Data science for executives

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Gabriel Guillermo Iturralde Duenas

2:34:52

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1 - Introduction

1 - Welcome all.mp4

01:36

2 - Data science tools

2 - ETL Extract Transform and Load Data.mp4

20:13

3 - Data visualization.mp4

15:36

4 - Statistics and Probability.mp4

32:23

3 - Data science implementation

5 - Setting up python environment for data science.mp4

21:07

6 - Data visualization on python and Google Analytics.mp4

15:33

7 - Data science implementation.mp4

25:30

8 - Machine learning implementation.mp4

22:54

Description

Machine learning, data visualization and hands on experience.

What You'll Learn?

Understand what are the main concepts behind data science for different industries
Learn about the tools available for data science
Getting real hands-on experience on the topics
Building your own algorithm based on Kaggle data
Get to know how to implement and ML using python
Get insights from different datasets.

Who is this for?

Business persons that want to extend their knowledge on data visualization

Intermediate python developers that are willing to learn data science

Business persons that want to find another business opportunities

More details

Description
This course is an intermediate course for professionals working in different industries that want to understand and get more insights from the data they manage at their work or from an aside project. The idea is to provide real hands on experience and an interactive approach to learn data science and some machine learning techniques. We will explore tools such as data visualization, statistics and machine learning as skills for analyzing data. The course include programming exercises with different datasets for python. We will be using additional tools such as Google Analytics to analyze and visualize data. Furthermore, we will discuss different application from different industries such as e-commerce, energy, medical and other business applications. Is intended for any kind of learner that already has some basic programming skills on python coding.
The course will be divided into 2 main modules:
1. Tools for data science.
-ETL: Extract, Transform and Load.
-Data visualization.
-Statistical tools.
2. Implementing data science.
-Setting up a environment for data science.
-Data visualization implementation.
-Data science implementation.
-Machine learning implementation.
The main objectives of the course:
1. Understand what are the main features and tools involved with data science.
2. Understand and infer information from graphical sources.
3. Show data insights through reports and other graphical tools.
4. Implement statistical tools for analyzing the behavior and distribution of our data.
5. Implement machine learning methods (specifically classification methods) to predict the behavior of our data.
Who this course is for:
Business persons that want to extend their knowledge on data visualization
Intermediate python developers that are willing to learn data science
Business persons that want to find another business opportunities

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My main professional and academic experience has been focused on incorporating technological solutions to different industries through data engineer and machine learning techniques. I own 2 startups in the tech-med industry, one in the solar energy industry and I was a part-time professor at Universidad San Francisco de Quito. My two MS were focused on Biomedical Robotics and Neural Engineering with a focus on ML techniques. Here I have worked in several research projects related with using brain computer interfaces to improve the functional recovery of post-stroke hemiparetic patients. My last MS thesis is based in using advanced machine learning algorithms to manipulate human organs and tissues for surgical robots. Overall, I have 8+ years of professional experience in data engineering and machine learning engineering.

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