Data Science and Machine Learning Consulting
The key aspect of data science is to closely work with business stakeholders to understand their goals and determine how data can be used to achieve those goals. As output in this process, we want to make value out of data.
Data Scientist gathers information from various sources and analyzes it in order to understand how your business performs. Working with large data sets and using them to identify trends we reach meaningful conclusions to inform strategic business decisions. Having discovered knowledge in your data a Machine Learning Engineer leverages it to build Artificial Intelligence tools to automate certain processes within your company.
The core hard skills in day-to-day work:
- Probability and Statistics: explore and understand more about the data, identify the underlying relationships, predict future trends, determine patterns, uncover anomalies
- Calculus and Linear Algebra: derivatives and gradients, step function, sigmoid function, logit function, ReLU (Rectified Linear Unit) function, cost function, plotting of functions, minimum and maximum values of a function, scalar, vector, matrix, and tensor functions
- Programming, Packages, and Software: Python, SQL, Julia, TensorFlow
- Data Visualization: plot data, determine relationships between variables, visualize areas that need attention, display trends, connections, visualize the volume of information, client reporting
- Machine Learning / Deep Learning: K-nearest neighbors, Random Forests, Naive Bayes, Regression Models, SVM, Scikit-Learn, XGBoost, PyTorch, TensorFlow, Keras, Time series, Natural language processing, Outlier detection, Computer vision, Recommendation engines, Survival analysis, Reinforcement learning, Adversarial learning.
Data Science and Machine Learning Consulting cover the following areas:
- Data Analysis: Identify patterns in data.
- Machine Learning Engineering: Implement algorithms to learn from data.
- Software Development: Build end-to-end systems that servers online artificial intelligence algorithms and enable us to use them to improve your business.
- Business intuition: Connect with stakeholders to grasp the understanding of your business.
- Analytical thinking: Find analytical solutions to abstract business challenges.
- Problem Solving: Ask the right questions to define a problem and a path to solve the problem.
- Critical thinking: Hard data analysis leading to conclusions. Digging deep into data to discover hidden patterns and trends.
- Interpersonal skills: Communicate positive and negative results to the stakeholders
To build a data-driven Artificial Intelligence solution we follow a step-by-step process:
- Identify the problem to solve.
- Determine correct datasets and variables.
- Collect datasets from different sources.
- Clean and validate data.
- Explore data and analyze it to find patterns and trends which describe the problem we are solving.
- Build machine learning models which learn patterns from data
- Test the models to verify they generalize well to new data.
- Build an end-to-end solution enabling a day-to-day application of the models.
- Periodical verification and adjustment of the models.
- Build a data pipeline continuously learning to form online data streams