In this data science tutorial, we will cover the different procedures utilized in information science utilizing the Python programming language. crowd.
This instructional exercise is intended to learn data science step-by-step through real-life analytics examples. Data Mining, Modeling, Tableau Visualization, and more!
What you’ll learn
- 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 the 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 modeling purposes
- Derive new independent variables for modeling 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
Requirements
- Only a passion for success
- All software used in this course is either available for Free or as a Demo version
Data Science Tutorial Description
Very Active… Amazingly Viable… Unimaginably Genuine!
This isn’t one of those cushioned classes where all that work out only the manner in which it ought to and your preparation is going great. This course tosses you into the profound end.
in this data science tutorial, you WILL encounter firsthand all of the Aggravation an Information Researcher goes through consistently. Degenerate information, abnormalities, inconsistencies – and so on!
The data science tutorial will provide you with a full outline of the Information Science venture. After finishing these tasks you will be aware:
- Step-by-step instructions to clean and set up your information for investigation
- Step-by-step instructions to perform a fundamental perception of your information
- Step-by-step instructions to display your information
- Step-by-step instructions to bend fit your information
- Lastly, how to introduce your discoveries and wow the crowd
The data science tutorial will give you so many practical exercises that the real world will seem like a piece of cake when you graduate from this class. The data science tutorial has homework exercises that are so thought-provoking and challenging that you will want to cry… But you won’t give up! You will crush it. In this course you will develop a good understanding of the following tools:
- SQL
- SSIS
- Tableau
- Gretl
This data science tutorial has pre-arranged pathways. Utilizing these pathways you can explore the course and join areas into YOUR OWN excursion that will get you the abilities that YOU want.
Or on the other hand, you can do the entire course and put yourself in a position for a mind-boggling vocation in Information Science.
The decision is yours. Join the class and begin advancing today!
See you inside,
Sincerely,
Kirill Eremenko
Who this Data Science Tutorial is for :
- Anybody with an interest in Data Science
- Anybody who wants to improve their data mining skills
- Anybody who wants to improve their statistical modeling skills
- Anybody who wants to improve their data preparation skills
- Anybody who wants to improve their Data Science presentation skills
