Code&Data Insights

[ Connecting to & Preparing Data ] Q. How to reduce the size of an extract ? A1) Aggregate the data to visible dimension A2) Hide All Unsued field => When we create an extract, we get many size options We can choose only include a sample of the data, Or aggregate to visible dimensions Or use extract filters Or choose the physical table option instead of logical table Q. The best reason to use a ..

Domain 4: Understanding Tableau Concepts 4.1 Understand dimensions and measures 1) Explain what kind of information dimensions usually contain - it contains qualitative values (such as names, dates, or geographical data). You can use dimensions to categorize, segment, and reveal the details in your data. - Dimensions affect the level of detail in the view. => level of detail in a view : how gran..

[ Domain 3: Sharing Insights ] 3.1 Format view for presentation 1) Use color from the marks card - To assign a color to marks in the view, => From the data pane, drag a field to Color on the Marks card - if you drop a discrete field (a blue field), such as Category, on Color, the marks in the view are broken out by category, and each category is assigned a color - If you drop a continuous field,..

[ Domain 2: Exploring & Analyzing Data ] 2.1 Create basic charts 1) Create a bar chart bar 차트의 종류 - horizontal / stacked / side-by-side bars headers : Sub-Catergory sales : axis A stacked bar - by adding a second dimension('segment') to view creates a stacked bar A side-by-side bar 2) Create a line chart ** Line charts always involve a date dimension. line chart의 종류 - discrete / continuous lines..

[ Domain 1: Connecting to & Preparing Data ] 1.1 Create live connections and extracts 1) Create a live connection to a data source Live connection : connecting to the data source directly rather than connecting to a copy. ( default in Tableau Desktop ) Extract : the subset of data (that we can use to improve performance or to take advandatage of Tableau functionality not available or supported i..

Hyper parameters Hyper parameters : configuration values used to control and tune the behavior of machine learning algorithms and models. These parameters have a significant impact on the model's training process and performance. => Properly setting hyperparameters can optimize the model's performance and prevent overfitting. => Model Performance Optimization, Preventing Overfitting, Saving Trai..

분류 예측시 confusion matrix로 TN, FN, TP, FP 를 표현 할 수 있음. 앞이 실제 값, 뒤가 예측값!!! ( FN - N 예측값 : Negative) TN - 실제 데이터셋 Negative(0), 예측 값 Negative(0) => True!!! TP - 실제 데이터셋 Positive(1), 예측 값 Positive(1) => True!!! FP - 실제 데이터셋 Negative(0), 예측 값 Positive(1) => False FN - 실제 데이터셋 Positive(1), 예측 값 Negative(0) => False 정확도 Accuracy rate - 예측 결과와 실제 값이 동일한 건수 계산? (TN + TP) / (TN + FN+TP+FP) 정밀도 Precision - 데..

[ K-Fold Cross Validation ] K-Fold Cross Validation : a validation technique used to evaluate the performance of a model. It involves dividing the given dataset into K different subsets (or folds), sequentially using each subset as the validation dataset, and using the remaining subsets as the training dataset. [ Grid Search ] Grid Search : Grid Search method involves training and evaluating the..