Linh Khanh Dang

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on the atlas — 61

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Your present self's string of todays is what ends up creating your future self, the one who is capable of achieving that end-goal.
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You don't need to fully believe in your future self. You just need to believe in your present self and what they're capable of doing today.
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You overcome your self-doubt one day at a time: by identifying the task in front of you, acknowledging your doubts about your ability, and proving to yourself that you can do it by tackling it anyways.
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Instead, bring your attention to the task that's in front of you: going to the store to buy art supplies, making a sketch, playing with colors. Can you overcome your self-doubt about this activity? That’s all you need to do today.
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Can I bring myself to do this thing (write ten pages, apply for five jobs, run a mile) today?
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The mistake that we make is believing that we have to overcome our self-doubt about our ability to achieve the entire future end-goal (write that book, get that job, and so on.)
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Identify your objective. Establish the purpose, goals, and desired outcomes of the meeting, including any questions or requests that need to be addressed. Acknowledge participants and keep them involved with different points of view and experiences with the data, the project, or the business. Organize the data to be presented. You might need to turn raw data into accessible formats or create data visualizations. Prepare and distribute an agenda. We will go over this next.
Lead great meetings | Coursera
The faster everyone agrees, the faster you can perform the first analysis to test the usefulness of the project,
Working with stakeholders | Coursera
start with a description and a quick visual of what you are trying to convey.
Working with stakeholders | Coursera
If you have a good understanding about why you are doing an analysis,
Working with stakeholders | Coursera
If you find that you need to prioritize other projects first, discuss what you can prioritize and when.
Working with stakeholders | Coursera
You can help stakeholders by asking about their goals and determining whether you can deliver what they need.
Working with stakeholders | Coursera
Plan for the unexpected. Before you start a project, make a list of potential roadblocks. Then, when you discuss project expectations and timelines with your stakeholders, give yourself some extra time for problem-solving at each stage of the process.
Working with stakeholders | Coursera
A big part of your job will be collaborating with other data team members to find new angles of the data to explore.
Working with stakeholders | Coursera
Then you share those findings with the data scientist on your team, who uses them to predict how new processes could boost employee productivity and engagement.
Working with stakeholders | Coursera
involve collecting and sharing data about consumers’ buying behavior to help inform product features
Working with stakeholders | Coursera
ay come to you with specific asks
Working with stakeholders | Coursera
Typically they compile information, set expectations, and communicate customer feedback to other parts of the internal organization.
Working with stakeholders | Coursera
less interested in the details.
Working with stakeholders | Coursera
These stakeholders think about decisions at a very high level and they are looking for the headline news about your project first.
Working with stakeholders | Coursera
But there are three common stakeholder groups that you might find yourself working with: the executive team, the customer-facing team, and the data science team.
Working with stakeholders | Coursera
Capture data by the source by connecting spreadsheets to other data sources,
Spreadsheets and the data life cycle | Coursera
rarely shared with upper management
Self-Reflection: Go deeper into dashboards | Coursera
track and maintain their immediate operational processes
Self-Reflection: Go deeper into dashboards | Coursera
time scale of days, weeks, or months, they can provide performance insight almost in real-time.
Self-Reflection: Go deeper into dashboards | Coursera
J2BD đặt ra là thu hút và nuôi dưỡng tệp khách hàng trẻ ngay từ hiện tại để xây dựng nền tảng tăng trưởng bền vững cho tương lai.
3 cách viết job-to-be-done hiệu quả để xây dựng chiến lược thương hiệu
categorizing things involves assigning items to categories; identifying themes takes those categories a step further by grouping them into broader themes.
Six common problem types | Coursera
Data analysts typically work with six problem types
Six common problem types | Coursera
What metrics to measure Locate data in your database Create security measures to protect that data
From issue to action: The six data analysis phases | Coursera
Define the problem you’re trying to solve Make sure you fully understand the stakeholder’s expectations Focus on the actual problem and avoid any distractions Collaborate with stakeholders and keep an open line of communication Take a step back and see the whole situation in context
From issue to action: The six data analysis phases | Coursera
Think about fairness from beginning to end
Consider fairness | Coursera
Use oversampling effectively
Consider fairness | Coursera
People bring conscious and unconscious bias to their observations about the world, including about other people. Using self-reporting methods to collect data can help avoid these observer biases. Additionally, separating self-reported data from other data you collect provides important context to your conclusions
Consider fairness | Coursera
Include self-reported data
Consider fairness | Coursera
Consider all of the available data
Consider fairness | Coursera
Identify surrounding factors
Consider fairness | Coursera
Best practice Explanation Example Consider all of the available data
Consider fairness | Coursera
. Often there will be data that isn’t relevant to what you’re focusing on or doesn’t seem to align with your expectations.
Consider fairness | Coursera
Since you know your audience is sales oriented, you already know that the data visualization you use should: Show sales numbers over time Connect sales to location Show the relationship between sales and website use Show which customers fuel growth
Plan a data visualization | Coursera
You create a SQL query similar to below, where means "does not equal":
Endless SQL possibilities | Coursera
Putting SQL to work as a data analyst
Endless SQL possibilities | Coursera
WHERE field1 LIKE 'Ch%' You can conclude that the LIKE clause is very powerful because it allows you to tell the database to look for a certain pattern! The percent sign % is used as a wildcard to match one or more characters. In the example above, both Chavez and Chen would be returned. Note that in some databases an asterisk * is used as the wildcard instead of a percent sign %.
Endless SQL possibilities | Coursera
Spreadsheets are suitable for organizing, cleaning, and analyzing small to medium datasets. Databases are ideal for storing, managing, and analyzing large and complex datasets.
Choose the right tool for the job | Coursera
spreadsheets and SQL.
More on the phases of data analysis and this program | Coursera
use spreadsheets and structured query language, or SQL, to clean data.
More on the phases of data analysis and this program | Coursera
Cleaning data Transforming data into a more useful format Combining two or more datasets to make information more complete Removing outliers (data points that could skew the information)
More on the phases of data analysis and this program | Coursera
ifferent types of data and how to identify which kinds of data are most useful f
More on the phases of data analysis and this program | Coursera
identifying and locating data you can use to answer your questions
More on the phases of data analysis and this program | Coursera
Takes the time to fully understand stakeholder expectations Defines the problem to be solved Decides which questions to answer in order to solve the problem
More on the phases of data analysis and this program | Coursera
một dịch vụ đạt PMF sẽ kéo theo cả nền tảng; năm dịch vụ dở dang sẽ kéo sập tất cả.
PM Case Study: Vietnamese Super App — Kiến Thức Cơ Bản Product Management