Is Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2024] beginner friendly?

No, this is an advanced level course. It is recommended for learners who already have foundational knowledge and practical experience in the domain.

How long does it take to complete Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2024]?

The total duration of the course is approximately 21 total hours. Since it is generally self-paced, completion time depends entirely on your learning schedule and availability.

Does this course include hands-on projects?

Yes, the curriculum is highly practical and includes hands-on projects. Completing these will help you build a portfolio of work to demonstrate your new skills.

What careers is this course relevant for?

The skills taught in this course are highly relevant for professionals pursuing roles within Data Science and adjacent technical fields looking to upskill.

Data Science|Data Science Learning Path

Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2024]

Name: Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2024]
Price: 4099 INR
Availability: InStock
Rating: 4.6 (34070 reviews)

Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!

4.6|Reviews (34K)|verifiedIncluded in Subscription

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Course Insight

Designed for advanced/expert practitioners. Designed for experienced practitioners. We recommend having a solid grasp of Data Science fundamentals before starting this specialization.

Advanced LevelSelf-Paced LearningProject-Based

SKILLS TO
MASTER

Analytics

Exploratory Data Analysis

Modeling

Predictive Machine Learning

SQL Querying

Relational Data Management

Pandas

Matplotlib

Statistics

Tableau

ETL

Careers:Data Scientist, Data Analyst, Machine Learning Engineer.

💡This course fits perfectly into our comprehensiveData Science Learning Path. Explore the ecosystem to see how it compares to other foundational skills.

Quick Facts

21 total hours

Independent Instructor

Below sections are verified from last major sync. For real-time updates and today's latest lectures, Check official page here.

What You’ll Learn

Extremely Hands-On... Incredibly Practical... Unbelievably Real!

This is not one of those fluffy classes where everything works out just the way it should and your training is smooth sailing. This course throws you into the deep end.

In this course you WILL experience firsthand all of the PAIN a Data Scientist goes through on a daily basis. Corrupt data, anomalies, irregularities - you name it!

This course will give you a full overview of the Data Science journey. Upon completing this course you will know:

This course has pre-planned pathways. Using these pathways you can navigate the course and combine sections into YOUR OWN journey that will get you the skills that YOU need.

Or you can do the whole course and set yourself up for an incredible career in Data Science.

The choice is yours. Join the class and start learning today!

See you inside,

Sincerely,

Kirill Eremenko

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Outcomes

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.

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FAQs

Reviews

⭐4.7 / 5 average rating from 34K+ learners

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