Learn AI For Data Analysis Online: Free Course with AI Tutor & Certificate
Data enthusiasts and aspiring professionals will learn how to apply machine learning to large datasets in this free AI data analyst course. You'll complete 11 modules and 68 video lessons taking about 1 hours. By the end, you'll know how to perform data preprocessing and build neural networks.
What you'll learn in this AI For Data Analysis course
Five concrete outcomes you'll walk away with, each mapped to modules in the curriculum below.
Apply various machine learning algorithms for data analysis.
Perform data preprocessing techniques effectively.
Utilize clustering techniques for pattern recognition.
Implement dimensionality reduction methods for complex data sets.
Understand and apply neural networks for data analysis.
AI For Data Analysis course syllabus
01 Introduction to AI for Data Analysis
- Overview
- Importance of AI in Data Analysis
- Basic Concepts of AI
- Applications of AI in Data Analysis
02 Data Preprocessing
- Data Cleaning
- Data Transformation
- Data Reduction
- Data Integration
- Data Discretization
03 Machine Learning Algorithms for Data Analysis
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Support Vector Machines
04 Clustering Techniques
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN
- Gaussian Mixture Models
05 Dimensionality Reduction
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Linear Discriminant Analysis (LDA)
- Autoencoders
06 Natural Language Processing (NLP) for Data Analysis
- Text Preprocessing
- Sentiment Analysis
- Named Entity Recognition
- Topic Modeling
- Text Classification
07 Time Series Analysis
- Time Series Decomposition
- Forecasting Techniques
- Seasonal Decomposition of Time Series (STL)
- ARIMA Models
08 Neural Networks for Data Analysis
- Introduction to Neural Networks
- Feedforward Neural Networks
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Long Short-Term Memory (LSTM)
09 Model Evaluation and Validation
- Cross-Validation
- Confusion Matrix
- Precision, Recall, and F1 Score
- ROC Curve and AUC
10 Feature Engineering
- 1 Feature Selection
- 1.1 Importance of Feature Selection|Understanding why feature selection is crucial in building effective machine learning models.
- 1.2 Feature Selection Techniques|Exploring various techniques for selecting the most relevant features, such as filter and wrapper methods.
- 1.3 Impact on Model Performance|Discussing how feature selection can impact the performance and interpretability of models.
- 2 Feature Extraction
- 2.1 Transforming Features|Understanding the process of transforming raw data into a set of features that better represent the underlying problem.
- 2.2 Principal Component Analysis (PCA)|Exploring PCA as a feature extraction technique and its application in reducing dimensionality.
- 2.3 Other Feature Extraction Methods|Discussing other methods for feature extraction, such as Independent Component Analysis (ICA) and Linear Discriminant Analysis (LDA).
- 3 Feature Scaling
- 3.1 Need for Feature Scaling|Understanding the need for scaling features to a uniform range in machine learning.
- 3.2 Standardization vs. Normalization|Exploring the differences between standardization and normalization and when to use each.
- 3.3 Scaling Techniques and Their Effects|Discussing various scaling techniques and their effects on model training and performance.
- 4 Feature Importance
- 4.1 Assessing Feature Relevance|Understanding methods for assessing the relevance of features to the model's predictions.
- 4.2 Permutation Feature Importance|Exploring permutation-based methods for evaluating feature importance.
- 4.3 Model-Specific Feature Importance|Discussing feature importance metrics specific to certain models, such as Gini importance for tree-based models.
11 Anomaly Detection
- 1 Types of Anomalies
- 1.1 Defining Anomalies|Understanding the different types of anomalies that can occur in data, such as point, contextual, and collective anomalies.
- 1.2 Anomaly Detection in Various Domains|Exploring the importance of anomaly detection in domains like finance, healthcare, and cybersecurity.
- 1.3 Challenges in Anomaly Detection|Discussing the challenges faced in accurately detecting anomalies in complex datasets.
- 2 Anomaly Detection Techniques
- 2.1 Statistical Methods|Understanding traditional statistical methods for anomaly detection, such as Z-score and Grubbs' test.
- 2.2 Machine Learning Approaches|Exploring machine learning approaches to anomaly detection, including supervised and unsupervised methods.
- 2.3 Hybrid Methods|Discussing the combination of statistical and machine learning methods for improved anomaly detection.
- 3 Isolation Forest
- 3.1 Principle of Isolation|Introducing the concept of isolation in identifying anomalies and how isolation forests implement it.
- 3.2 Algorithm Details|Understanding the algorithmic details of isolation forests and their advantages over other methods.
- 3.3 Practical Applications|Discussing the practical applications of isolation forests in various
Real-world projects you'll work on
Practice the exact kind of work AI For Data Analysis professionals do on the job.
- AI-Powered Customer Segmentation for Personalized Marketing Strategies at a Leading E-commerce Giant
- Predictive Maintenance Using Machine Learning Algorithms for Optimized Operations at a Tech Startup Disrupting the Industry
- Sentiment Analysis and Topic Modeling for Enhanced Customer Engagement at a Global Social Media Platform
- Anomaly Detection and Bias Mitigation Strategies for Ethical AI Implementation at a Top Financial Services Firm
Skills you'll gain
Add these to your LinkedIn profile and resume the day you finish.
Who should take this AI For Data Analysis course?
And exactly what you need before starting (spoiler: almost nothing).
This course is for
- Students in India looking to build a foundation in data science and machine learning.
- Working professionals who want to transition into an ai data analyst role.
- Career switchers seeking practical experience with neural networks and time series analysis.
- Business analysts hoping to automate data preprocessing and feature engineering tasks.
Prerequisites
- No prior experience is strictly required, but basic computer literacy helps.
- Familiarity with basic statistics and probability is beneficial.
- A basic understanding of Python programming will make the machine learning concepts easier to grasp.
How learning works on LearnTube
The structure of a bootcamp, the price of free.
AI curates your path
Our AI picks the top 1% of video content for each topic and arranges it into a structured, goal-based curriculum. No more tutorial roulette.
Learn, then get quizzed
After every lesson, your AI tutor asks you a question and gives personalised feedback on your answer, so gaps get fixed immediately.
Finish with proof
Complete the course, build the capstone project, and claim a certificate recognised by 900+ hiring partners.
A complete AI For Data Analysis course: free, structured and job-focused
Aspiring analysts and tech professionals will learn how to process complex datasets using modern algorithms in this ai data analytics course online. You'll start with an Introduction to AI for Data Analysis before moving through 11 specific modules. The curriculum covers Data Preprocessing, Machine Learning Algorithms for Data Analysis, and Clustering Techniques. Later sections teach Dimensionality Reduction, Natural Language Processing (NLP) for Data Analysis, and Time Series Analysis. You'll finish by exploring Neural Networks for Data Analysis, Model Evaluation and Validation, alongside Feature Engineering and Anomaly Detection.
You'll apply these skills across four hands-on projects to build a real portfolio. These include AI-Powered Customer Segmentation for Personalized Marketing Strategies at a Leading E-commerce Giant and Predictive Maintenance Using Machine Learning Algorithms for Optimized Operations at a Tech Startup Disrupting the Industry. You will also complete Sentiment Analysis and Topic Modeling for Enhanced Customer Engagement at a Global Social Media Platform, plus Anomaly Detection and Bias Mitigation Strategies for Ethical AI Implementation at a Top Financial Services Firm.
LearnTube makes it easy to study ai in data analysis at your own pace. The platform pairs curated YouTube video lessons with an interactive AI tutor. You'll take a quick quiz after every lesson to test your knowledge. The learning content is completely free. If you want to share your new skills with employers, you can upgrade to an optional paid certificate once you finish.
Jobs you can target after learning AI For Data Analysis
AI For Data Analysis is a gateway skill for some of the fastest-growing roles in tech.
Analyzes complex datasets using machine learning algorithms to find business patterns.
Builds predictive models and neural networks to solve advanced business problems.
Designs and deploys automated clustering and anomaly detection systems.
Typical entry-level salary ranges in India as of August 2026. Actual compensation varies by city, company and experience.
Earn a AI For Data Analysis certificate employers recognise
Finish the course and claim a certificate you can share anywhere.
- Learn 100% free: every lesson, quiz and the AI tutor cost nothing
- Claim your certificate when you complete the course (an optional paid add-on)
- Recognised by 900+ hiring partners across India
- One click to share on LinkedIn and add to your resume
Why learners choose LearnTube
Real reviews from Google. You learn free, you get assessed free, and you pay only if you want the certificate.
Though I have 11 years of experience as an HRBP, the course helped me refresh my knowledge and gave me insights into new trends in the market. The modules were clear and easy to understand, and the quizzes were well designed. I have added the certificate to my LinkedIn profile.
I had a lot of doubts while learning, but the AI chatbot answered them in detail with simple summaries. The videos on the platform saved me a lot of time as I didn't have to search YouTube and watch random content.
Just earned my certificate in HR Management from LearnTube.ai! The platform is user-friendly and the assessment was a great way to validate my knowledge. Feeling proud of this accomplishment!
I enrolled in a LearnTube personalized course to get into a Data Analytics role. It helped me learn SQL in depth and gave me hands-on experience with Power BI. I attached the LearnTube certificate to my CV, it added credibility to my profile, and I'm currently in the interview process.
The interface is user-friendly and intuitive, making it easy to navigate the course. I encountered no errors during my time using the platform, which was a refreshing change from other online learning platforms.
Just cleared the Business Analyst assessment and got certified! Loved how the course simplifies big concepts like data-driven decision making into short, practical lessons. The automated quizzes kept me on track the whole time. Perfect boost for my resume!
The certificate gave me a real edge during my B.Tech. By the time I graduated, I had skills that set me apart from my peers, and the certification opened up more job opportunities. It gave me the confidence to stand out during interviews.
I manage many software projects as a General Manager and was looking for a good certification program to stay competitive. After researching many programs, I chose LearnTube and it was much better than I expected. The material is well-organized and practical, and what I liked most is the lifetime access.
Frequently asked questions
Everything learners ask before starting this AI For Data Analysis course.
Is this ai data analytics course free?
Yes, all 68 video lessons and AI-tutor quizzes are completely free to access. You only pay if you choose to add the optional verified certificate at the end of your studies.
How long does it take to finish the curriculum?
The entire program takes about 1 hours of watch time. You can learn at your own pace, so you might finish it in a single afternoon or spread it across a few days.
Can I study this ai for data analysis course on my phone?
Absolutely. LearnTube works on any mobile device or desktop browser. You can watch the curated videos, take the quizzes, and complete the modules wherever you have an internet connection.
What will I actually build during the classes?
You will complete four practical projects. These include building customer segmentation models for e-commerce, predictive maintenance systems for tech startups, and anomaly detection strategies for financial services firms.
Is the ai data analyst certificate worth getting?
Earning the optional paid certificate gives you a formal credential to share on LinkedIn or your resume. It helps prove to the 900+ hiring partners in our network that you understand neural networks and machine learning.
Are ai data analysts still in high demand?
Yes. Companies across India and globally need professionals who can use natural language processing and time series analysis to process large datasets. This skill set remains highly sought after in modern tech roles.
Do I need to attend at specific times?
No, the schedule is entirely self-paced. You log in whenever you want to watch the curated YouTube videos and answer the AI tutor questions, making it easy to fit around work or college.
Learners also explore these free courses
Pair AI For Data Analysis with a complementary skill to stand out even more.
Start learning AI For Data Analysis today, for free
68 lessons, an AI tutor that never gets tired, and a certificate at the finish line. All you need is an hour a day.
Start Learning Free