Math and Statistics For AI, Data Science
Learn the key concepts of Math and Statistics that lay the foundations for a strong data science career. This course is carefully curated to simulate real-time organizational experience to prepare you for the current job market and at the same time provides you with an ultimate learning experience through storytelling and intuitive explanations.
No Experience
Needed
Start from scratch
and build up
Flexible
Schedule
Learn at your
own pace
Get
Job-Ready
Acquire essential
job skills
/308538/Updated%20Courses%20Thumbnails/Math-_-Stats_Thumbnail_1000.webp)
This course includes:
- 12h:49m:38s on-demand video
- 98 Lectures
- 17 Exercises
- 19 Quizzes
- Access on any Device
- Certificate of completion
No Experience Needed
Start from scratch
and build up
Flexible Schedule
Learn at your
own pace
Get Job-Ready
Acquire essential
job skills
Course Curriculum
98 Lectures | 12h:49m:38s
1:
Welcome to Math and Statistics Experience
7 Lectures
-
1.1: Importance of Math and Stats in Data Science Career
Free -
1.2: How Is This Course Different from Other Courses on the Internet?
Free -
1.3: What Support Do You Provide If I Have Questions?
Free -
1.4: Who Should Take This Course?
Free -
1.5: Pre-requisites
Free -
1.6: System Requirements
Free -
1.7: Course Overview
Free
5:
Pandas, Matplotlib and Seaborn Basics
8 Lectures
-
5.1: Pandas Introduction and Installation
Free -
5.2: Dataframe Basics
Free -
5.3: Read, Write Excel and CSV Files
Free -
5.4: Handle Missing Data - Part 1
Free -
5.5: Handle Missing Data - Part 2
Free -
5.6: Grouping Data
Free -
5.7: Data Concatenation and Merging
Free -
5.8: Data Visualization with Matplotlib and Seaborn
Free
6:
Measures Of Central Tendency and Dispersion
18 Lectures
-
6.1: Descriptive vs. Inferential Statistics
Free -
6.2: Measures of Central Tendency: Mean, Median, Mode
Free -
6.3: Percentile
-
6.4: Analysis: Shoe Sales (Using Mean, Median, Percentile)
-
6.5: Quiz
-
6.6: Exercise
-
6.7: Measures of Dispersion: Range, IQR
-
6.8: Box or Whisker Plot
-
6.9: Outlier Treatment Using IQR and Box Plot
-
6.10: Quiz
-
6.11: Exercise
-
6.12: Measures of Dispersion: Variance and Standard Deviation
-
6.13: Analysis: Stock Returns Volatility (Using Variance and Std Dev)
-
6.14: Correlation
-
6.15: Correlation vs Causation
-
6.16: Quiz
-
6.17: Exercise
-
6.18: Chapter Summary
10:
Phase 1: Find Target Market
21 Lectures
-
10.1: Data Validation Of Acquired Data
-
10.2: Data Understanding, MySQL Setup
-
10.3: Data Import in Jupyter Notebook
-
10.4: Data Cleaning: Handle NULL Values (Annual Income)
-
10.5: Data Cleaning: Treat Outliers (Annual Income)
-
10.6: Data Visualization: Annual Income
-
10.7: Exercise: Treat Outliers in Age Column
-
10.8: Exercise Solution: Treat Outliers in Age Column
-
10.9: Data Visualization: Age, Gender, Location
-
10.10: Peter’s Nightmare
-
10.11: Data Cleaning: Credit Score Table - Part 1
-
10.12: Data Cleaning: Credit Score Table - Part 2
-
10.13: Correlation among Credit Profile Variables
-
10.14: Exercise: Handle NULL Values in Transactions Table
-
10.15: Exercise Solution: Handle NULL Values in Transactions Table
-
10.16: Peter’s Confusion: IQR or Std Dev?
-
10.17: Data Cleaning: Treat Outliers using IQR (Transaction Amount)
-
10.18: Data Visualization: Transactions Table
-
10.19: Finalize the Target Group
-
10.20: Phase 1 Feedback Meeting With Stakeholders
-
10.21: Get Ready For Phase 2
11:
Central Limit Theorem
12 Lectures
-
11.1: Random Sampling & Sample Bias
-
11.2: The Law of Large Numbers
-
11.3: Central Limit Theorem, Sampling Distribution
-
11.4: Case Study: Solar Panels
-
11.5: Standard Error
-
11.6: Quiz
-
11.7: Z Score Table (Z-Table)
-
11.8: Quiz
-
11.9: Confidence Interval
-
11.10: Confidence Interval: Estimate Car Miles
-
11.11: Exercise
-
11.12: Chapter Summary
12:
Hypothesis Testing
19 Lectures
-
12.1: Null vs Alternate Hypothesis
-
12.2: Z Test, Rejection Region
-
12.3: Housing Inflation Test: Rejection Region
-
12.4: Quiz
-
12.5: Exercise
-
12.6: p-Value
-
12.7: Housing Inflation Test: p-Value
-
12.8: Quiz
-
12.9: Exercise
-
12.10: One-Tailed vs Two-Tailed Test
-
12.11: Type 1 and Type 2 Errors
-
12.12: Quiz
-
12.13: Statistical Power & Effect Size
-
12.14: A/B Testing
-
12.15: A/B Testing Using Z Test
-
12.16: A/B Testing: Drug Trial
-
12.17: Quiz
-
12.18: Exercise
-
12.19: Chapter Summary
What our learners experience
Our content is rated 5.0/5 from 2054+ Learners
Thank you Dhaval Sir and the entire Codebasics team for coming up with such courses which are super rare to find. Totally worth taking it. All the concepts were taught very easily and very intuitively so that we can grasp and digest the fundamentals within no time. The best part I like about the courses on codebasics.io is that they try to replicate real industry experiences in the form of Peter Panday and Tony Sharma. Well done. Keep up the good work. Will take every course from codebasics.io which is relevant for me. Cheers !!
Landed a Job
Just finished this awesome course! It was clear, engaging, and packed with useful info. The lessons were short and easy to understand, making learning fun and efficient. Highly recommend to anyone looking to improve their skills!
Landed a Job
Hey, it’s Javidan Akbarov from the Computer Science faculty again 😎. Honestly, this Math and Statistics course was hands down the best intro to the topic 📊. The fact that it’s applied makes it even better 💡. After finishing it, you can seriously move forward with statistics confidently ✅. Huge thanks to Dhaval for this amazing course 🙏. My advice to anyone taking it: take notes after each video 📝—Notion.so or any other app works—because it really helps. Another milestone checked off in the Gen AI and Data Science bootcamp 🚀.
/fit-in/100x100/filters:format(webp)/fit-in/100x100/596041/avatar/68acdd5a65b50.png)
Javidan Akbarov
Computer Science Student spec in AI/ML/DS
Landed a Job
To be honest, I tried many courses. But this one couse is superb. And I highly recommend this course to everyone who want to learn Statistics and Math practically.
Thanks Codebasics!
/fit-in/100x100/filters:format(webp)/fit-in/100x100/filters:format(webp)/fit-in/55x55/563632/avatar/697ba38be9ec1.png)
Babar Ali
Data Scientist & AI Engineer
I recently completed the Statistics course by Dhaval Patel sir , and it has helped me a lot! Even though I was already somewhat familiar with statistics, this course made my concepts super clear ✅. The explanations are practical, easy to understand, and really boosted my confidence.
This course is just a part of the entire Data Science course, and after completing the entire course, I am really excited and motivated 💪. I would love to join and contribute to in the future! ❤️
Abhishek Dwivedi
Student
System Requirements
- Any computer with 4GB or more RAM
- OS: Windows, Mac, or Linux
Course Instructor/Creator
Get Certified
When You Complete This Course
You receive a ‘Certificate of Completion’ signed and addressed personally by me, your guide and mentor. – Dhaval Patel
Add and share this certificate with your Resume/CV or on your LinkedIn profile.
Frequently Asked Questions
Q.1
How does this online 'Math & Statistics' course streamline the learning process for essential data science skills?
This course simplifies learning Math and Statistics through project-based learning with a real dataset, focusing on practical data science applications. With engaging storytelling, simple explanations, and interactive exercises, including quizzes, ensures a comprehensive and accessible learning experience. A certificate upon completion further validates your learning journey.
Q.2
What is different in this course compared to hundreds of courses on the internet and free tutorials on YouTube?
Most of the courses available on the internet teach you how to build x & y without any business context and do not prepare you for real-world problem-solving. However, our 'Math & Statistics' course offers a unique experience in which you will learn by solving real-life use cases in an imaginary company called AtliQo Bank. The tutorials are easy to understand, featuring elements of fun to keep the learning process engaging.
Q.3
What dataset is used in this course? Is it some toy dataset or something that mimics a real-world problem?
In the early stages of the course, we introduce fundamental concepts using a variety of small datasets. As the course progresses, we engage with the 'AtliQo Credit Card' data which centers around a dataset from the fictional AtliQo Bank. This project provides a hands-on experience in data validation, cleaning, and visualization, reflecting a realistic scenario where the bank aims to enhance its credit card market penetration. This dataset contains over half a million records.
Q.4
What is the duration of this 'Math & Statistics' course?
The 'Math & Statistics' course consists of 11 hours and 34 minutes of on-demand video content.
Q.5
Is this course part of the Data Analytics Bootcamp?
No, this course is not part of our data analytics curriculum. Nevertheless, mastering the concepts of mathematics and statistics will significantly enhance your analytical ability.
Q.1
What are the things I need to know before starting this course? Also, will it help me learn 'Math & Statistics' from scratch for data science?
This 'Math & Statistics' course is designed for absolute beginners, so you don't need any specific skills other than basic familiarity with computers. It's the perfect starting point for anyone looking to embark on a data science journey.
Q.2
How can I get help if I have any doubt and need support?
We have an active Discord server where you can post your questions. You can expect to receive a response in a reasonable timeframe.
Q.1
Can I add this course to my resume
Yes. Absolutely you can mention AtliQo Bank project experience in your resume with the relevant skills that you will learn from this course.
Q.2
I’m not sure if this 'Math & Statistics' course is good enough for me to invest some money. What can I do?
Don't worry. Many videos in this 'Math & Statistics' course are free, allowing you to gauge the quality of teaching before making an investment. Dhaval Patel, the course instructor, runs a popular data science YouTube channel called Codebasics. There, you can watch his videos and read comments to get an idea of his teaching style.
Q.3
Will I receive a price deduction on the Data Analytics Bootcamp if I enroll in this course?
No. This course is not part of the Data Analytics Bootcamp.
More Courses
/308538/optimized_images/python_course_thumbnail.webp)
Python: Beginner to Advanced For Data Professionals
This project-based learning course will teach Python through hands-on experience with two real-world projects: (1) Data analysis in the hospitality domain and (2) Building an expense tracking system. It is designed for both complete beginners with no prior coding experience and those with some Python knowledge looking to enhance their skills. The course is taught by an industry expert with over 14 years of experience at leading tech companies such as Nvidia and Bloomberg.
/308538/Updated%20Courses%20Thumbnails/SQL-Thumbnail_1000.webp)
SQL Beginner to Advanced For Data Professionals
Beginners to Advanced SQL course for those preparing for a data career (Data Analyst, Data Scientist, or Data Engineer). This course is carefully curated to simulate real-time organizational experience to prepare you for the current job market and at the same time provides you with an ultimate learning experience through a storytelling mode that you would see in movies.
/81349/Course_thumbnails/Thumbnail_AI_for_Everyone.webp)
AI for Everyone
This AI Course will teach you the fundamentals and know-hows of AI like it is taught to an 8-year-old and help you to align your career with AI. This will be helpful for anyone who wants to add “AI” in their resume with confidence and without learning to code necessarily.
This course includes:
- 12h:49m:38s on-demand videos
- 98 Lectures
- 17 Exercises
- 19 Quizzes
- Access on any Device
- Certificate of completion
Learning knows no limits. Here’s to your journey of seamless learning. Pick your preferred course from the list of paid & free resources.
Quick Links
Log in with Google
Log in with Linkedin