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

Why 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.

Have this cheat sheet at your fingertips

Download PDF

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.

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

Data Collection: Compile data from different sources and store it for efficient access
Exploration and Visualization: Explore and visualize data through dashboards
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:

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

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

Popular Dashboard Tools

Spreadsheets

Excel
Sheets

BI Tools

Power BI

Tableau

Looker

Customized Tools

R Shiny

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

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?"

Topics

Data Literacy

Data Science

The Data Literacy Imperative: Why Upskilling in Data is Essential for Your Career

Discover the importance of data literacy for your career growth, job market potential, and societal impact. Learn how to upskill in data and stay ahead in the competitive professional landscape.

Matt Crabtree

7 min

The Future of Data Literacy: A Fundamental Skill Shaping Society

Uncover the role of data literacy in the 21st century.

Matt Crabtree

7 min

Google Bard for Data Science Projects

Learn how to leverage Google Bard for project planning, data preprocessing, exploratory data analysis, feature engineering, model selection, hyperparameter tuning, model validation, and building and deployment of a web application.