Data Science A-Z

Description

Extremely Hands-On… Incredibly Practical… Unbelievably Real! This is not one of those fluffy classes where everything goes perfectly, and your learning journey is smooth and easy. Instead, this course plunges you into the deep end.

In this program, you WILL face firsthand the PAIN that a Data Scientist encounters daily. Corrupt data, anomalies, irregularities—you name it!

This course offers a comprehensive overview of the Data Science journey. By the end, you’ll understand:

How to clean and prepare your data for analysis
How to perform basic visualisation of your data
How to model your data
How to curve-fit your data
And how to present your findings and impress your audience

Packed with extensive practical exercises, this training ensures the real world feels like a breeze once you complete the class. The course includes challenging homework tasks that are thought-provoking and intense—but you’ll persevere and succeed! Throughout this course, you’ll build solid knowledge of the following tools:

SQL
SSIS
Tableau
Gretl

This course also features pre-planned pathways. You can use these to tailor your learning journey, selecting sections that match the skills YOU want to master—or complete the entire course to launch an incredible career in Data Science. The choice is yours. Join the class and start learning today!

What are the requirements?

Only a passion for success
All software used in this course is either free or available as demo versions

What am I going to get from this course?

Successfully perform all steps in a complex Data Science project
Create Basic Tableau Visualisations
Perform Data Mining in Tableau
Understand how to apply the Chi-Squared statistical test
Apply Ordinary Least Squares method to create Linear Regressions
Assess R-Squared for all types of models
Assess the Adjusted R-Squared for all types of models
Create a Simple Linear Regression (SLR)
Create a Multiple Linear Regression (MLR)
Create Dummy Variables
Interpret coefficients of an MLR
Read statistical software output for created models
Use Backward Elimination, Forward Selection, and Bidirectional Elimination methods to create statistical models
Create a Logistic Regression
Intuitively understand a Logistic Regression
Operate with False Positives and False Negatives and know the difference
Read a Confusion Matrix
Create a Robust Geodemographic Segmentation Model
Transform independent variables for modelling purposes
Derive new independent variables for modelling purposes
Check for multicollinearity using VIF and the correlation matrix
Understand the intuition of multicollinearity
Apply the Cumulative Accuracy Profile (CAP) to assess models
Build the CAP curve in Excel
Use Training and Test data to build robust models
Derive insights from the CAP curve
Understand the Odds Ratio
Derive business insights from the coefficients of a logistic regression
Understand what model deterioration actually looks like
Apply three levels of model maintenance to prevent model deterioration
Install and navigate SQL Server
Install and navigate Microsoft Visual Studio Shell
Clean data and look for anomalies
Use SQL Server Integration Services (SSIS) to upload data into a database
Create Conditional Splits in SSIS
Deal with Text Qualifier errors in RAW data
Create Scripts in SQL
Apply SQL to Data Science projects
Create stored procedures in SQL
Present Data Science projects to stakeholders