We'll start the course by defining what data science is. We'll cover the data science workflow, and how data science is applied to real-world business problems. We'll finish the chapter by learning about ways to structure your data team to meet your organization's needs. 

What is Data Science?

Order the tasks

Customer Segmentation Workflow

Tasks

Data Collection

Exploration and Visualization

Experimentation and Prediction

Building a customer service chatbot

Improving OKRs

Applications of Data Science

Options

Traditional Machine Learning

Internet of Things

Deep Learning

Assigning data science projects

Investment research

Building a data science team

Interpreting a team sprint

Editing a job posting

Skills

Data Engineer

Machine Learning Scientist

Matching skills to jobs

Data Analyst

Classifying data tasks

Introduction to Data Science

Now that we understand the data science workflow, we'll dive deeper into the first step: data collection. We'll learn about the different data sources your company can draw from, and how to store that data once it's collected. 

Data sources and risks

Classifying data for security

Creating web data events

Protecting PII

Solicited data

Identifying question purpose

Validating focus group feedback

Net Promoter Score

Collecting additional data

Sorting data sources

Asthma frequency

Data storage and retrieval

Cloud platforms

Querying a database

Which type of database?

Data Collection and Storage

In this chapter, we'll discuss ways to explore and visualize data through dashboards. We'll discuss the elements of a dashboard and how to make a directed request for a dashboard. This chapter will also cover making ad hoc data requests and A/B tests, which are a powerful analytics tool that de-risk decision-making. 

Dashboards

Classifying dashboard elements

Improving a dashboard

Choosing the right dashboard

Ad hoc analysis

Filling out an ad hoc request

Classifying requests

A/B Testing

Creating an A/B testing workflow

Sample size

Intermediate results

Analysis and Visualization

In this final chapter, we'll discuss the buzziest topic in data science: machine learning! We'll cover supervised and unsupervised machine learning, and clustering. Then, we'll move on to special topics in machine learning, including time series prediction, natural language processing, deep learning, and explainable AI! 

Supervised machine learning

When to use Supervised Learning

Features and labels

Model evaluation

Clustering

Supervised vs. unsupervised

Cluster size selection

Special topics in Machine Learning

Classifying machine learning tasks

Sentiment Analysis

Deep Learning and Explainable AI

Finding the correct solution

Should I use Deep Learning?

Prediction

What is data science and how can you use it to strengthen your organization? This course will teach you about the skills you need on your data team, and how you can structure that team to meet your organization's needs. Data is everywhere! This course will provide you with an understanding of data sources your company can use and how to store that data. You'll also discover ways to analyze and visualize your data through dashboards and A/B tests. To wrap up the course, we'll discuss exciting topics in machine learning, including clustering, time series prediction, natural language processing (NLP), deep learning, and explainable AI! Along the way, you'll learn about a variety of real-world applications of data science and gain a better understanding of these concepts through practical exercises.

<h2>Learn to Use Data Science for Business</h2> 
What is data science, and how can you use it to strengthen your organization? This course will teach you about the skills you need on your data team and how you can structure that team to meet your organization's needs.
<br><br>
This course will also provide you with an understanding of data sources your company can use and how to store, analyze, and visualize that data.
<br><br>
<h2>Understand the Data Science Workflow</h2> 
You’ll start with an introduction to data science for businesses, looking at the data science workflow and how to apply it to real-world problems. You’ll also explore how data collection works, looking at how you can source and store data.
<br><br>
<h2>Learn to Analyze and Visualize Your Data</h2> 
You'll also discover ways to analyze and visualize your data through dashboards and A/B tests. To wrap up the course, we'll discuss exciting topics in machine learning, including clustering, time series prediction, natural language processing (NLP), deep learning, and explainable AI.
<br><br>
Along the way, you'll learn about a variety of real-world applications of data science and gain a better understanding of these concepts through practical exercises.
<br><br>
This is an ideal introduction to data science for managers, giving you the chance to learn about this powerful business tool.

Learn about data science for managers and businesses and how to use data to strengthen your organization. 

Data Science for Business

Gather corresponding customer information for each conversation 


Plot the number of conversations vs. the time of day

Create a bar chart of the number of conversations of each type

Create an algorithm that classifies the initial customer question


Use a Markov model to predict possible responses for each question


Building a customer service chatbot

Data Science for Business

Esercizio pratico interattivo