Truly Practical Data Science Training with Real-Life Cases

Are you willing to gain practical skills in Data Science to tackle business tasks? Seek theoretical knowledge to be delivered in a structured way? During this course, attendees will proceed from theory to expert-led hands-on practice that encompasses a set of real cases to solve. In addition, you can submit a use case of choice to develop the expertise needed for your current business concerns.


Why enroll

  • Get the structured information you would otherwise have to look for in different sources
  • Explore the machine learning–related issues the practitioners face and the best practices to address them
  • Get ready-to-use scripts as the basis for creating algorithms of your own to solve business-specific problems
  • Collect valuable insights on the complete development life cycle of an ML solution
  • Get a fully-applicable template of the development life cycle, as well as recommendations for its subsequent adaptation to a changing business environment
  • Each trainee will have 16 hours of online Data Science practice with a personal trainer on the project of your choice.

Who should attend

  • Engineers who want to gain expertise in machine learning tools and frameworks
  • Everyone willing to move from theory to applied knowledge across challenging business tasks

Training program

1
DAY 1
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Core Concepts and Techniques
  • Comprehensive review of the concepts, methods and models on which machine learning is based. In this module you'll learn:
    • Formal notation about ML tasks and definitions
    • Core principles of building an ML algorithms
    • Whole set ML algorithms, from Linear Regression to Random Forest
    • Introduction to core Python packages for ML
We'll cover the algorithms:
  • Linear and Logistic Regression
  • kNN and k-Means
  • Decision Trees and Random Forest
We'll show how to handle classification, regression and clustering tasks.
2
DAY 2
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Feature Engineering and Development Methodology
  • Proven to work recipes and methods that help build better models and develop whole solution. We'll get a hold on a wide range of questions related to building ML models, such as:
    • Feature Engineering
    • Dealing with Missing Data and Outliers
    • Dealing with Imbalanced Classification
    • Advanced Validation Schemes
    • Handling of Versioning of models
    • CRISP-DM as main ML development methodology
3
DAY 3
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Tabular Data
  • Transactional data and structured data sources in general are largely prevalent types of datasets, especially in telecom/banking. Purpose of this module is to show an approach for this data to retrieve useful insights.
    • Data preparation of transactional data
    • Time series specific family of algorithms
    • Statistical and Neural Network approaches for this task
4
PRACTICE
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16 hours of hands-on practice
  • Real Estate Price Forecasting. Using the historical data of the Russian housing market along with demographic data, we will learn how to build a model for forecasting a house price.
  • Customer Income Prediction. We propose to analyze the customer data set in the Google Merchandise Store (also known as GStore, where Google Swag is sold). The goal is to create a model that predicts store revenue per customer.
  • Assessment of loan applications. This is a classic banking task to minimize financial risks. Using the client’s historical data, we will build a model that predicts the probability with which the client will return a bank loan.
  • Your own project. Each trainee can propose a project they'd like to work on.
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Prerequisites
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Altoros recommends that all students have:

  • Programming: Basic Python programming skills, a capability to work effectively with data structures
  • Experience with the Jupyter Notebook applications
  • Basic experience with Git
  • A basic understanding matrix vector operations and notation
  • A basic knowledge of statistics
  • Basic command line operations

All code will be written in Python with the use of the following libraries:

  • Pandas/NumPy are the libraries for matrix calculations and data frame operations. We strongly recommend to browse through the available tutorials for these packages, for instance, the official one
  • Scikit-learn
  • Matplotlib

A workstation with the following capabilities:

  • A web browser (Chrome/Firefox)
  • Internet connection
  • A firewall allowing outgoing connections on TCP ports 80 and 443

The following developer utilities should be installed:

  • Anaconda
  • Jupyter Notebook (will be installed using Anaconda)

All these libraries will be installed using Anaconda

If software requirements cannot be satisfied due to the security policy of your employer, please inform us about the situation to find an appropriate solution for this issue.

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Alexandra Mironova

Alexandra Mironova

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