# 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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