Duration: 16 hours / 2 days
Knowledge: Beginner
Trainer: Lada Banić
Training Methods: Virtual Class

Data Science is the most significant trend in the field of analytical data, which requires a wider picture of data related to data, analysis and presentation of data. Predictive analytics as a part of the field of data science gets more importance because traditional reports can answer the past and the present, but not the future.

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Overview

After actively participating in the Data Science and Predictive Analytics education you will be ready to apply Data Science experience to address business challenges by using your own data.

This training is not just focused on the use of algorithms for predictive analytics but learning of the entire data science methodology, which includes defining business problems and data sets, data processing, building predictive models, and in the end evaluation of models.

Audience

This training is intended for all interested students regardless of their previous knowledge, whether you are a beginner or want to upgrade their knowledge of Data Science methodology. This training requires basic mathematical and statistical knowledge.

What you will learn

After education, the student will be ready to ask right business questions, define and join different data sources, process and structure inaccurate data, apply prediction models for future events, and interpret and present Data Science solutions.

Syllabus

In this module you will need to learn what Data Science is, where you can use it in business and learn the skills needed to work on Data Science projects.

In this module you will learn what Data Science Methodology is, why it’s important in Data Science projects and how it is implemented on real projects.

  • Workshop

In this module you will learn what are descriptive and predictive analytics and how to use them together to extract useful knowledge from the data. You will also learn the most commonly used algorithms in predictive analytics such as regression, decision tree, logistic regression, clustering, and association.

  • Workshop

In this module, you will learn how to prepare and process data for Data Science projects to make predictive algorithms meaningful and accurate. During this module you will learn how to merge data from multiple data sources, structure them, normalize, transform, and remove extreme values.

  • Workshop

In this module, you will apply the Data Science learned theory to concrete examples using KNIME Analytics, the world leader in the Data Science platforms. Also, in this module you will learn how to predict the churn users, anticipate customer creditworthiness, group customers in consumer segments, and create consumer shopping carts based on customer purchases.

  • Workshop

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