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Data Science Cheat Sheet for Business Leaders

This cheat sheet guides you through the basics of how data science can help your business, including building your data science team and the common steps in the data science workflow.
Oct 2019  · 6 min read

Request a demo of DataCamp for BusinessWhy is data science so important for organizations? It allows us to draw meaningful conclusions from all the data around us.

As you may know, we recently launched a non-coding course to help business leaders make sense of how data science can best position their organizations for success. This course touches on a wide range of topics: the skills necessary for data teams, the different types of data sources and storage options, the best way to visualize data through dashboards, special topics in machine learning, and more.

To supplement the course, we’ve also created a cheat sheet for business leaders to use as a reference on the hot topics in data science they need to know, including building your data science team and the common steps in the data science workflow.

Click the image below to download the cheat sheet.

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To learn how DataCamp can help your team become proficient in data science and analytics, visit datacamp.com/business/demo or click here to schedule a demo of our platform.

Request a demo of DataCamp for Business

Data Science Basics 

Types of Data Science 

  • Descriptive Analytics (Business Intelligence): Get useful data in front of the right people in the form of dashboards, reports, and emails  
    • Which customers have churned?
    • Which homes have sold in a given location, and do homes of a certain size sell more quickly?
  • Predictive Analytics (Machine Learning): Put data science models continuously into production
    • Which customers may churn?
    • How much will a home sell for, given its location and number of rooms? 
  • Prescriptive Analytics (Decision Science): Use data to help a company make decisions
    • What should we do about the particular types of customers that are prone to churn?
    • How should we market a home to sell quickly, given its location and number of rooms?

The Standard Data Science Workflow 

  1. Data Collection: Compile data from different sources and store it for efficient access
  2. Exploration and Visualization: Explore and visualize data through dashboards
  3. Experimentation and Prediction: The buzziest topic in data science—machine learning!

Building a Data Science Team

Your data team members require different skills for different purposes.

Data Engineer  Data Analyst  Machine Learning Engineer  Data Scientist
Store and maintain data  Visualize and describe data  Write production-level code to predict with data  Build custom models to drive business decisions
SQL/Java/Scala/Python SQL + BI Tools + Spreadsheets Python/Java/R Python/R/SQL. 

Data Science Team Organizational Models

Centralized/Isolated Embedded Hybrid 
The data team is the owner of data and answers requests from other teams 

Data experts are dispersed across an organization and report to functional leaders

Data experts sit with functional eams and also report to the Chief Data Scientist—so data is an organizational priority

Exploration and Visualization 

The type of dashboard you should use depends on what you’ll be using it for.

Common Dashboard Elements 

Type: Time Series 
What is it best for?:  Tracking a value over time 
Example:  

Time Series

Type: Stacked bar chart 
What is it best for?:  Tracking composition over time 
Example:  

Stacked Bar Chart

Type: Bar chart 
What is it best for?:  Categorical comparison 
Example:  

Bar Chart

Spreadsheets 

ExcelExcel 
Google Sheets Sheets 

BI Tools 

Power BI Power BI 

Tableau Tableau 

Looker 

Customized Tools 

R Shiny R Shiny 

d3.js d3.js 

When You Should Request a Dashboard

When you'll use it multiple times 

When you'll need the information updated regularly 

When the request will always be the same 

Experimentation and Prediction

Machine Learning

Machine learning is an application of artificial intelligence (AI) that builds algorithms and statistical models to train data to address specific questions without explicit instructions.

  Supervised Machine Learning  Unsupervised Machine Learning 
Purpose 

Makes predictions from data with labels and features

Makes predictions by lustering data with no labels into categories
Example 

Recommendation systems, email subject optimization, churn rediction

Image segmentation, customer segmentation 

                                                                                                            Supervised vs Unsupervised Machine Learning

Special Topics in Machine Learning

Time Series Forecasting is a technique for predicting events through a equence of time and can capture seasonality or periodic events.

Natural Language Processing (NLP) allows computers to process and analyze arge amounts of natural language data.

  • Text as input data
  • Word counts track the important words in a text
  • Word embeddings create features that group similar words

Deep Learning / Neural Networks enables unsupervised machine learning using data that is unstructured or unlabeled.                    

Explainable AI is an emerging field  in machine learning that applies AI such that results can be easily understood.
Highly accurate predictions  Understandable by humans
Better for "What?"  Better for "Why?"  
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