Data Science A-Z™: Real-Life Data Science Exercises Included

Data Science A-Z™: Real-Life Data Science Exercises Included

Study Data Science step-by-step by actual Analytics examples. Data Mining, Modeling, Tableau Visualization and extra!

What you’ll study

  • Efficiently carry out all steps in a posh Data Science challenge
  • Create Fundamental Tableau Visualisations
  • Carry out Data Mining in Tableau
  • Perceive easy methods to apply the Chi-Squared statistical check
  • Apply Unusual Least Squares methodology to Create Linear Regressions
  • Assess R-Squared for every type of fashions
  • Assess the Adjusted R-Squared for every type of fashions
  • Create a Easy Linear Regression (SLR)
  • Create a A number of Linear Regression (MLR)
  • Create Dummy Variables
  • Interpret coefficients of an MLR
  • Learn statistical software program output for created fashions
  • Use Backward Elimination, Ahead Choice, and Bidirectional Elimination strategies to create statistical fashions
  • Create a Logistic Regression
  • Intuitively perceive a Logistic Regression
  • Function with False Positives and False Negatives and know the distinction
  • Learn a Confusion Matrix
  • Create a Strong Geodemographic Segmentation Mannequin
  • Remodel impartial variables for modelling functions
  • Derive new impartial variables for modelling functions
  • Test for multicollinearity utilizing VIF and the correlation matrix
  • Perceive the instinct of multicollinearity
  • Apply the Cumulative Accuracy Profile (CAP) to evaluate fashions
  • Construct the CAP curve in Excel
  • Use Coaching and Check information to construct sturdy fashions
  • Derive insights from the CAP curve
  • Perceive the Odds Ratio
  • Derive enterprise insights from the coefficients of a logistic regression
  • Perceive what mannequin deterioration really appears like
  • Apply three ranges of mannequin upkeep to forestall mannequin deterioration
  • Set up and navigate SQL Server
  • Set up and navigate Microsoft Visible Studio Shell
  • Clear information and search for anomalies
  • Use SQL Server Integration Providers (SSIS) to add information right into a database
  • Create Conditional Splits in SSIS
  • Take care of Textual content Qualifier errors in RAW information
  • Create Scripts in SQL
  • Apply SQL to Data Science tasks
  • Create saved procedures in SQL
  • Current Data Science tasks to stakeholders

Necessities

  • Solely a ardour for achievement
  • All software program used on this course is both out there for Free or as a Demo model

  • Anyone with an curiosity in Data Science
  • Anyone who needs to enhance their information mining abilities
  • Anyone who needs to enhance their statistical modelling abilities
  • Anyone who needs to enhance their information preparation abilities
  • Anyone who needs to enhance their Data Science presentation abilities


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