“Data Science A-Z™: Real-Life Data Science Exercises Included” course is designed to successfully perform data science tasks. It also helps one to understand how to apply the Chi Squad Statistical test. This course would also help one to transform independent variables for modeling purposes, and also derive business insights.
There is no pre-requisite for this course, the instructor starts from the basics so that students of all levels can understand it. This course will give a practical idea about real-world exercises and it also helps you to learn about SQL, SSIS, Tableau, and Greti. Currently, Udemy is offering Data Science A-Z™: Real-Life Data Science Exercises Included course for up to 87 % off i.e. INR 449 (INR 3,499).
Who can opt for this course?
- Anyone with a passion for data science
- Anyone looking to develop their data mining abilities
- Anyone looking to develop their statistical modeling abilities
- Anyone looking to increase their data preparation capabilities
- Anyone looking to develop their data science presentation abilities
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 21 Hours |
| Rating | 4.5/5 |
| Student Enrollment | 208,256 students |
| Instructor | Kirill Eremenko https://www.linkedin.com/in/kirilleremenko |
| Topics Covered | Areas of Data Science, Adding labels and formatting, Connecting Tableau to an Excel File, Validating Tableau Data Mining with a Chi-Squared test |
| Course Level | Intermediate |
| Total Student Reviews | 32,503 |
Learning Outcomes
- Complete a challenging Data Science project successfully
- Build simple Tableau visualizations
- Use Tableau to perform data mining
- Know how to use the Chi-Squared test in statistics
- Linear regressions should be created using the Ordinary Least Squares approach
- Analyze R-Squared for each kind of model
- For all kinds of models, evaluate the Adjusted R-Square
- Establish a Simple Linear Regression (SLR)
- Multiple Linear Regression should be created (MLR)
- Make some fake variables
- Interpret an MLR’s coefficients
- For developed models, examine the output of statistical software
- Create statistical models using the backward elimination, forward selection, and bidirectional elimination approaches
- Construct a logistic regression
- Understand a Logistic Regression intuitively
- Utilize false positives and false negatives while understanding their differences
- Confusion Matrix must be read
- Build a reliable geodemographic segmentation model
- For modeling purposes, transform independent variables
- Create fresh independent variables to be used in modeling
- Using the correlation matrix and VIF, check for multicollinearity
- Recognize the basic principles of multicollinearity
- To evaluate models, use the Cumulative Accuracy Profile (CAP)
- Using Excel, create the CAP curve
- To create reliable models, use the Training and Test data
- glean knowledge from the CAP curve
- Recognize the odds ratio
- glean business lessons from a logistic regression’s coefficients
- Recognize how model deterioration truly appears
- To stop model deterioration, implement three levels of model maintenance
- Set up and use SQL Server
- The Microsoft Visual Studio Shell installation and navigation
- Data cleaning and anomaly detection To upload data into a database, use SQL Server Integration Services (SSIS)
- In SSIS, create conditional splits
- Correct RAW data’s Text Qualifier issues
- Create SQL scripts
- Projects in data science should use SQL
- Make SQL-stored procedures
- Describe data science projects to interested parties
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Get Excited (05 minutes) | Welcome to Data Science A-Z™ |
| Get the Datasets here | ||
| Extra Resources | ||
| 2. | What is Data Science? (20 minutes) | Intro (what you will learn in this section) |
| The profession of the future | ||
| Areas of Data Science | ||
| IMPORTANT: Course Pathways | ||
| Some Additional Resources!! | ||
| EXTRA: Interview with DJ Patil | ||
| 3. | ————————— Part 1: Visualisation ————————— (01 minutes) | Welcome to Part 1 |
| 4. | Introduction to Tableau (55 minutes) | Intro (what you will learn in this section) |
| Installing Tableau Desktop and Tableau Public (FREE) | ||
| Challenge description + view data in the file | ||
| Connecting Tableau to a Data file – CSV file | ||
| Navigating Tableau – Measures and Dimensions | ||
| Creating a calculated field | ||
| Adding colors | ||
| Adding labels and formatting | ||
| Exporting your worksheet | ||
| Section Recap | ||
| Tableau Basics | ||
| 5. | How to use Tableau for Data Mining (50 minutes) | Intro (what you will learn in this section) |
| Get the Dataset + Project Overview | ||
| Connecting Tableau to an Excel File | ||
| How to visualize an AB test in Tableau? | ||
| Working with Aliases | ||
| Adding a Reference Line | ||
| Looking for anomalies | ||
| A handy trick to validate your approach/data | ||
| Section Recap | ||
| 6. | Advanced-Data Mining With Tableau (01 hour 29 minutes) | Intro (what you will learn in this section) |
| Creating bins & Visualizing distributions | ||
| Creating a classification test for a numeric variable | ||
| Combining two charts and working with them in Tableau | ||
| Validating Tableau Data Mining with a Chi-Squared test | ||
| Chi-Squared test when there are more than 2 categories | ||
| Quick Note | ||
| Visualizing Balance and Estimated Salary distribution | ||
| Extra: Chi-Squared Test (Stats Tutorial) | ||
| Extra: Chi-Squared Test Part 2 (Stats Tutorial) | ||
| Section Recap | ||
| Part Completed | ||
| 7. | ————————— Part 2: Modelling ————————— (03 minutes) | Welcome to Part 2 |
| 8. | Stats Refresher (32 minutes) | Intro (what you will learn in this section) |
| Types of variables: Categorical vs Numeric | ||
| Types of regressions | ||
| Ordinary Least Squares | ||
| R-squared | ||
| Adjusted R-squared | ||
| 9. | Simple Linear Regression (22 minutes) | Intro (what you will learn in this section) |
| Introduction to Gretl | ||
| Get the dataset | ||
| Import data and run descriptive statistics | ||
| Reading Linear Regression Output | ||
| Plotting and analyzing the graph | ||
| 10. | Multiple Linear Regression (01 hour 30 minutes) | Intro (what you will learn in this section) |
| Get the dataset | ||
| Assumptions of Linear Regression | ||
| Dummy Variables | ||
| Dummy Variable Trap | ||
| Understanding the P-Value | ||
| Ways to build a model: BACKWARD, FORWARD, STEPWISE | ||
| Backward Elimination – Practice time | ||
| Using Adjusted R-squared to create Robust models | ||
| Interpreting coefficients of MLR | ||
| Section Recap | ||
| 11. | Logistic Regression (01 hour 02 minutes) | Intro (what you will learn in this section) |
| Get the dataset | ||
| Binary outcome: Yes/No-Type Business Problems | ||
| Logistic regression intuition | ||
| Your first logistic regression | ||
| False Positives and False Negatives | ||
| Confusion Matrix | ||
| Interpreting coefficients of a logistic regression | ||
| 12. | Building a robust geodemographic segmentation model (01 hours 12 minutes) | Intro (what you will learn in this section) |
| Get the dataset | ||
| What is geo-demographic segmentation? | ||
| Let’s build the model – the first iteration | ||
| Let’s build the model – backward elimination: STEP-BY-STEP | ||
| Transforming independent variables | ||
| Creating derived variables | ||
| Checking for multicollinearity using VIF | ||
| Correlation Matrix and Multicollinearity Intuition | ||
| The model is Ready and the Section Recap | ||
| 13. | Assessing your model (01 hour 08 minutes) | Intro (what you will learn in this section) |
| Accuracy paradox | ||
| Cumulative Accuracy Profile (CAP) | ||
| How to build a CAP curve in Excel | ||
| Assessing your model using the CAP curve | ||
| Get my CAP curve template | ||
| How to use test data to prevent overfitting your model | ||
| Applying the model to test data | ||
| Comparing training performance and test performance | ||
| Section Recap | ||
| 14. | Drawing insights from your model (50 minutes) | Intro (what you will learn in this section) |
| Power insights from your CAP | ||
| Coefficients of a Logistic Regression – Plan of Attack (advanced topic) | ||
| Odds ratio (advanced topic) | ||
| Odds Ratio vs Coefficients in a Logistic Regression (advanced topic) | ||
| Deriving insights from your coefficients (advanced topic) | ||
| Section Recap | ||
| 15. | Model maintenance (30 minutes) | Intro (what you will learn in this section) |
| What does model deterioration look like? | ||
| Why do models deteriorate? | ||
| Three levels of maintenance for deployed models | ||
| Section Recap | ||
| 16. | ————————— Part 3: Data Preparation ————————— (02 minutes) | Welcome to Part 3 |
| 17. | Business Intelligence (BI) Tools (30 minutes) | Intro (what you will learn in this section) |
| Working with Data | ||
| What is a Data Warehouse? What is a Database? | ||
| Setting up Microsoft SQL Server 2014 for practice | ||
| Important: Practice Database | ||
| ETL for Data Science – what is Extract Transform Load (ETL)? | ||
| Microsoft BI Tools: What is SSDT-BI and what are SSIS/SSAS/SSRS? | ||
| Installing SSDT with MSVS Shell | ||
| 18. | ETL Phase 1: Data Wrangling before the Load (11 minutes) | Intro (what you will learn in this section) |
| Preparing your folder structure for your Data Science project | ||
| Download the dataset for this section | ||
| Two things you HAVE to do before the load | ||
| Notepad ++ | ||
| Editpad Lite | ||
| 19. | ETL Phase 2: Step-by-step guide to uploading data using SSIS (22 minutes) | Intro (what you will learn in this section) |
| Starting and navigating an SSIS Project | ||
| Creating a flat file source task and OLE DB destination | ||
| Setting up your flat file source connection | ||
| Setting up your database connection and creating a RAW table | ||
| Run the Upload & Disable | ||
| Due Diligence: Upload Quality Assurance | ||
| 20. | Handling errors during ETL (Phases 1 & 2) (02 hours 09 minutes) | Intro (what you will learn in this section) |
| Download the dataset for this section | ||
| How excel can mess up your data | ||
| Bulletproof Blueprint for Data Wrangling before the Load | ||
| SSIS Error: Text qualifier not specified | ||
| What do you do when your source file is corrupt? (Part 1) | ||
| What do you do when your source file is corrupt? (Part 2) | ||
| SSIS Error: Data truncation | ||
| A handy trick for finding anomalies in SQL | ||
| Automating Error Handling in SSIS: Conditional Split | ||
| Automating Error Handling in SSIS: Conditional Split (Level 2) | ||
| How to analyze the error files | ||
| Due Diligence: the one thing you HAVE to do every time | ||
| Types of Errors in SSIS | ||
| Summary | ||
| Homework | ||
| 21. | SQL Programming for Data Science (01 hour 11 minutes) | Intro (what you will learn in this section) |
| Download the dataset for this section | ||
| Getting To Know MS SQL Management Studio | ||
| Shortcut to upload the data | ||
| SELECT * Statement | ||
| Using the WHERE clause to filter data | ||
| How to use Wildcards / Regular Expressions in SQL (% and _) | ||
| Comments in SQL | ||
| Order By | ||
| Data Types in SQL | ||
| Implicit Data Conversion in SQL | ||
| Using Cast() vs Convert() | ||
| Working with NULLs | ||
| Understanding how LEFT, RIGHT, INNER, and OUTER join work | ||
| Joins with duplicate values | ||
| Joining multiple fields | ||
| Practicing Joins | ||
| 22. | ETL Phase 3: Data Wrangling after the load (01 hours 43 minutes) | Intro (what you will learn in this section) |
| RAW, WRK, DRV tables | ||
| Download the dataset for this section | ||
| Create your first Stored Proc in SQL | ||
| Executing Stored Procedures | ||
| Modifying Stored Procedures | ||
| Create table | ||
| Insert INTO | ||
| Check if table exists + drop table + Truncate | ||
| Intermediate Recap – Procs | ||
| Create the proc for the second file | ||
| Adding leading zeros | ||
| Converting data on the fly | ||
| How to create a proc template | ||
| Archiving Procs | ||
| What you can do with these tables going forward [drv files etc.] | ||
| 23. | Handling errors during ETL (Phase 3) (01 hours 09 minutes) | Intro (what you will learn in this section) |
| Download the dataset for this section | ||
| Upload the data to the RAW table | ||
| Create Stored Proc | ||
| How to deal with errors using the numeric function | ||
| How to deal with errors | ||
| How to deal with errors using the function | ||
| Additional Quality Assurance check: Balance | ||
| Additional Quality Assurance check: ZipCode | ||
| Additional Quality Assurance check: Birthday | ||
| Part Completed | ||
| ETL Error Handling “Vehicle Service” Project | ||
| 24. | ————————— Part 4: Communication ————————— (01 minutes) | Welcome to Part 4 |
| 25. | Working with people (29 minutes) | Intro (what you will learn in this section) |
| Cross-departmental Work | ||
| Come to me with a Business Problem | ||
| Setting expectations and pre-project communication | ||
| Go and sit with them | ||
| The art of saying “No” | ||
| Sometimes you have to go to the top | ||
| Building a data culture | ||
| 26. | Presenting for Data Scientists (01 hour 02 minutes) | Intro (what you will learn in this section) |
| Case study | ||
| Analyzing the intro | ||
| Intro dissection – recap | ||
| REAL Data Science Presentation Walkthrough – Make Your Audience Say “WOW” | ||
| My brainstorming method | ||
| How to present to executives | ||
| The truth is not always pretty | ||
| Passion and the Wow-factor | ||
| Extra: my full presentation | LIVE 2015 | ||
| Extra: links to other examples of good storytelling | ||
| 27. | Homework Solutions (01 hour 16 minutes) | Advanced-Data Mining with Tableau: Visualising Credit Score & Tenure |
| Advanced-Data Mining with Tableau: Chi-Squared Test for Country | ||
| ETL Error Handling (Phases 1 and 2) | ||
| ETL Error Handling “Vehicle Service” Project (Part 1 of 3) | ||
| ETL Error Handling “Vehicle Service” Project (Part 2 of 3) | ||
| ETL Error Handling “Vehicle Service” Project (Part 3 of 3) | ||
| THANK YOU bonus video | ||
| 28. | Special Offer (02 minutes) | ***YOUR SPECIAL BONUS*** |
Resources Required
- Passion for Sucess
- Software utilized in this course is either free or has a demo version accessible
Featured Review
Deni Avelar (5/5) : Great content! The course is designed for all levels of experts. Highly recommend it!
Pros
- Benedict DSouza (5/5): This course is excellent with an excellent explanation of every topic or unit by the instructor.
- Rahul Phatak (5/5): If you want to learn data science then Kirill Eremenko is the best teacher ever.
- Wendy Kurniawan (5/5): Keep going in providing excellent teaching courses and materials in the analytics field.
- Christos Asvestopoulos (5/5): The “Data Science A-Z™: Real-Life Data Science Exercises Included” is one of the best online courses I have ever taken.
Cons
- Mike Maaß (2/5): While it does not really come into play in the first parts it is really annoying in the Data Engineering section.
- Alexander Riha (2/5): The sections on Data Prep (which mentions makes up 70% of what Data Science is) require the Microsoft SQL Server – this is not available for Mac users.
- Eng Teck Tan (2/5): That makes it almost impossible to follow through the course seamlessly without spending hours on the side to resolve technical problems.
About the Author
The instructor of this course is Kirill Eremenko who is a Data Scientist. He has a 4.5 Instructor Rating and 599,095 Reviews on Udemy. He offers 59 Courses and has taught 2,251,523 students so far.
- Professionally, Kirill Eremenko is a data science consultant with experience in the retail, transportation, retail, and financial sectors
- At Deloitte Australia, Kirill Eremenko received training from the top analytics mentors, and since he started teaching on Udemy, he has shared his experience with thousands of aspiring data scientists
- You will quickly see from his courses how he gives skilled step-by-step tutoring in the field of data science by fusing his real-world expertise and academic background in physics and mathematics
- His emphasis on intuitive explanations is one of his teaching strengths, so you can be confident that you will fully comprehend even the most challenging subjects.
Comparison Table
| Parameters | Data Science A-Z™: Real-Life Data Science Exercises Included | Tableau 2023 A-Z: Hands-On Tableau Training for Data Science | Python A-Z™: Python For Data Science With Real Exercises! |
|---|---|---|---|
| Offers | INR 449 ( | INR 449 ( | INR 449 ( |
| Duration | 21 hours | 8.5 hours | 11 hours |
| Rating | 4.5 /5 | 4.6 /5 | 4.6 /5 |
| Student Enrollments | 208,256 | 325,763 | 149,179 |
| Instructors | Kirill Eremenko | Kirill Eremenko | Kirill Eremenko |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
Data Science A-Z™: Real-Life Data Science Exercises Included: FAQs
Ques. What topics does data science include?
Ans. Statistics, coding, business intelligence, data structures, mathematics, machine learning, and algorithms are some of the key topics covered in the Data Science curriculum.
Ques. How do you do data science in R?
Ans. R is a programming language that offers users the tools they need to explore, model, and visualize data. For data analysis, R is employed. To manage, store, and analyze data in data science, R is utilized. It can be applied to statistical modeling and data analysis.
Ques. How long does it take to learn R for data science?
Ans. It takes seven days to master R programming, with at least three hours per day of study, if you have prior knowledge of any programming language. It takes three weeks to learn R programming from scratch. Learn ideas like how to construct, append, subset datasets, lists, and join throughout the second week.
Ques. What is R code in data science?
Ans. R is a programming language that offers users the tools they need to explore, model, and visualize data. For data analysis, R is employed to manage, store, and analyze data in data science. It can be applied to statistical modeling and data analysis.
Ques. How long does it take to teach yourself data science?
Ans. Depending on how you progress, it is advised that you wait at least six months before classifying yourself as a beginner data scientist. This will present you with the chance to acquire the necessary skills and put them into practice through the creation of personal projects.
Ques. Is R enough for data science?
Ans. R might be a good fit for you if you’re passionate about the statistical computation and data visualization aspects of data analysis. Python might be a better choice if, on the other hand, you’re interested in working as a data scientist and utilizing big data, artificial intelligence, and deep learning methods.
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