MST ROUSONARA BGEUM’s Post

Clean Data = Reliable Insights! 📈 But how do you check if your data is reliable? Start by asking yourself these 5 questions: 👉 Is the data correct and free from errors? 👉 Does it contain all necessary columns to build my KPIs? 👉 Is the data in the right format? 👉 Is it up-to-date or are some entries obsolete? 👉 Is it easily accessible by people who need it? Our Senior Data Analyst gives you the answers you need to improve your data-cleaning process now: https://lnkd.in/ei2Zwqbg #DataCleaning #DataManagement #DataAnalyst

When you are getting ready for data analysis, you might realize you don’t have the data you need or you don’t have enough of it. In some cases, you can use what is known as proxy data in place of the real data. Think of it like substituting oil for butter in a recipe when you don’t have butter. In other cases, there is no reasonable substitute and your only option is to collect more data. From the "Process Data from Dirty to Clean" course, it can be concluded that there are a few issues that a data analyst will face, namely, no data, too little data, and wrong data. #googledataanalytics #dataissues

Data Models - Thinking Dates If the attribute is a date make sure it is a date in the data model. If there are problems loading the data make sure that you fix it. Don't even consider loading it to a varchar. You will regret that always. Dates are one of the most important data types, and if you keep it a date you open up a lot of functions. Also, don't assume all your dates and times are in the same time zone. Ask for confirmation in writing so you can sure. Also, if you are supporting multiple time zones you may need multiple attributes; one on local time and one on system time. You also may need to confirm the business definition for business quarters - to make sure you have the data aligned correctly. #data #database #datamodel

Day 1 of posting small information on data analysis. What makes a good analyst? In some countries, the national standard is YYYY-MM-DD so October 12, 2020 becomes 2020-10-12. In the United States, (MM/DD/YY) is the accepted format, so October 12, 2020 is going to be 10/12/20. Now, think about what would happen if you were working as a data analyst for a global company and didn’t check date formats. Well, your data integrity would probably be questionable. Any analysis of the data would be inaccurate. Imagine ordering extra inventory for December when it was actually needed in October! A good analysis depends on the integrity of the data, and data integrity usually depends on using a common format. So it is important to double-check how dates are formatted to make sure what you think is December 10, 2020, isn’t really October 12, 2020, and vice versa. #Dataanalysis #DataMining #DataScience #DataInsights #DataAnalytics #DataEngineering  #PredictiveAnalytics #DataDrivenDecisions 

In my previous post, I talked about some constraints you can use to validate your data. Below are practical examples of how to use some of these constraints to validate your data while creating the tables. Doing this will give you an error free data. There are other constraints you can also use which are: Primary key, Foreign key, Int, Default etc. Do you find this post useful?. Let us know in the comments. #datavalidation #dataanalysis #datacleaning #dataanalystjobs.

"Data management is very crucial to organisations in order to manage the life cycle of Data in an effecive manner"📌 #dataanalyst# #datagovernance# #dataenablement#

Check out my blog post https://wix.to/NyeqUW9 Data analysts are the key to unlocking inefficiencies and improving business performance. Start optimizing with data now! https://wix.to/6LLk6zv #DataAnalysis #Optimization 🚀 #dataanalyst #decisionmaker #analystjourney Follow me for more. 😊

Any profession is about building useful habits. This is especially true for the Data Analyst profession. To build useful habits in Data Analysis, at least, you need to: 1/ Get access to raw data in tabular form and look through it 2/ While building any measure, divide complex calculations into steps 3/ Learn how to trace intermediate results 4/ Always test resulting measures on visual 5/ Assess whether results are aligned with common sense 6/ Check the measure with a few of the most often-used dimensions Even if it sounds like overcomplication, trust me: the most successful Data Analysts always do more checks than you can imagine. #dataanalysis #usefulhabits

Data Cleaning Checklist: ➡️If you're just starting out in the world of data analytics, hopefully this checklist helps demystify the concept of "data cleaning". ✔️ Missing data - Decide if you're going to omit the datapoint, mathematically estimate the missing data using statistical methods, or use an external source to fill in the missing data. ✔️ Duplicate data - Identify duplicate data and what it means in context. Is the duplicate an error that needs to be deleted? Or is it possible that you could have two of the same data point? ✔️ Formatting errors - Ensure all data is rounded to the correct decimal place, all data is aligned correctly, and the data format is consistent within columns. ✔️Incorrect data types - Ensure all of your data is pulled as the correct data type (ex. making sure that integers are not used for money values). ✔️ Outliers - Identify data points that are +/- 2 standard deviations from the mean, and double check that these values are correct. If they are correct, they may require further investigation. #dataanalytics #datacleaning #dataanalysis #dataanalyst

The first step in data analysis is to identify what questions the data analysis needs to answer, who will use the information and what decisions or key documents the information will support. I'm glad to have successfully completed this course "Data Analysis".