Complete data science process learningReal-world business applicationsMaster predictive analyticsHands-on with advanced toolsTrainers
TBA
DURATION
16 hours / 2 days
LEVEL
Advanced
LANGUAGES
Croatian, English
LOCATION
Virtual classroom (for larger groups)
Overview
Data Science is the most significant trend in the field of analytical data. It requires a wider understanding of data that’s related to other data, data analysis, and the presentation of data.
Predictive analytics — as a part of the data science field — is getting more important because traditional reports can tell us about the past and the present, but not about the future. Predictive analytics has been the most significant trend in the field of data analytics during the last decade, and the focus is on predicting future outcomes based on patterns in past data.
Using data science and predictive methodologies allows you to discover unknown business insights in your data — insights you were not aware of — and to predict future events with a certain degree of probability.
Audience
The training is intended for participants with basic Python knowledge, data analysts, IT engineers, project managers, and business users from various fields like finance, sales, and HR.
Syllabus
01
Introduction to Data Science
In this module you will learn what Data Science is, what it is used for, how to apply it in a business context, and how to learn the skills you need to work on Data Science projects.
Practical exercises: advanced Python, NumPy, and Pandas
02
Analysis, Preparation and Processing of Data for Data Science Projects
This module focuses on how to prepare and process data for Data Science projects, so that predictive algorithms can provide meaningful and accurate results. During the module you will learn how to merge data from multiple data sources, structure them, normalize, transform, and how to remove the outliers.
Practical exercises: Visualization (Matplotlib + Seaborn), Data preparation
03
Predictive and Descriptive Analytics
The third module teaches you about what descriptive and predictive analytics are, and how to use them together to extract useful knowledge from data. You will also learn the most commonly used algorithms in predictive analytics — such as regression, decision trees, logistic regression, and clustering.
Practical exercises: Linear regression, Decision tree (+Random Forest), Clustering
04
Creating, Evaluating and Enhancing Predictive Algorithms
During this module you will learn how to create your own algorithms and neural networks, and adapt them to the specific needs of your project. Instead of relying on plug-and-play solutions, often not tailored to your needs, you will learn how to create your own models and get more precise, higher-quality results.
Practical exercises: Neural network
05
Project — Applying Predictive Algorithms
In the final module, you will apply the theory and practice learned to a specific example, using all the tools covered during this education. Your job will be to predict whether a patient sent to the ER will survive, based on certain data.
Practical exercises: Project description and data for the project.
Sign Up
Training