הקורסים החמים ביותר והנדרשים ביותר עכשיו באונליין-לייב עם הנחות חסרות תקדים

חייגו עכשיו: 073-2865544

Data Science for Managers

Description:

Data-driven companies will present larger increase in revenue, compared to not data-driven companies. This is because making data based decisions turns instinctive and sense to less effective and makes commercial sense to become stronger. For instance, the McKinsey Global Institute indicates that data-driven companies are 23 times more likely to acquire customers, 6 times as likely to retain those customers, and 19 times as likely to be profitable as a result.

Data science and its related areas are becoming increasingly present in more and more industries, and machine learning models are becoming part of the basic set of skills for many technical figures. This trend leads many times to misunderstandings between managers and their teams, as they don’t always share the same perception of the business problem.

As a manager of projects or products or employees, as well as other positions, like team leader, you have to understand what question and when to ask, which tool and solution is best for what kind of problem.

In this course we will have an overview of the data science world and be familiar with its most important terms and concepts. The course will cover many real-world use-cases, which will be examined and analyzed through the eyes of the managers, allowing them to adopt the data science jargon and use it properly and to leave the course with an understanding of concepts and the ability to take advantage for immediate business value.

Managers of all levels in data-driven companies.

No technical skills are required.

Part 1 – Introduction – short overview

  • Background and motivation
  • Programming fundamentals

Part 2 – Data Preparation

Part 3 – Regression

  • Introduction and measures
  • Linear regression

Part 4 – Classification

  • Introduction and measures
  • Decision tree
  • Logistic regression
  • k-nearest neighbors and the metric concept

Part 5 – Clustering

  • Introduction and measures
  • k-means
  • Agglomerative clustering and the linkage concept

Part 6 – Miscellaneous 

  • Deep Learning
  • Recommender systems
  • Text mining and NLP
  • Going to Production – Internal Use Case – optional
מורן אלקובי מרצהעמית הינו Data Scientist בחברת נאיה טכנולוגיות, מנהל תחום Data Science בנאיה אקדמי ומרצה בכיר ומוביל בתחום. עמית בוגר הטכניון בתואר הנדסת חשמל ופיזיקה ובעל ניסיון רב בהובלת פרויקטים טכנולוגית עתירי מידע
  • טרם נקבע מועד פתיחה
  • 09:00-16:30daysימים ושעות
  • 24academic hours שעות אקדמיות
  • בסיסיcourse levelרמת הקורס
  • עברית/Englishlanguageשפת הדרכה
  • לבדיקת התאמה לקורס
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