Data Analysis

Data Analysis

1. 1- Ask (SMART)

   • A- define the problem to be solved
     • - Define a problem by examining the current state and determining how it differs from the ideal state. - Usually there's an obstacle we need to get rid of or something wrong that needs to be fixed. - zooming out to understand the big picture or the context  For instance, a sports arena might want to reduce the time fans spend waiting in the ticket line. The obstacle is figuring out how to get the customers to their seats more quickly.
     • Making predictions
       • A company that wants to know the best advertising method to bring in new customers is an example of a problem requiring analysts to make predictions. Analysts with data on location, type of media, and number of new customers acquired as a result of past ads can't guarantee future results, but they can help predict the best placement of advertising to reach the target audience.
     • Categorizing things
       • An example of a problem requiring analysts to categorize things is a company's goal to improve customer satisfaction. Analysts might classify customer service calls based on certain keywords or scores. This could help identify top-performing customer service representatives or help correlate certain actions taken with higher customer satisfaction scores.
     • Spotting something unusual
     • Identifying themes
     • Discovering connections
       • A third-party logistics company working with another company to get shipments delivered to customers on time is a problem requiring analysts to discover connections. By analyzing the wait times at shipping hubs, analysts can determine the appropriate schedule changes to increase the number of on-time deliveries.
     • Finding patterns
       • Minimizing downtime caused by machine failure is an example of a problem requiring analysts to find patterns in data. For example, by analyzing maintenance data, they might discover that most failures happen if regular maintenance is delayed by more than a 15-day window.
   • B- understand the stakeholder expectations
     • know who they are
       • That may include your manager, an executive sponsor, or your sales partners. There can be lots of stakeholders. But what they all have in common is that they help make decisions, influence actions and strategies, and have specific goals they want to meet.
     • communicating with your stakeholder
       • Communicating with your stakeholders is key in making sure you stay engaged and on track throughout the project. So as a data analyst, developing strong communication strategies is very important.

2. 2- Prepare

   • Prepare the overall process of the analysis for the certain project (ex: timeline and deadline of the project // who will have access to the result and row data // how to get the row data // types of question to be asked to the people in the online survey // possible problem they might face during the analysis process // (last step in preparation phase) collecting, generating and storing the information)
   • data life cycle of this stage is in planning, capturing  During planning, a business decides what kind of data it needs, how it will be managed throughout its life cycle, who will be responsible for it, and the optimal outcomes also here we're talking about how we care for our data, how and where it's stored, the tools used to keep it safe and secure, and the actions taken to make sure that it's maintained properly.  For example, let's say an electricity provider wanted to gain insights into how to save people energy. In the planning phase, they might decide to capture information on how much electricity its customers use each year, what types of buildings are being powered, and what types of devices are being powered inside of them. The electricity company would also decide which team members will be responsible for collecting, storing, and sharing that data. All of this happens during planning, and it helps set up the rest of the project
   • They’re asking questions such as, “What internal data is available in the database?” and “What outside facts do I need to research?”
   • Scope of work

3. 3- process

   • This phase is all about getting the details right , So you'll also fix typos, inconsistencies, or missing and inaccurate data. To top it off, you'll gain strategies for verifying and sharing your data cleansing with stakeholders. (build models / eliminate any errors and inaccuracies that can get in the way of results / cleaning row data, transforming it into a more useful format, combining two or more datasets to make information more complete and removing outliers)
   • The data life cycle here is managing where we are clean the data to be analysable

4. 4- Analyze

   • data life cycle of this stage is in analyzing. This is where data analysts really shine. In this phase, the data is used to solve problems, make great decisions, and support business goals.
   • Asking question to myself like: How will my data help me solve this problem? What story is my data telling me?

5. 5- share

   • * Understand visualization  • Create effective visuals • Bring data to life • Use data storytelling • Communicate to help  others understand results 
   • Reports
     • PROS: 1- high-level historical data (data explained properly for weeks/months/years) 2- easy to design  3- precleaned and sorted data 
     • CONS : 1- continual maintainance  2- less visually appealing  3- static 
   • dashboards
     • steps to create an effective dashboard: 1- Identify the stakeholders who need to see the data and how they will use it  ( use the requirment gathering work sheet from google course ) 2- Design the dashboard (what should be displayed) Use these tips to help make your dashboard design clear, easy to follow, and simple: ● Use a clear header to label the information ● Add short text descriptions to each visualization ● Show the most important information at the top 3. Create mock-ups if desired This is optional, but a lot of data analysts like to sketch out their dashboards before creating them. 4. Select the visualizations you will use on the dashboard (use the diagram from twitter to decide)
     • Pros: 1- interactive / automated (time saver) 2- live monitoring for all stakeholders
     • Cons: 1- not easy to make  2- can be confusing 

6. 6- Act

   • • Apply your insights • Solve problems • Make decisions

7. Analytical skills

   • personal skills that will help you to go through the all 6 steps of analysis process professionally 
   • Curiosity
   • understand the context
   • Having Technical mindset
   • data design
   • data strategy