Google Data Analytics Professional Certificate (part 2)

This is my review article on second course series in Google Data Analytics professional certificate part(2 of 8). I shall walk you through the summary of skills and best practices that you would gain in this course.

Course Name: Ask Questions to make data-driven decisions

Instructor: Ximera, Finance analyst, Google.

Course Duration: 15 hours

Skills covered:

• Asking SMART and effective questions
• Structuring how you think
• Summarizing data
• Putting things into context
• Managing team and stakeholder expectations
• Problem-solving and conflict-resolution

Concepts that are covered in each week.

Week 1:

Structured Thinking: Structured thinking is the process of recognizing the current problem or situation, organizing available information, revealing gaps and opportunities, and identifying the options.

The data analyst works on 6 common problem types.

1. Making predictions,
2. Categorizing things,
3. Spotting something unusual,
4. Identifying themes,
5. Discovering connections and
6. Finding patterns.

Asking Highly effective questions using SMART technique:

Here’s an example that breaks down the thought process of turning a problem question into one or more SMART questions using the SMART method: What attributes do people look for when buying a new house?

• Specific: Does the question focus on a particular house parameter?
• Measurable: Does the question include a feature rating system?
• Action-oriented: Does the question influence creation of different or new feature packages?
• Relevant: Does the question identify which features make or break a potential house purchase?
• Time-bound: Does the question validate data on the most important features from the last decade?

Week 2:

1. Learnt about the key difference between Data driven and Data inspired decisions.
2. Data driven decision making is something where we will use the data and the metrics to make informed decisions whereas Data-inspired decision-making explores different data sources to find out what they have in common.
3. You will emphasize more on learning qualitative and quantitative data in business.
4. Big Data vs Small Data and the ways to approach with such data sources.
5. You will learn about the types of dashboards and the learn the application of chart types.
6. Importance of dashboards in the business decision making process.

Week 3:

1. You will encounter hands on exercises with spreadsheets which is a powerful tool in a data analyst tool kit. With tons of resources and formulas provided to explore, it is a decent introduction to formulas, functions and creating pivot tables.
2. The course focuses on real world scenarios where in you have to put your legs in the shoes of a data analyst to solve the problem.
3. It reinforces the theoretical learning with learning log activity which is creating and following along the course with our own case study. Applying each learning to our case study eventually improves our ability to work on real business problems.
4. The importance of context and the need of it in the first place for a data analyst to keep things simple from the beginning and in every stage of a data science project.
5. You will learn about SOW (Scope OF Work) document and will also end up creating an own SOW for a real world case study.

A scope of work or SOW is an agreed- upon outline of the work you’re going to perform on a project. Here’s what might be in scope of work: deliverables, timeline, milestones, and reports.

Week 4:

Here is where you will get the cherry on the cake. Now comes the very much essential and often neglected part in a data analyst profession i.e., best practices of the communication.

1. Understanding who the stakeholders are for any projects.
2. Understanding executive, customer-facing and data science teams who are often involved in a data science project.
3. Working effectively with stakeholders involves discussing goals, setting expectations to the stake holders, sometimes saying No and reframing the question to put the context clear.
4. Communicating often is the best of understanding the problem and fixing things on time. It is always important to discuss with the right teams and know their feedback.
5. Other than technical attributes, a successful data analyst must be a curious to ask measurable and SMART questions, be good at handling conflicts politely, address the short comings or lack of resources to the concerned people through any communication channel.

Finally, the course ends with a course challenge that covers almost all the learnings made in the course through case studies. You will end the course with complete understanding of the ASK phase in the life cycle of a data analysis project.

Read my reviews on other parts here:

1. Google Data Analytics Professional Certificate (part 1)

Thank You!