Data Science Foundation Bootcamp With Virtual Internship
Industry Projects-Based Bootcamp Created by Hiring Managers
Build a strong foundation to become a high-paying Data Scientist by learning industry secrets from an AI entrepreneur with extensive experience and a YouTube channel (Codebasics) with 1M+ subscribers. This foundational bootcamp consists of use cases, end-end projects and data sets with high relevance to current industry standards.
20+
Real Scenarios
8
Business Projects
4 Weeks
Virtual Internship
Free
Portfolio Website
Lifetime
Validity
803+
Enrolled Learners
Join our Bootcamp
Learn In-demand Skills
Build Foundation
20+
Real Scenarios
8
Business Projects
4 Weeks
Virtual Internship
Free
Portfolio Website
Lifetime
Validity
803+
Enrolled Learners
Why this is the most effective Data Science Bootcamp?
-
Automated portfolio website to showcase your work
-
Unlimited daily doubt clearance support via private Discord community
-
Real-time industry-based projects in F & B, Banking, Healthcare, Finance, Hospitality domain
-
Complex datasets that have more than 1 Million records like you will get in a company
-
Highly engaging content with the cinematic experience, real business practice problems, business meetings, etc.
-
Easy explanation of complex topics by Dhaval Patel, a famous Youtuber (1M+ subs) teacher & active data industry expert
-
Real-time tasks prepared by Hemanand Vadivel, an industry leader with 8+ years of experience in International Markets
Hear It From
Our Happy Learners
Our content is rated 4.9/5 from 20712+ Learners
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Machine Learning
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SQL
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Python
One of the finest course, I have come across!!
The content is sequentially crafted and covers from the fundamentals of Machine learning to advanced level coding. This has cleared both the theoretical and code level doubts and has helped developing math intuition behind any challenges. Codebasics provide the best courses that are both cost effective and contents are well engaging with the simplest explanation.
I have an overall experience of 3.8 years in Data Science,engineering and visualisation using the tools like Dataiku, MarkLogic and Qlik sense. But this course has taught me Python alongwith Data science with a better approach. My love for mathematics has helped me to deep dive into nitty gritty aspects in each algorithm. After a career gap of 7 months, I hope to land a better job where I can explore, develop my skills and contribute to greater challenges in the field of AI/ML. Research in this domain is my preference but I am open to learn and contribute.
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This course provides a comprehensive introduction to the Python programming language. The course is well structured, starting with the basics and gradually building up to more advanced concepts. The lessons are taught through clear and concise video tutorials, accompanied by interactive coding exercises that reinforce the concepts covered. The course covers topics such as data types, functions, object-oriented programming, and more. The instructor is knowledgeable and passionate about Python, and the course is well-paced, making it easy to follow along and absorb the material. Overall, the Code Basics Python course is an excellent resource for anyone looking to learn Python, from beginners to those with some programming experience.
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Landed a Job
Being from a non-tech background and I know only economics and statistics, I feel happy because these industry experts made our life so easy. From the bottom of my heart, I congratulate Mr Dhaval Patel for introducing this fantastic course.
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Landed a Job
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 !!
Math & Stats
Landed a Job
I just finished this AI course and it was amazing! I was really nervous at first because AI seemed so hard, but Dhaval sir made everything easy to understand. He breaks down difficult topics into simple, clear lessons, using practical examples that make learning fun and straightforward. His passion for teaching and deep knowledge of AI shine through in every class. Thanks to his incredible support and teaching style, I completed the course, which I never thought I could do. I highly recommend this course to anyone interested in AI. Dhaval sir, you made this journey unforgettable and truly exciting. Thank you!
AI for Everyone
Overview
What you'll learn in
this Data Science Bootcamp
Welcome to The Gen AI and Data Science Bootcamp Experience
01h:13m:35s on-demand video
|
20 Lectures
Python: Beginner to Advanced For Data Professionals
16h:57m:31s on-demand video
|
108 Lectures
Project 2: Expense Tracking System
11 Lectures
-
Problem Statement & Tech Architecture
-
Database CRUD Operations
-
Automated Tests Setup for CRUD
-
Expense Management: Backend (FastAPI)
-
Expense Management: Logging
-
Streamlit Introduction
-
Expense Management: Frontend (Streamlit)
-
Analytics: Backend (FastAPI)
-
Analytics: Frontend (Streamlit)
-
README and Requirements.txt
-
Exercise
Online Credibility
00h:24m:12s on-demand video
|
4 Lectures
SQL for Data Science
02h:02m:08s on-demand video
|
86 Lectures
SQL Basics: Data Retrieval - Single Table
14 Lectures
-
Install MySQL: Windows
Free -
Install MySQL: Linux, Mac
Free -
Import Movies Dataset in MySQL
Free -
Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)
Free -
Exercise - Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)
Free -
Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)
Free -
Exercise - Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)
Free -
Summary Analytics (MIN, MAX, AVG, GROUP BY)
Free -
Exercise - Summary Analytics (MIN, MAX, AVG, GROUP BY)
Free -
HAVING Clause
Free -
Calculated Columns (IF, CASE, YEAR, CURYEAR)
Free -
Exercise - Calculated Columns (IF, CASE, YEAR, CURYEAR)
Free -
The Data God’s Blessing
Free -
Quiz
Math and Statistics For AI, Data Science
12h:49m:38s on-demand video
|
98 Lectures
Measures Of Central Tendency and Dispersion
18 Lectures
-
Descriptive vs. Inferential Statistics
Free -
Measures of Central Tendency: Mean, Median, Mode
Free -
Percentile
-
Analysis: Shoe Sales (Using Mean, Median, Percentile)
-
Quiz
-
Exercise
-
Measures of Dispersion: Range, IQR
-
Box or Whisker Plot
-
Outlier Treatment Using IQR and Box Plot
-
Quiz
-
Exercise
-
Measures of Dispersion: Variance and Standard Deviation
-
Analysis: Stock Returns Volatility (Using Variance and Std Dev)
-
Correlation
-
Correlation vs Causation
-
Quiz
-
Exercise
-
Chapter Summary
Phase 1: Find Target Market
21 Lectures
-
Data Validation Of Acquired Data
-
Data Understanding, MySQL Setup
-
Data Import in Jupyter Notebook
-
Data Cleaning: Handle NULL Values (Annual Income)
-
Data Cleaning: Treat Outliers (Annual Income)
-
Data Visualization: Annual Income
-
Exercise: Treat Outliers in Age Column
-
Exercise Solution: Treat Outliers in Age Column
-
Data Visualization: Age, Gender, Location
-
Peter’s Nightmare
-
Data Cleaning: Credit Score Table - Part 1
-
Data Cleaning: Credit Score Table - Part 2
-
Correlation among Credit Profile Variables
-
Exercise: Handle NULL Values in Transactions Table
-
Exercise Solution: Handle NULL Values in Transactions Table
-
Peter’s Confusion: IQR or Std Dev?
-
Data Cleaning: Treat Outliers using IQR (Transaction Amount)
-
Data Visualization: Transactions Table
-
Finalize the Target Group
-
Phase 1 Feedback Meeting With Stakeholders
-
Get Ready For Phase 2
Hypothesis Testing
19 Lectures
-
Null vs Alternate Hypothesis
-
Z Test, Rejection Region
-
Housing Inflation Test: Rejection Region
-
Quiz
-
Exercise
-
p-Value
-
Housing Inflation Test: p-Value
-
Quiz
-
Exercise
-
One-Tailed vs Two-Tailed Test
-
Type 1 and Type 2 Errors
-
Quiz
-
Statistical Power & Effect Size
-
A/B Testing
-
A/B Testing Using Z Test
-
A/B Testing: Drug Trial
-
Quiz
-
Exercise
-
Chapter Summary
Online Credibility & Domain Knowledge Course
00h:31m:00s on-demand video
|
5 Lectures
Master Machine Learning for Data Science & AI: Beginner to Advanced
22h:41m:19s on-demand video
|
201 Lectures
Supervised Machine Learning: Regression
29 Lectures
-
Simple Linear Regression
Free -
Multiple Linear Regression
Free -
Quiz
-
Exercise
-
Cost Function
-
Derivatives and Partial Derivatives
-
Chain Rule
-
Quiz
-
Exercise
-
Gradient Descent Theory
-
Gradient Descent: Python Implementation
-
Why MSE (and not MAE)?
-
Model Evaluation: Train, Test Split
-
Model Evaluation: Metrics
-
Peter Pandey Flexes his ML skills on LinkedIn
-
Quiz
-
Exercise
-
Data Preprocessing: One Hot Encoding
-
Quiz
-
Polynomial Regression
-
Quiz
-
Exercise
-
Overfitting and Underfitting
-
Reasons and Remedies For Overfitting / Underfitting
-
L1 and L2 Regularization
-
Bias Variance Trade Off
-
Quiz
-
Exercise
-
Chapter Summary
Supervised Machine Learning: Classification
31 Lectures
-
Introduction to Classification
Free -
Logistic Regression: Binary Classification
Free -
Model Evaluation: Accuracy, Precision and Recall
-
Quiz
-
Exercise
-
Model Evaluation: F1 Score, Confusion Matrix
-
Logistic Regression: Multiclass Classification
-
Cost Function: Log Loss
-
Quiz
-
Exercise
-
Support Vector Machine (SVM)
-
Data Pre-processing: Scaling
-
Sklearn Pipeline
-
Quiz (disabled)
-
Quiz
-
Exercise
-
Naive Bayes: Theory
-
Naive Bayes: SMS Spam Classification
-
Quiz
-
Exercise
-
Decision Tree: Theory
-
Decision Tree: Salary Classification
-
I Need a Favour
-
Quiz
-
Exercise
-
Handle Class Imbalance: Theory
-
Handle Class Imbalance Using imblearn: Churn Prediction
-
Quiz
-
Exercise
-
Get inspired by Peter Pandey
-
Chapter Summary
Ensemble Learning
21 Lectures
-
What is Ensemble Learning?
Free -
Majority Voting, Average and Weighted Average
-
Bagging
-
Bagging: Random Forest
-
Random Forest: Raisin Classification
-
Quiz
-
Exercise
-
Boosting: AdaBoost
-
Gradient Boosting: Regression Walk Through
-
Gradient Boosting: Regression Math
-
Gradient Boosting: Revenue Prediction
-
Quiz
-
Exercise
-
Gradient Boosting: Classification
-
XGBoost: Walk Through
-
XGBoost: California Housing Prediction
-
XGBoost: Synthetic Data Classification
-
XGBoost: Benefits
-
Quiz
-
Exercise
-
Chapter Summary
Model Evaluation & Fine Tuning
16 Lectures
-
Introduction
-
Model Evaluation: ROC Curve & AUC
-
Cost Benefit Analysis Using ROC in Sklearn
-
Quiz
-
Exercise
-
K Fold Cross Validation
-
Stratified K Fold Cross Validation
-
Hyperparameter Tuning: GridsearchCV
-
Hyperparameter Tuning: RandomizedSearchCV
-
Quiz
-
Exercise
-
Model Selection Guide
-
Luck favors the LinkedIn post
-
Selecting the Right Evaluation Metric
-
Quiz
-
Chapter Summary
Project 1: Healthcare Premium Prediction (Regression)
16 Lectures
-
The Rise of AtliQ AI
Free -
Project Charter Meeting
Free -
Scope of Work, Task Planning in JIRA
-
Data Collection
-
Data Cleaning & EDA - Part 1
-
Data Cleaning & EDA - Part 2
-
Feature Engineering
-
Model Training, Fine Tunning
-
98% Model Accuracy, Really?
-
Error Analysis
-
Model Segmentation
-
Request More Data
-
Model Retraining
-
Build App Using Streamlit
-
Deployment
-
Exercise
Project 2: Credit Risk Modelling (Classification)
19 Lectures
-
Peter's Promotion: New Project
Free -
Domain Understanding: NBFC & Credit Approvals
Free -
Scope of Work & Tech Architecture
Free -
Data Collection
-
Quick Intro to Data Leakage
-
Data Cleaning
-
Exploratory Data Analysis (EDA)
-
Feature Engineering – Part 1
-
Weight of Evidence (WOE), Information Value (IV)
-
Feature Engineering – Part 2
-
Model Training & Evaluation
-
Introduction to Optuna
-
Model Fine Tuning Using Optuna
-
Intro To Rank Ordering & KS Statistic
-
Model Evaluation Using KS Statistic & Gini Coefficient
-
Streamlit App
-
Business Presentation
-
Deployment
-
Exercise
ML Ops & Cloud Tools
22 Lectures
-
What is ML Ops?
Free -
Importance of ML Ops in Your Career
Free -
ML Flow: Purpose and Overview
Free -
ML Flow: Experiment Tracking
-
ML Flow: Model Registry
-
ML Flow: Centralized Server Using Dagshub
-
Quiz
-
What is API?
-
FastAPI Basics
-
Build FastAPI Server For Credit Risk Project
-
Quiz
-
Git Version Control System
-
Introduction to ML Cloud Platforms
-
AWS Sagemaker: Account Setup
-
AWS Sagemaker: Sagemaker Studio
-
AWS Sagemaker: 4 Ways to Train Model
-
AWS Sagemaker: Built In Algorithms
-
AWS Sagemaker: Script Mode
-
Quiz
-
Data Drift Detection Using PSI & CSI
-
PSI & CSI: Practical Implementation
-
Quiz
Build your Portfolio Website
24min on-demand video
|
7 Lectures
Virtual Internship
00h:09m:49s on-demand video
|
10 Lectures
Week1
29 Lectures
-
Welcome Note
-
Your Onboarding Letter
-
Welcome Note from Your Manager
-
Let's Dive Right Into Week 1!
-
Getting Help From Your Mentors / Seniors
-
Your First Task
-
Incoming Task Email 1
-
Have You Completed This Task?
-
Quality Check 1
-
Quality Check 2
-
Quality Check 3
-
Quality Check 4
-
Quality Check 5
-
Quality Check 6
-
Quality Check 7
-
Quality Check 8
-
Quality Check 9
-
Quality Check 10
-
Congratulations, You Have Completed the First Task of Your Internship.
-
Your Second Task
-
Incoming Task Email 2
-
Have You Completed the Assigned Task?
-
Presentation Submission (Visualization Task)
-
Congratulations! You Have Completed 2 Tasks in a Row!
-
You Need Scrum Training
-
Incoming Task Email 3
-
Have You Completed the Assigned Task?
-
Scrum Knowledge Check
-
Congratulations, You Have Completed Week 1 Successfully.
Week 2
26 Lectures
-
Let's Dive Right into Week 2!
-
Can You Handle this Project?
-
Incoming Task Email 1
-
Have You Completed the Assigned Task?
-
Quality Check 1
-
Quality Check 2
-
Quality Check 3
-
Quality Check 4
-
Quality Check 5
-
Quality Check 6
-
Quality Check 7
-
Quality Check 8
-
Quality Check 9
-
Quality Check 10
-
Congratulations on Finishing This Task!
-
SQL Query Debugging
-
Incoming Task Email 2
-
Quality Check 1
-
Quality Check 2
-
Quality Check 3
-
Quality Check 4
-
Quality Check 5
-
Quality Check 6
-
Quality Check 7
-
Quality Check 8
-
Congratulations on Successfully Completing the Task!
Week 3 & 4
26 Lectures
-
Let's Dive Right into the Final 2 Weeks!
-
Can You Step Up for a Client Task?
-
Incoming Task Email: Data Cleaning
-
Please Don't Share Datasets
-
Have You Completed the Data Cleaning?
-
Quality Check 1
-
Quality Check 2
-
Quality Check 3
-
Quality Check 4
-
Incoming Task Email: Feature Engineering
-
Have You Completed the Feature Engineering Task?
-
Quality Check 1
-
Quality Check 2
-
Quality Check 3
-
Quality Check 4
-
Let's Start Predictive Modeling
-
Incoming Task Email: Predictive Modeling
-
Have You Completed the Modeling Task?
-
Quality Check
-
Incoming Task Email: MLflow Deployment
-
Submission of DagsHub link | ML flow
-
Incoming Task Email: Streamlit App Development
-
Have You Completed the Streamlit App Development?
-
Can You Present to Client?
-
Presentation Submission (CodeX Project)
-
End Note
Get Your Certificate
00:00 on-demand video
|
0 Lectures
AI for Everyone
16h:37m:39s on-demand video
|
67 Lectures
Supplementary Learning (Industry Projects)
6 Lectures
-
Intro: How to use this supplementary learning
-
Statistical ML Project: Build a House Prediction System
-
Deep Learning Project: Build a Potato Disease Classification System
-
NLP Project: Build a chatbot using Dialog Flow
-
Gen AI Project: Build a Q & A system in retail domain
-
Gen AI Project: Build a news research tool in finance domain
SQL Beginner to Advanced For Data Professionals
11h:15m:16s on-demand video
|
84 Lectures
SQL Basics: Database Creation & Updates
17 Lectures
-
Database Normalization and Data Integrity
-
Entity Relationship Diagram (ERD)
-
Mentor Talk: Art of Googling
-
Data Types: Numeric (INT, DECIMAL, FLOAT, DOUBLE)
-
Data Types: String (VARCHAR, CHAR, ENUM)
-
Data Types: Date, Time (DATETIME, DATE, TIME, YEAR, TIMESTAMP)
-
Data Types: JSON, Spatial (JSON, GEOMETRY)
-
Luck Favors the LinkedIn Post
-
Primary key
-
Foreign Key
-
Create a Database From an Entity Relationship Diagram - ERD
-
Import Data From a CSV File Into a Database
-
Insert Statement
-
Update and Delete
-
I Need a Favour
-
Expect the Unexpected: The Intermission Scene
-
Quiz
SQL Advanced: Finance Analytics
10 Lectures
-
Backlog Grooming Meeting: Gross Sales Report
-
User-Defined SQL Functions
-
Exercise: User-Defined SQL Functions
-
Gross Sales Report: Monthly Product Transactions
-
Gross Sales Report: Total Sales Amount
-
Exercise: Yearly Sales Report
-
Stored Procedures: Monthly Gross Sales Report
-
Stored Procedure: Market Badge
-
Benefits of Stored Procedures
-
Quiz
SQL Advanced: Top Customers, Products, Markets
16 Lectures
-
Problem Statement and Pre-Invoice Discount Report
-
Performance Improvement # 1
-
Performance Improvement # 2
-
Database Views: Introduction
-
Database Views: Post Invoice Discount, Net Sales
-
Exercise: Database Views
-
Top Markets and Customers
-
Exercise: Top Products
-
The Two Most Important Skills for the Data Analyst
-
Window Functions: OVER Clause
-
Window Functions: Using it in a Task
-
Exercise: Window Functions: OVER Clause
-
Window Functions: ROW_NUMBER, RANK, DENSE_RANK
-
Exercise: Window Functions: ROW_NUMBER, RANK, DENSE_RANK
-
5 Ways SQL is Used in the Industry
-
Quiz
SQL Advanced: Supply Chain Analytics
14 Lectures
-
Supply Chain Basics : Simplified
-
Problem Statement
-
Create a Helper Table
-
Database Triggers
-
Database Events
-
Temporary Tables & Forecast Accuracy Report
-
Exercise: CTE, Temporary Tables
-
Subquery vs CTE vs Views vs Temporary Table
-
User Accounts and Privileges
-
Database Indexes: Overview
-
Database Indexes: Composite Index
-
Database Indexes: Index Types
-
Peter Pandey's Order: I Have Completed the Course - Now What?
-
Quiz
What's Next?
00:00 on-demand video
|
1 Lectures
Live Webinar
30h:18m:35s on-demand video
|
22 Lectures
Welcome to The Gen AI and Data Science Bootcamp Experience
01h:13m:35s on-demand video
|
20 Lectures
1:
Welcome to The Bootcamp Experience
7 Lectures
-
1.1: What will I get in this “foundation” bootcamp?
Free -
1.2: Why should I become a data scientist / AI engineer?
Free -
1.3: Quality over Quantity
Free -
1.4: Do You Provide Job Assistance in this Foundation Bootcamp?
Free -
1.5: How Do I Get Doubt-Clearance Support? (Discord)
Free -
1.6: Unlock Discord Channels
-
1.7: Bootcamp overview
Free
Python: Beginner to Advanced For Data Professionals
16h:57m:31s on-demand video
|
108 Lectures
6:
Python Basics: Functions, Dictionaries, Tuples and File Handling
9 Lectures
-
6.1: Functions
-
6.2: Dictionary and Tuples
-
6.3: Modules and Pip
-
6.4: File Handling
-
6.5: Quiz: Functions, Dictionaries, Tuples and File Handling
-
6.6: Peter’s Request to Tony
-
6.7: Exercise: Functions, Dictionaries, Tuples and File Handling
-
6.8: Two Deadly Viruses Infecting Learners
-
6.9: Chapter Summary
15:
Project 2: Expense Tracking System
11 Lectures
-
15.1: Problem Statement & Tech Architecture
-
15.2: Database CRUD Operations
-
15.3: Automated Tests Setup for CRUD
-
15.4: Expense Management: Backend (FastAPI)
-
15.5: Expense Management: Logging
-
15.6: Streamlit Introduction
-
15.7: Expense Management: Frontend (Streamlit)
-
15.8: Analytics: Backend (FastAPI)
-
15.9: Analytics: Frontend (Streamlit)
-
15.10: README and Requirements.txt
-
15.11: Exercise
Online Credibility
00h:24m:12s on-demand video
|
4 Lectures
SQL for Data Science
02h:02m:08s on-demand video
|
86 Lectures
2:
SQL Basics: Data Retrieval - Single Table
14 Lectures
-
2.1: Install MySQL: Windows
Free -
2.2: Install MySQL: Linux, Mac
Free -
2.3: Import Movies Dataset in MySQL
Free -
2.4: Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)
Free -
2.5: Exercise - Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)
Free -
2.6: Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)
Free -
2.7: Exercise - Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)
Free -
2.8: Summary Analytics (MIN, MAX, AVG, GROUP BY)
Free -
2.9: Exercise - Summary Analytics (MIN, MAX, AVG, GROUP BY)
Free -
2.10: HAVING Clause
Free -
2.11: Calculated Columns (IF, CASE, YEAR, CURYEAR)
Free -
2.12: Exercise - Calculated Columns (IF, CASE, YEAR, CURYEAR)
Free -
2.13: The Data God’s Blessing
Free -
2.14: Quiz
Math and Statistics For AI, Data Science
12h:49m:38s on-demand video
|
98 Lectures
3:
Measures Of Central Tendency and Dispersion
18 Lectures
-
3.1: Descriptive vs. Inferential Statistics
Free -
3.2: Measures of Central Tendency: Mean, Median, Mode
Free -
3.3: Percentile
-
3.4: Analysis: Shoe Sales (Using Mean, Median, Percentile)
-
3.5: Quiz
-
3.6: Exercise
-
3.7: Measures of Dispersion: Range, IQR
-
3.8: Box or Whisker Plot
-
3.9: Outlier Treatment Using IQR and Box Plot
-
3.10: Quiz
-
3.11: Exercise
-
3.12: Measures of Dispersion: Variance and Standard Deviation
-
3.13: Analysis: Stock Returns Volatility (Using Variance and Std Dev)
-
3.14: Correlation
-
3.15: Correlation vs Causation
-
3.16: Quiz
-
3.17: Exercise
-
3.18: Chapter Summary
7:
Phase 1: Find Target Market
21 Lectures
-
7.1: Data Validation Of Acquired Data
-
7.2: Data Understanding, MySQL Setup
-
7.3: Data Import in Jupyter Notebook
-
7.4: Data Cleaning: Handle NULL Values (Annual Income)
-
7.5: Data Cleaning: Treat Outliers (Annual Income)
-
7.6: Data Visualization: Annual Income
-
7.7: Exercise: Treat Outliers in Age Column
-
7.8: Exercise Solution: Treat Outliers in Age Column
-
7.9: Data Visualization: Age, Gender, Location
-
7.10: Peter’s Nightmare
-
7.11: Data Cleaning: Credit Score Table - Part 1
-
7.12: Data Cleaning: Credit Score Table - Part 2
-
7.13: Correlation among Credit Profile Variables
-
7.14: Exercise: Handle NULL Values in Transactions Table
-
7.15: Exercise Solution: Handle NULL Values in Transactions Table
-
7.16: Peter’s Confusion: IQR or Std Dev?
-
7.17: Data Cleaning: Treat Outliers using IQR (Transaction Amount)
-
7.18: Data Visualization: Transactions Table
-
7.19: Finalize the Target Group
-
7.20: Phase 1 Feedback Meeting With Stakeholders
-
7.21: Get Ready For Phase 2
8:
Central Limit Theorem
12 Lectures
-
8.1: Random Sampling & Sample Bias
-
8.2: The Law of Large Numbers
-
8.3: Central Limit Theorem, Sampling Distribution
-
8.4: Case Study: Solar Panels
-
8.5: Standard Error
-
8.6: Quiz
-
8.7: Z Score Table (Z-Table)
-
8.8: Quiz
-
8.9: Confidence Interval
-
8.10: Confidence Interval: Estimate Car Miles
-
8.11: Exercise
-
8.12: Chapter Summary
9:
Hypothesis Testing
19 Lectures
-
9.1: Null vs Alternate Hypothesis
-
9.2: Z Test, Rejection Region
-
9.3: Housing Inflation Test: Rejection Region
-
9.4: Quiz
-
9.5: Exercise
-
9.6: p-Value
-
9.7: Housing Inflation Test: p-Value
-
9.8: Quiz
-
9.9: Exercise
-
9.10: One-Tailed vs Two-Tailed Test
-
9.11: Type 1 and Type 2 Errors
-
9.12: Quiz
-
9.13: Statistical Power & Effect Size
-
9.14: A/B Testing
-
9.15: A/B Testing Using Z Test
-
9.16: A/B Testing: Drug Trial
-
9.17: Quiz
-
9.18: Exercise
-
9.19: Chapter Summary
Online Credibility & Domain Knowledge Course
00h:31m:00s on-demand video
|
5 Lectures
Master Machine Learning for Data Science & AI: Beginner to Advanced
22h:41m:19s on-demand video
|
201 Lectures
3:
Supervised Machine Learning: Regression
29 Lectures
-
3.1: Simple Linear Regression
Free -
3.2: Multiple Linear Regression
Free -
3.3: Quiz
-
3.4: Exercise
-
3.5: Cost Function
-
3.6: Derivatives and Partial Derivatives
-
3.7: Chain Rule
-
3.8: Quiz
-
3.9: Exercise
-
3.10: Gradient Descent Theory
-
3.11: Gradient Descent: Python Implementation
-
3.12: Why MSE (and not MAE)?
-
3.13: Model Evaluation: Train, Test Split
-
3.14: Model Evaluation: Metrics
-
3.15: Peter Pandey Flexes his ML skills on LinkedIn
-
3.16: Quiz
-
3.17: Exercise
-
3.18: Data Preprocessing: One Hot Encoding
-
3.19: Quiz
-
3.20: Polynomial Regression
-
3.21: Quiz
-
3.22: Exercise
-
3.23: Overfitting and Underfitting
-
3.24: Reasons and Remedies For Overfitting / Underfitting
-
3.25: L1 and L2 Regularization
-
3.26: Bias Variance Trade Off
-
3.27: Quiz
-
3.28: Exercise
-
3.29: Chapter Summary
4:
Supervised Machine Learning: Classification
31 Lectures
-
4.1: Introduction to Classification
Free -
4.2: Logistic Regression: Binary Classification
Free -
4.3: Model Evaluation: Accuracy, Precision and Recall
-
4.4: Quiz
-
4.5: Exercise
-
4.6: Model Evaluation: F1 Score, Confusion Matrix
-
4.7: Logistic Regression: Multiclass Classification
-
4.8: Cost Function: Log Loss
-
4.9: Quiz
-
4.10: Exercise
-
4.11: Support Vector Machine (SVM)
-
4.12: Data Pre-processing: Scaling
-
4.13: Sklearn Pipeline
-
4.14: Quiz (disabled)
-
4.15: Quiz
-
4.16: Exercise
-
4.17: Naive Bayes: Theory
-
4.18: Naive Bayes: SMS Spam Classification
-
4.19: Quiz
-
4.20: Exercise
-
4.21: Decision Tree: Theory
-
4.22: Decision Tree: Salary Classification
-
4.23: I Need a Favour
-
4.24: Quiz
-
4.25: Exercise
-
4.26: Handle Class Imbalance: Theory
-
4.27: Handle Class Imbalance Using imblearn: Churn Prediction
-
4.28: Quiz
-
4.29: Exercise
-
4.30: Get inspired by Peter Pandey
-
4.31: Chapter Summary
5:
Ensemble Learning
21 Lectures
-
5.1: What is Ensemble Learning?
Free -
5.2: Majority Voting, Average and Weighted Average
-
5.3: Bagging
-
5.4: Bagging: Random Forest
-
5.5: Random Forest: Raisin Classification
-
5.6: Quiz
-
5.7: Exercise
-
5.8: Boosting: AdaBoost
-
5.9: Gradient Boosting: Regression Walk Through
-
5.10: Gradient Boosting: Regression Math
-
5.11: Gradient Boosting: Revenue Prediction
-
5.12: Quiz
-
5.13: Exercise
-
5.14: Gradient Boosting: Classification
-
5.15: XGBoost: Walk Through
-
5.16: XGBoost: California Housing Prediction
-
5.17: XGBoost: Synthetic Data Classification
-
5.18: XGBoost: Benefits
-
5.19: Quiz
-
5.20: Exercise
-
5.21: Chapter Summary
6:
Model Evaluation & Fine Tuning
16 Lectures
-
6.1: Introduction
-
6.2: Model Evaluation: ROC Curve & AUC
-
6.3: Cost Benefit Analysis Using ROC in Sklearn
-
6.4: Quiz
-
6.5: Exercise
-
6.6: K Fold Cross Validation
-
6.7: Stratified K Fold Cross Validation
-
6.8: Hyperparameter Tuning: GridsearchCV
-
6.9: Hyperparameter Tuning: RandomizedSearchCV
-
6.10: Quiz
-
6.11: Exercise
-
6.12: Model Selection Guide
-
6.13: Luck favors the LinkedIn post
-
6.14: Selecting the Right Evaluation Metric
-
6.15: Quiz
-
6.16: Chapter Summary
7:
ML Project Life Cycle
10 Lectures
-
7.1: 10 Stages of AI Project Life Cycle
Free -
7.2: Requirements and Scope of Work (SOW)
Free -
7.3: Data Collection
-
7.4: Data Cleaning & Exploratory Data Analysis
-
7.5: Feature Engineering
-
7.6: Model Selection & Training
-
7.7: Model Fine Tuning
-
7.8: Model Deployment
-
7.9: Monitoring and Feedback Using ML Ops
-
7.10: Chapter Summary
9:
Unsupervised Learning
13 Lectures
-
9.1: Introduction
-
9.2: K Means Clustering: Theory
-
9.3: K Means Clustering: Customer Segmentation
-
9.4: Hierarchical Clustering: Theory
-
9.5: Hierarchical Clustering: Customer Segmentation
-
9.6: Quiz
-
9.7: Exercise
-
9.8: DBSCAN: Theory
-
9.9: DBSCAN: Practical Implementation
-
9.10: Peter AI
-
9.11: Quiz
-
9.12: Exercise
-
9.13: Chapter Summary
10:
Project 1: Healthcare Premium Prediction (Regression)
16 Lectures
-
10.1: The Rise of AtliQ AI
Free -
10.2: Project Charter Meeting
Free -
10.3: Scope of Work, Task Planning in JIRA
-
10.4: Data Collection
-
10.5: Data Cleaning & EDA - Part 1
-
10.6: Data Cleaning & EDA - Part 2
-
10.7: Feature Engineering
-
10.8: Model Training, Fine Tunning
-
10.9: 98% Model Accuracy, Really?
-
10.10: Error Analysis
-
10.11: Model Segmentation
-
10.12: Request More Data
-
10.13: Model Retraining
-
10.14: Build App Using Streamlit
-
10.15: Deployment
-
10.16: Exercise
11:
Project 2: Credit Risk Modelling (Classification)
19 Lectures
-
11.1: Peter's Promotion: New Project
Free -
11.2: Domain Understanding: NBFC & Credit Approvals
Free -
11.3: Scope of Work & Tech Architecture
Free -
11.4: Data Collection
-
11.5: Quick Intro to Data Leakage
-
11.6: Data Cleaning
-
11.7: Exploratory Data Analysis (EDA)
-
11.8: Feature Engineering – Part 1
-
11.9: Weight of Evidence (WOE), Information Value (IV)
-
11.10: Feature Engineering – Part 2
-
11.11: Model Training & Evaluation
-
11.12: Introduction to Optuna
-
11.13: Model Fine Tuning Using Optuna
-
11.14: Intro To Rank Ordering & KS Statistic
-
11.15: Model Evaluation Using KS Statistic & Gini Coefficient
-
11.16: Streamlit App
-
11.17: Business Presentation
-
11.18: Deployment
-
11.19: Exercise
12:
ML Ops & Cloud Tools
22 Lectures
-
12.1: What is ML Ops?
Free -
12.2: Importance of ML Ops in Your Career
Free -
12.3: ML Flow: Purpose and Overview
Free -
12.4: ML Flow: Experiment Tracking
-
12.5: ML Flow: Model Registry
-
12.6: ML Flow: Centralized Server Using Dagshub
-
12.7: Quiz
-
12.8: What is API?
-
12.9: FastAPI Basics
-
12.10: Build FastAPI Server For Credit Risk Project
-
12.11: Quiz
-
12.12: Git Version Control System
-
12.13: Introduction to ML Cloud Platforms
-
12.14: AWS Sagemaker: Account Setup
-
12.15: AWS Sagemaker: Sagemaker Studio
-
12.16: AWS Sagemaker: 4 Ways to Train Model
-
12.17: AWS Sagemaker: Built In Algorithms
-
12.18: AWS Sagemaker: Script Mode
-
12.19: Quiz
-
12.20: Data Drift Detection Using PSI & CSI
-
12.21: PSI & CSI: Practical Implementation
-
12.22: Quiz
Build your Portfolio Website
24min on-demand video
|
7 Lectures
Virtual Internship
00h:09m:49s on-demand video
|
10 Lectures
2:
Week1
29 Lectures
-
2.1: Welcome Note
-
2.2: Your Onboarding Letter
-
2.3: Welcome Note from Your Manager
-
2.4: Let's Dive Right Into Week 1!
-
2.5: Getting Help From Your Mentors / Seniors
-
2.6: Your First Task
-
2.7: Incoming Task Email 1
-
2.8: Have You Completed This Task?
-
2.9: Quality Check 1
-
2.10: Quality Check 2
-
2.11: Quality Check 3
-
2.12: Quality Check 4
-
2.13: Quality Check 5
-
2.14: Quality Check 6
-
2.15: Quality Check 7
-
2.16: Quality Check 8
-
2.17: Quality Check 9
-
2.18: Quality Check 10
-
2.19: Congratulations, You Have Completed the First Task of Your Internship.
-
2.20: Your Second Task
-
2.21: Incoming Task Email 2
-
2.22: Have You Completed the Assigned Task?
-
2.23: Presentation Submission (Visualization Task)
-
2.24: Congratulations! You Have Completed 2 Tasks in a Row!
-
2.25: You Need Scrum Training
-
2.26: Incoming Task Email 3
-
2.27: Have You Completed the Assigned Task?
-
2.28: Scrum Knowledge Check
-
2.29: Congratulations, You Have Completed Week 1 Successfully.
3:
Week 2
26 Lectures
-
3.1: Let's Dive Right into Week 2!
-
3.2: Can You Handle this Project?
-
3.3: Incoming Task Email 1
-
3.4: Have You Completed the Assigned Task?
-
3.5: Quality Check 1
-
3.6: Quality Check 2
-
3.7: Quality Check 3
-
3.8: Quality Check 4
-
3.9: Quality Check 5
-
3.10: Quality Check 6
-
3.11: Quality Check 7
-
3.12: Quality Check 8
-
3.13: Quality Check 9
-
3.14: Quality Check 10
-
3.15: Congratulations on Finishing This Task!
-
3.16: SQL Query Debugging
-
3.17: Incoming Task Email 2
-
3.18: Quality Check 1
-
3.19: Quality Check 2
-
3.20: Quality Check 3
-
3.21: Quality Check 4
-
3.22: Quality Check 5
-
3.23: Quality Check 6
-
3.24: Quality Check 7
-
3.25: Quality Check 8
-
3.26: Congratulations on Successfully Completing the Task!
4:
Week 3 & 4
26 Lectures
-
4.1: Let's Dive Right into the Final 2 Weeks!
-
4.2: Can You Step Up for a Client Task?
-
4.3: Incoming Task Email: Data Cleaning
-
4.4: Please Don't Share Datasets
-
4.5: Have You Completed the Data Cleaning?
-
4.6: Quality Check 1
-
4.7: Quality Check 2
-
4.8: Quality Check 3
-
4.9: Quality Check 4
-
4.10: Incoming Task Email: Feature Engineering
-
4.11: Have You Completed the Feature Engineering Task?
-
4.12: Quality Check 1
-
4.13: Quality Check 2
-
4.14: Quality Check 3
-
4.15: Quality Check 4
-
4.16: Let's Start Predictive Modeling
-
4.17: Incoming Task Email: Predictive Modeling
-
4.18: Have You Completed the Modeling Task?
-
4.19: Quality Check
-
4.20: Incoming Task Email: MLflow Deployment
-
4.21: Submission of DagsHub link | ML flow
-
4.22: Incoming Task Email: Streamlit App Development
-
4.23: Have You Completed the Streamlit App Development?
-
4.24: Can You Present to Client?
-
4.25: Presentation Submission (CodeX Project)
-
4.26: End Note
Get Your Certificate
00:00 on-demand video
|
0 Lectures
AI for Everyone
16h:37m:39s on-demand video
|
67 Lectures
3:
Demystify AI: Gen AI, LLMs and NLP
10 Lectures
-
3.1: What is Generative AI (or Gen AI)?
-
3.2: Evolution of Gen AI Models
-
3.3: What is LLM? Analogy Based Simple Explanation
-
3.4: What is NLP?
-
3.5: Embeddings and Vector Database
-
3.6: Retrieval Augmented Generation (RAG)
-
3.7: Prompt Engineering
-
3.8: Tooling for Gen AI, LLM
-
3.9: Takeaways & Jargons
-
3.10: Quiz
8:
Supplementary Learning (Industry Projects)
6 Lectures
-
8.1: Intro: How to use this supplementary learning
-
8.2: Statistical ML Project: Build a House Prediction System
-
8.3: Deep Learning Project: Build a Potato Disease Classification System
-
8.4: NLP Project: Build a chatbot using Dialog Flow
-
8.5: Gen AI Project: Build a Q & A system in retail domain
-
8.6: Gen AI Project: Build a news research tool in finance domain
SQL Beginner to Advanced For Data Professionals
11h:15m:16s on-demand video
|
84 Lectures
2:
SQL Basics: Database Creation & Updates
17 Lectures
-
2.1: Database Normalization and Data Integrity
-
2.2: Entity Relationship Diagram (ERD)
-
2.3: Mentor Talk: Art of Googling
-
2.4: Data Types: Numeric (INT, DECIMAL, FLOAT, DOUBLE)
-
2.5: Data Types: String (VARCHAR, CHAR, ENUM)
-
2.6: Data Types: Date, Time (DATETIME, DATE, TIME, YEAR, TIMESTAMP)
-
2.7: Data Types: JSON, Spatial (JSON, GEOMETRY)
-
2.8: Luck Favors the LinkedIn Post
-
2.9: Primary key
-
2.10: Foreign Key
-
2.11: Create a Database From an Entity Relationship Diagram - ERD
-
2.12: Import Data From a CSV File Into a Database
-
2.13: Insert Statement
-
2.14: Update and Delete
-
2.15: I Need a Favour
-
2.16: Expect the Unexpected: The Intermission Scene
-
2.17: Quiz
3:
AtliQ Hardware & Problem Statement
8 Lectures
-
3.1: The Rise of Databases at AtliQ
Free -
3.2: Relational vs No-SQL Database
-
3.3: AtliQ Hardware’s Business Model
-
3.4: Profit & Loss Statement
-
3.5: ETL, Data Warehouse, OLAP vs OLTP, Data Catalog
-
3.6: Fact vs Dimension Table, Star vs Snowflake Schema, Data Import
-
3.7: Simplified: What is Kanban?
-
3.8: Quiz
4:
SQL Advanced: Finance Analytics
10 Lectures
-
4.1: Backlog Grooming Meeting: Gross Sales Report
-
4.2: User-Defined SQL Functions
-
4.3: Exercise: User-Defined SQL Functions
-
4.4: Gross Sales Report: Monthly Product Transactions
-
4.5: Gross Sales Report: Total Sales Amount
-
4.6: Exercise: Yearly Sales Report
-
4.7: Stored Procedures: Monthly Gross Sales Report
-
4.8: Stored Procedure: Market Badge
-
4.9: Benefits of Stored Procedures
-
4.10: Quiz
5:
SQL Advanced: Top Customers, Products, Markets
16 Lectures
-
5.1: Problem Statement and Pre-Invoice Discount Report
-
5.2: Performance Improvement # 1
-
5.3: Performance Improvement # 2
-
5.4: Database Views: Introduction
-
5.5: Database Views: Post Invoice Discount, Net Sales
-
5.6: Exercise: Database Views
-
5.7: Top Markets and Customers
-
5.8: Exercise: Top Products
-
5.9: The Two Most Important Skills for the Data Analyst
-
5.10: Window Functions: OVER Clause
-
5.11: Window Functions: Using it in a Task
-
5.12: Exercise: Window Functions: OVER Clause
-
5.13: Window Functions: ROW_NUMBER, RANK, DENSE_RANK
-
5.14: Exercise: Window Functions: ROW_NUMBER, RANK, DENSE_RANK
-
5.15: 5 Ways SQL is Used in the Industry
-
5.16: Quiz
6:
SQL Advanced: Supply Chain Analytics
14 Lectures
-
6.1: Supply Chain Basics : Simplified
-
6.2: Problem Statement
-
6.3: Create a Helper Table
-
6.4: Database Triggers
-
6.5: Database Events
-
6.6: Temporary Tables & Forecast Accuracy Report
-
6.7: Exercise: CTE, Temporary Tables
-
6.8: Subquery vs CTE vs Views vs Temporary Table
-
6.9: User Accounts and Privileges
-
6.10: Database Indexes: Overview
-
6.11: Database Indexes: Composite Index
-
6.12: Database Indexes: Index Types
-
6.13: Peter Pandey's Order: I Have Completed the Course - Now What?
-
6.14: Quiz
What's Next?
00:00 on-demand video
|
1 Lectures
Live Webinar
30h:18m:35s on-demand video
|
22 Lectures
May we help you?
Frequently Asked
Questions
Q.1
Are the lectures going to be LIVE?
To keep the bootcamp affordable, we have kept the lectures as self-paced videos that you can watch at your convenience. However, there will be monthly seminars and live discord community support.
Q.2
Do you provide any Virtual Internship?
Yes, there is Virtual Internship, included in this BootCamp.
Q.3
Can I expect to work on real-world datasets during the course?
The datasets used in this bootcamp are crafted from scratch to mimic the real business world by consolidating our years of experience. The datasets not just contain more than a million rows but also cover multiple facets of the business.
Q.4
Does this bootcamp provide lifetime access to the content?
Yes, this bootcamp will be available to you for a lifetime.
Q.1
What if I have an unrelated degree but want to pursue data science?
A computer science degree or an IT degree is not required. We have many success stories of folks from non-tech backgrounds successfully cracking into a data science career.
Q.2
What prior knowledge or skills do I need before enrolling in this bootcamp?
The only thing you need as a prerequisite is the passion and interest in learning AI and Data Science. Actual prerequisites such as Python, Math, and Statistics basics are all covered in this course.
Q.3
How much coding/programming do I need to know to become a Data Scientist?
The first module in our bootcamp is the Python module where we teach everything from basics. You don't need to be an expert coder, but proficiency in data manipulation, visualization, and basic algorithms is essential.
Q.4
Can I enroll in the bootcamp if I have no background in mathematics or statistics?
Yes. In this bootcamp, we have a module on Math and statistics. This module designed for absolute beginners, simplifies learning Math and Statistics through project-based learning with a real dataset, focusing on practical data science applications.
Q.1
Will I receive a certificate upon completing the bootcamp?
Upon successfully completing each tool, you will receive a certificate. In total, you'll earn four individual certificates for Python, SQL, Maths & Statistics, and Machine Learning, as well as another one for completing the entire Bootcamp. Additionally, you'll also receive a letter of completion for the Virtual Internship.
Q.1
Do you have an EMI (Installment) option?
Our website doesn't support an EMI option right now. But don't worry, you can still pay in installments by following these steps,
(1) First buy the Python course.
(2) After you have completed the Python course, buy the Math and Statistics course.
(3) After completing the Math and Statistics course, buy the Bootcamp.
This way you will pay in 3 installments
Remember that the Bootcamp includes the same content from individual Python and Math and Statistics courses that you bought previously hence you will not lose anything in terms learning in your Bootcamp curriculum.
Also, all the progress you made in these individual courses will be transferred to Bootcamp automatically.
Q.2
I have already purchased other Codebasics courses like SQL and Python. Will I have to pay the full amount to enroll in this bootcamp?
No, since both of these courses are part of this bootcamp, the amount you've already paid for the SQL and Python courses will be deducted from the bootcamp price.
Q.3
I’m already enrolled in the Codebasics Data Analytics Bootcamp. Will I have to pay the full amount to enroll in this bootcamp?
No, since the SQL and Python courses in the Data Analytics Bootcamp are also part of this bootcamp, the price you’ve paid for those courses will be deducted from the bootcamp price.
Q.4
How do I get the certificate?
You need to complete at least 70% of the course and score 70% or more on the final quiz. After completing the quiz, simply click “complete and get certificate” to download your certificate.
Q.1
What if I don’t like this bootcamp? Is there a refund policy?
Yes, we offer a no-questions-asked refund policy. No hidden conditions—if you request a refund within 30 days of enrollment, you will receive a 100% refund.
Q.2
How can I request a refund?
Simply email us at [email protected] with your name, course name, course purchase date, and the registered email ID you used on the Codebasics website. We will initiate the refund after verifying your details and eligibility according to our refund policy.
Q.1
What kind of support is available if I have questions during the bootcamp?
We have created every lecture of this bootcamp with the motive to explain everything in an easy-to-understand manner. While working on these lectures you could make mistakes in steps or have some doubts. You need to commit yourself to hold patience, make efforts & figure out the solution by googling in order to become truly job ready.
For any questions, that Google cannot answer or if you hit a wall - we got you covered!
You can join our active discord community, which is a dedicated platform to discuss & clear your doubts with fellow learners & mentors.
Q.2
Is there a community platform where I can interact with other students?
Yes, you’ll be part of an active Discord community where you can interact, and learn from one another.
Q.1
Are the lectures going to be LIVE?
To keep the bootcamp affordable, we have kept the lectures as self-paced videos that you can watch at your convenience. However, there will be monthly seminars and live discord community support.
Q.2
Do you provide any Virtual Internship?
Yes, there is Virtual Internship, included in this BootCamp.
Q.3
Can I expect to work on real-world datasets during the course?
The datasets used in this bootcamp are crafted from scratch to mimic the real business world by consolidating our years of experience. The datasets not just contain more than a million rows but also cover multiple facets of the business.
Q.4
Does this bootcamp provide lifetime access to the content?
Yes, this bootcamp will be available to you for a lifetime.
Q.1
What if I have an unrelated degree but want to pursue data science?
A computer science degree or an IT degree is not required. We have many success stories of folks from non-tech backgrounds successfully cracking into a data science career.
Q.2
What prior knowledge or skills do I need before enrolling in this bootcamp?
The only thing you need as a prerequisite is the passion and interest in learning AI and Data Science. Actual prerequisites such as Python, Math, and Statistics basics are all covered in this course.
Q.3
How much coding/programming do I need to know to become a Data Scientist?
The first module in our bootcamp is the Python module where we teach everything from basics. You don't need to be an expert coder, but proficiency in data manipulation, visualization, and basic algorithms is essential.
Q.4
Can I enroll in the bootcamp if I have no background in mathematics or statistics?
Yes. In this bootcamp, we have a module on Math and statistics. This module designed for absolute beginners, simplifies learning Math and Statistics through project-based learning with a real dataset, focusing on practical data science applications.
Q.1
Will I receive a certificate upon completing the bootcamp?
Upon successfully completing each tool, you will receive a certificate. In total, you'll earn four individual certificates for Python, SQL, Maths & Statistics, and Machine Learning, as well as another one for completing the entire Bootcamp. Additionally, you'll also receive a letter of completion for the Virtual Internship.
Q.1
Do you have an EMI (Installment) option?
Our website doesn't support an EMI option right now. But don't worry, you can still pay in installments by following these steps,
(1) First buy the Python course.
(2) After you have completed the Python course, buy the Math and Statistics course.
(3) After completing the Math and Statistics course, buy the Bootcamp.
This way you will pay in 3 installments
Remember that the Bootcamp includes the same content from individual Python and Math and Statistics courses that you bought previously hence you will not lose anything in terms learning in your Bootcamp curriculum.
Also, all the progress you made in these individual courses will be transferred to Bootcamp automatically.
Q.2
I have already purchased other Codebasics courses like SQL and Python. Will I have to pay the full amount to enroll in this bootcamp?
No, since both of these courses are part of this bootcamp, the amount you've already paid for the SQL and Python courses will be deducted from the bootcamp price.
Q.3
I’m already enrolled in the Codebasics Data Analytics Bootcamp. Will I have to pay the full amount to enroll in this bootcamp?
No, since the SQL and Python courses in the Data Analytics Bootcamp are also part of this bootcamp, the price you’ve paid for those courses will be deducted from the bootcamp price.
Q.4
How do I get the certificate?
You need to complete at least 70% of the course and score 70% or more on the final quiz. After completing the quiz, simply click “complete and get certificate” to download your certificate.
Q.1
What if I don’t like this bootcamp? Is there a refund policy?
Yes, we offer a no-questions-asked refund policy. No hidden conditions—if you request a refund within 30 days of enrollment, you will receive a 100% refund.
Q.2
How can I request a refund?
Simply email us at [email protected] with your name, course name, course purchase date, and the registered email ID you used on the Codebasics website. We will initiate the refund after verifying your details and eligibility according to our refund policy.
Q.1
What kind of support is available if I have questions during the bootcamp?
We have created every lecture of this bootcamp with the motive to explain everything in an easy-to-understand manner. While working on these lectures you could make mistakes in steps or have some doubts. You need to commit yourself to hold patience, make efforts & figure out the solution by googling in order to become truly job ready.
For any questions, that Google cannot answer or if you hit a wall - we got you covered!
You can join our active discord community, which is a dedicated platform to discuss & clear your doubts with fellow learners & mentors.
Q.2
Is there a community platform where I can interact with other students?
Yes, you’ll be part of an active Discord community where you can interact, and learn from one another.
Learning knows no limits. Here’s to your journey of seamless learning. Pick your preferred course from the list of paid & free resources.
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