

Read here: https://buff.ly/3TWcDye 

---

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