1. Foundations of Data Science

1. Introduction of data science concepts

1.1. What is data science vs. data analytics

1.1.1. Data science

1.1.1.1. an entire field dedicated to making data more useful

1.1.1.1.1. Produces broad insights that concentrate on which questions should be asked about data

1.1.1.1.2. Confronts what is unknown by using advanced techniques to make predictions about the future

1.1.1.2. A data scientist is a professional that **uses raw data** to develop new ways to **model data** and understand **the unknown**

1.1.2. Data analytics

1.1.2.1. a subfield of the larger data science discipline

1.1.2.1.1. Emphasizes discovering answers to questions being asked

1.1.2.1.2. Determines actionable insights that can be applied immediately based on existing queries

1.1.3. The connections between data science and data analytics

1.1.3.1. share a fundamental goal: **discover insights** that can be used to lead an organization to improve and grow

1.1.4. Explore your data toolbox

1.1.4.1. The R programming language

1.1.4.1.1. used by researchers and academics

1.1.4.1.2. can create complex statistical models

1.1.4.2. The Python programming language

1.1.4.2.1. taught in this program

1.1.4.2.2. emphasizes readability

1.1.4.2.3. flexible

1.1.4.2.4. visually uncluttered

1.1.4.2.5. online communities and resources

2. The impact of data today

2.1. Two categories of data careers

2.1.1. Technical

2.1.1.1. expertise in mathematics, statistics, and computing

2.1.1.2. build models and make predictions

2.1.1.3. explore datasets

2.1.2. Strategic

2.1.2.1. intepret information for an organization's operations, finance, research, and development

2.1.2.2. work aligns with business strategy

2.2. Industries

2.2.1. Finance

2.2.1.1. assess risks

2.2.1.2. monitor market trends

2.2.1.3. reduce fraud

2.2.1.4. create a more stable financial system

2.2.2. Healthcare

2.2.2.1. process clinical data

2.2.2.2. support early detection

2.2.2.3. more precise diagnoses

2.2.3. Agriculture

2.2.3.1. develop new approaches

2.2.3.2. improve harvesting technologies

2.2.4. Manufacturing

2.2.4.1. predict when to perform preventative maintenance

2.2.4.2. maximize quality assurance

2.2.4.3. respond to logistical issues

2.2.4.4. enable clear communication

2.3. The top skills needed for a data career

2.3.1. Interpersonal skills

2.3.1.1. traits that focus on communicating and building relationships

2.3.2. Active listening

2.3.2.1. allowing team members, bosses, and other collaborative stakeholders to share their own points of view befor offering responses

2.3.3. Critical thinking

2.3.4. Problem solving

2.4. Volunteer data skills to make a positive impact

2.4.1. Charity Navigator

2.4.1.1. is the world's largest and most trusted nonprofit evaluator.

2.4.2. DataKind

2.4.2.1. helps social organizations identify their data and artificial intelligence opportunities, recruits and manages volunteers, and then sees the solutions are used.

2.4.3. Devpost

2.4.3.1. is a place to build products, practice skills, learn technologies, sign up for competitions, and grow your network.

2.4.4. IRS.gov tax exempt organizations listing

2.4.4.1. is a website to find charitable organizations throughout the United States.

2.4.5. Statistics without borders

2.4.5.1. contributes to the common good by providing free statistical, data science, and analytical services.

2.5. Critical data security and privacy principles

2.5.1. PII

2.5.1.1. personally identifiable information

2.5.2. data anonymization

2.5.2.1. the process of protecting people's private or sensitive data by eliminating PII

2.5.3. data aggregation

2.5.3.1. the process of collecting and combining details from a significant number of users in terms of totals or summary

2.6. The practices and principles of good data stewardship

2.6.1. Data stewardship

2.6.1.1. the practice of ensuring that data is accessible, usable, and safe

2.6.2. Respect privacy

2.6.3. Be cautious of unintentional harm

2.6.4. Avoid creating or reinforcing bias

2.6.5. Consider inclusivity

2.6.6. Uphold high standards of scientific excellence

2.7. Data professional responsibilities

2.7.1. Look for patterns and trends with big datasets

2.7.2. Uncover the stories inside data

2.7.3. Help guide decision making

2.7.4. Translate key information into visualizations

2.8. Data engineer responsibilities

2.8.1. Make data accessible

2.8.2. Ensure data ecosystem produces reliable results

2.8.3. Deal with infrastructure for data across enterprise

2.9. Business Intelligence Engineer (or Analyst)

2.9.1. A highly strategic role focused on organizing information and making it accessible

2.10. Five principles for data team building

2.10.1. 1. Adaptability

2.10.2. 2. Activation

2.10.3. 3. Standardization

2.10.4. 4. Accountability

2.10.5. 5. Business impact

3. Your career as a data professional

3.1. Tools

3.1.1. Spreadsheets

3.1.1.1. Google Sheets

3.1.1.2. Microsoft Excel

3.1.2. Databases

3.1.2.1. Google Cloud

3.1.2.2. CloudSQL

3.1.2.3. Oracle

3.1.2.4. Microsoft SQL Server

3.1.3. Programming languages

3.1.3.1. SQL

3.1.3.2. R

3.1.3.3. Python

3.1.3.4. Java

3.1.3.5. C++

3.1.4. Data visualization

3.1.4.1. Tableau

3.1.4.2. Matplotlib

3.1.4.3. Seaborn

3.1.4.4. Google Charts

3.1.4.5. InfoGram

3.1.4.6. ChartBlocks

3.1.5. Dashboards

3.1.5.1. Tableau

3.1.5.2. LookerStudio

3.1.5.3. Microsoft PowerBI

3.2. How data professionals use AI

3.2.1. AI and human data professionals

3.2.1.1. Human data professionals possess skills, abilities, and qualities that AI currently lacks

3.2.1.1.1. Intuition

3.2.1.1.2. Deal with ambiguity

3.2.1.1.3. Interpersonal communication

3.2.1.1.4. Creativity

3.2.1.1.5. Critical thinking

3.2.1.1.6. Leadership

3.2.1.1.7. Factuality

3.3. Opportunities to build relationships

3.3.1. Follow organizations and business leaders on social platforms

3.3.2. Search for data field webinars

3.3.3. Attend data science and data analytics events

3.4. Showcase your skills: How to prepare for the interview

3.4.1. Interview question types

3.4.1.1. Behavioral questions

3.4.1.2. Technical questions

3.4.1.3. Situational questions

3.4.1.4. Subject questions

3.4.2. Applying course skills

4. Data applications and workflow

4.1. Data workflow structure

4.1.1. Ask

4.1.2. Prepare

4.1.3. Process

4.1.4. Analyze

4.1.5. Share

4.1.6. Act

4.2. PACE framework

4.2.1. Plan

4.2.1.1. What are the goals of the project?

4.2.1.2. What strategies will be needed?

4.2.1.3. What will be the business or operational impacts of this plan?

4.2.2. Analyze

4.2.2.1. Acquire data from primary and secondary sources

4.2.2.2. Clean, reorganize, and transform data for analysis

4.2.2.3. Engage in EDA

4.2.2.4. Work with stakeholders

4.2.3. Construct

4.2.3.1. Build and revise machine learning models

4.2.3.2. Uncover relationships in the data

4.2.3.3. Apply statistical inferences about data relationships

4.2.4. Execute

4.2.4.1. Present findings to internal and external stakeholders

4.2.4.2. Answer questions

4.2.4.3. Consider differing viewpoints

4.2.4.4. Present recommendations based on the data

4.3. Key elements of communication

4.3.1. Purpose

4.3.1.1. the reason why communication is taking place

4.3.2. Receiver

4.3.2.1. your audience

4.3.2.1.1. Questions to keep in mind

4.3.3. Sender

4.3.3.1. the person responsible for crafting the message or communication

4.3.3.1.1. Questions to keep in mind

4.4. Seven tips for effective communication

4.4.1. 1. Speak the language of your audience

4.4.2. 2. Invite questions and welcome feedback

4.4.3. 3. Be the connection to the data

4.4.4. 4. Let your visualizations help tell the story

4.4.5. 5. Build positive professional relationships

4.4.6. 6. Identify assumptions about the data

4.4.7. 7. Identify limitations in the data

4.5. Elements of successful communication

4.5.1. Understanding why

4.5.1.1. Having a clear vision of why you are communicating is the first thing you need to consider

4.5.1.1.1. Goals of the project you are communicating about

4.5.1.1.2. What you hope to gain from this communication

4.5.1.1.3. What you’re asking your audience to do

4.5.1.1.4. What you need your audience to understand

4.5.2. Set the stage

4.5.2.1. Need to think about where the communication is taking place

4.5.3. All about time

4.5.4. Active Listening

4.5.5. Asking Questions

4.5.5.1. Ask questions that haven’t been answered already

4.5.5.2. Ask questions that reveal the bigger picture

4.5.5.3. Ask questions that gather information or further the knowledge of the team

4.5.5.4. Ask questions that can help clarify misunderstandings

4.6. Communicate objectives with a project proposal

4.6.1. Project proposals

4.6.1.1. A project proposal's main function is to outline objectives and requirements

4.6.2. Elements of a project proposal

4.6.2.1. Project title

4.6.2.2. Project objective

4.6.2.3. Milestones

4.6.2.4. Tasks

4.6.2.5. Outcomes

4.6.2.6. Deliverables

4.6.2.7. Stakeholders

4.6.2.8. Estimated time

4.7. Connect PACE with executive summaries

4.7.1. Executive summaries

4.7.1.1. Executive summaries are documents that summarize the most important points about a project, giving decision makers a brief overview of the most relevant information

4.7.2. Elements of an executive summary

4.7.2.1. Project title

4.7.2.2. The problem

4.7.2.3. The solution

4.7.2.4. Details/Key Insights

5. Course 1 end-of-course project

5.1. Building a portfolio

5.1.1. Experiential learning

5.1.1.1. Understanding through doing

5.1.1.2. Benefits

5.1.1.2.1. Discover how organizations use data analysis every day

5.1.1.2.2. Identify the specific types of industries and projects that are most interesting

5.1.1.2.3. Gain the confidence necessary to discuss them with potential employers

5.1.2. Porfolio

5.1.2.1. A collection of materials that can be shared with potential employers

5.1.3. Transferable skill

5.1.3.1. A capability or proficiency that can be applied from one job to another