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DataScience & Gen AI - Learnees

DataScience & Gen AI

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The Data Science & Gen AI course provides a solid foundation in data analysis, machine learning, and modern generative AI techniques. Learners gain hands-on experience with real-world datasets, model building, and AI tools to develop intelligent, data-driven solutions. This course prepares you for industry-ready roles in data science and next-generation AI applications.

Trainer :

LiveOnline Classstarts on:

Upto First 3 session - 100% Refund

1. Introduction to Data Science

Topics:

Overview of data science: its applications
and the data science process.
Overview of data science: its applications
and the data science process.
Learning Outcome: Understand the basics of data science and its role in various industries.
Real-World Application: Learn how data science is used in personalized recommendations for e- commerce websites.

Hands-on:

Skills You Will Learn:

2. Data Collection and Preprocessing

Topics:

Techniques for collecting data from various sources (databases APIs web scraping)
Data cleaning and preprocessing methods including handling missing values outliers and data transformation.
Learning Outcome: Master data collection and preparation techniques.
Real-World Application: Learn how to clean and prepare customer data for marketing analysis.

Hands-on:

Skills You Will Learn:

3. Introduction to Python

Topics:

Introduction to the Python programming language including data types control flow functions and modules.
Learning Outcome: Become proficient in basic Python programming.
Learning Outcome: Become proficient in basic Python programming.

Hands-on:

Skills You Will Learn:

4. Data Manipulation with Pandas

Topics:

Using the Pandas library for data manipulation cleaning and analysis.
Working with DataFrames handling missing data and performing data aggregation.
Learning Outcome: Effectively manipulate and analyze data using Pandas.
Real-World Application: Analyze sales data to identify top-performing products and customer segments.

Hands-on:

Skills You Will Learn:

5. Data Visualization with Matplotlib and Seaborn

Topics:

Creating informative and visually appealing charts and graphs using Matplotlib and Seaborn.
Customizing plots exploring different visualization techniques and communicating data insights.
Learning Outcome: Visualize data effectively to identify trends and patterns.
Real-World Application: Create visualizations to present survey results to stakeholders.

Hands-on:

Skills You Will Learn:

6. Statistical Analysis

Topics:

Fundamentals of statistical analysis including descriptive statistics probability distributions hypothesis testing and regression analysis.
Learning Outcome: Apply statistical methods to analyze data and draw meaningful conclusions.
Real-World Application: Perform A/B testing to determine the effectiveness of different marketing campaigns.

Hands-on:

Skills You Will Learn:

7. SQL for Data Science

Topics:

Introduction to SQL for querying and manipulating data in relational databases.
Writing SQL queries to extract filter and aggregate data for analysis.
Learning Outcome: Retrieve and manipulate data from databases using SQL.
Real-World Application: Extract customer transaction data from a database for fraud detection analysis.

Hands-on:

Skills You Will Learn:

8. Machine Learning Fundamentals

Topics:

Overview of machine learning concepts including supervised learning unsupervised learning and reinforcement learning.
Introduction to common machine learning algorithms and their applications.
Learning Outcome: Understand the basics of machine learning and its different paradigms.
Real-World Application: Identify use cases for machine learning in various industries.

Hands-on:

Skills You Will Learn:

9. Linear Regression

Topics:

Building and evaluating linear regression models for predicting continuous variables.
Understanding model assumptions interpreting coefficients and assessing model performance.
Learning Outcome: Build and interpret linear regression models.
Real-World Application: Predict housing prices based on features like size location and amenities.

Hands-on:

Skills You Will Learn:

10. Decision Trees

Topics:

Introduction to decision trees including building visualizing and interpreting decision tree models.
Understanding concepts like entropy information gain and pruning.
Learning Outcome: Build and interpret decision tree models.
Real-World Application: Build a decision tree to classify loan applications as high-risk or low-risk.

Hands-on:

Skills You Will Learn:

11. Random Forests

Topics:

Module 12: Random Forests
Learning Outcome: Build and optimize random forest models.
Real-World Application: Predict customer purchase behavior using a random forest model.

Hands-on:

Skills You Will Learn:

12. Support Vector Machines (SVM)

Topics:

Introduction to Support Vector Machines (SVM) for classification regression tasks. Understanding kernels /margins/ and support vectors.
Learning Outcome: Understand and apply Support Vector Machines.
Real-World Application: Classify images using an SVM model.

Hands-on:

Skills You Will Learn:

13. K-Means Clustering

Topics:

Unsupervised learning with K-Means clustering. Understanding the K- Means algorithm choosing the optimal number of clusters and interpreting cluster results.
Learning Outcome: Apply K-Means clustering to segment data.
Real-World Application: Segment customers based on their purchasing patterns.

Hands-on:

Skills You Will Learn:

14. Dimensionality Reduction with PCA

Topics:

Reducing the dimensionality of data using Principal Component Analysis (PCA).
Understanding the PCA algorithm and interpreting the principal components.
Learning Outcome: Reduce the dimensionality of data using PCA.
Learning Outcome: Reduce the dimensionality of data using PCA.

Hands-on:

Skills You Will Learn:

15. Model Evaluation and Selection

Topics:

Metrics for evaluating machine learning models (accuracy precision recall F1-score AUC).
Techniques for model selection including cross- validation and hyperparameter tuning.
Learning Outcome: Evaluate and select the best machine learning model for a given task.
Real-World Application: Compare the performance of different models for predicting customer churn.

Hands-on:

Skills You Will Learn:

16. Natural Language Processing (NLP) Basics

Topics:

Introduction to Natural Language Processing (NLP).
Text preprocessing techniques sentiment analysis and topic modeling.
Learning Outcome: Understand the basics of NLP and its applications.
Real-World Application: Perform sentiment analysis on customer reviews to understand customer satisfaction.

Hands-on:

Skills You Will Learn:

17. Time Series Analysis

Topics:

Analyzing time series data. Time series decomposition forecasting techniques (ARIMA Exponential Smoothing).
Learning Outcome: Analyze and forecast time series data.
Real-World Application: Forecast sales for the next quarter based on historical sales data.

Hands-on:

Skills You Will Learn:

18. Big Data Technologies

Topics:

Introduction to Big Data technologies like Hadoop and Spark.
Understanding the challenges of big data and how these technologies address them.
Learning Outcome: Understand the basics of Big Data technologies.
Real-World Application: Process large datasets using Spark.

Hands-on:

Skills You Will Learn:

19. Cloud Computing for Data Science

Topics:

Using cloud platforms (AWS Azure GCP) for data science tasks. Deploying machine learning models on the cloud.
Learning Outcome: Deploy data science solutions on the cloud.
Real-World Application: Deploy a machine learning model on AWS SageMaker.

Hands-on:

Skills You Will Learn:

20. Data Ethics and Privacy

Topics:

Ethical considerations in data science. Data privacy regulations (GDPR CCPA). Bias in machine learning models.
Learning Outcome: Understand ethical considerations in data science.
Real-World Application: Identify and mitigate bias in a machine learning model.

Hands-on:

Skills You Will Learn:

21. Data Storytelling and Communication

Topics:

Communicating data insights effectively. Creating data visualizations for presentations.
Writing data-driven reports.
Real-World Application: Present data findings to a non-technical audience.

Hands-on:

Skills You Will Learn:

22. Generative AI & Prompt Engineering

Topics:

Learn the fundamentals of Generative AI and Large Language Models like ChatGPT and Gemini and explore how they can be used to build intelligent text-based applications.
This module covers prompt engineering techniques/ hands-on usage of OpenAI APIs/ and ethical considerations like hallucinations and bias.
Learning Outcome: Understand and apply Gen-AI tools to real tasks with confidence.
Real-World Application: Build a mini-project such as an AI-powered Resume Generator/ FAQ Bot/ or Content Assistant using prompts and APIs.

Hands-on:

Skills You Will Learn:

23. Building a Data Science Portfolio & Job Search Strategies

Topics:

Creating a portfolio of data science projects to showcase your skills.
Building a personal website or GitHub repository .Strategies for finding data science jobs.
Resume writing/ interview preparation/ and networking.
Learning Outcome: Prepare for a data science job search.
Real-World Application: Practice answering common data science interview questions.

Hands-on:

Skills You Will Learn:

24. Capstone Project

Topics:

A comprehensive data science project that integrates all the skills and knowledge learned throughout the course.
Learning Outcome: Apply data science skills to solve a real-world problem.
Real-World Application: Complete a capstone project that can be showcased to potential employers.

Hands-on:

Skills You Will Learn:

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Dur: 26 weeks

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Class Type: Weekday

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Upto First 3 session - 100% Refund

Why Choose DataScience & Gen AI?

Why enroll for DataScience & Gen AI Certification?

Unlock your potential in cloud computing with DataScience & Gen AI certification

Most Popular Certification

DataScience & Gen AI certification is rated as one of the most popular and lucrative cloud certifications in IT globally.

Growing Market

The global DataScience & Gen AI market size is expected to grow at a CAGR of 21.2% from 2024 to 2030.

High Salary Potential

The estimated total pay for an DataScience & Gen AI is ₹7,09,000 per year, with an average salary of ₹6,09,000 per year - Glassdoor.

Python programming

Data analysis

Machine learning

Statistics

Natural language processing

Prompt engineering

Data visualization

SQL

Cloud computing

Curriculum Designed by Experts

1. Introduction to Data Science

Topics:

Overview of data science: its applications
and the data science process.
Overview of data science: its applications
and the data science process.
Learning Outcome: Understand the basics of data science and its role in various industries.
Real-World Application: Learn how data science is used in personalized recommendations for e- commerce websites.

2. Data Collection and Preprocessing

3. Introduction to Python

4. Data Manipulation with Pandas

5. Data Visualization with Matplotlib and Seaborn

6. Statistical Analysis

7. SQL for Data Science

8. Machine Learning Fundamentals

9. Linear Regression

10. Decision Trees

11. Random Forests

12. Support Vector Machines (SVM)

13. K-Means Clustering

14. Dimensionality Reduction with PCA

15. Model Evaluation and Selection

16. Natural Language Processing (NLP) Basics

17. Time Series Analysis

18. Big Data Technologies

19. Cloud Computing for Data Science

20. Data Ethics and Privacy

21. Data Storytelling and Communication

22. Generative AI & Prompt Engineering

23. Building a Data Science Portfolio & Job Search Strategies

24. Capstone Project

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We are happy to help you 24/7

1. Introduction to Data Science

Topics:

Overview of data science: its applications
and the data science process.
Overview of data science: its applications
and the data science process.
Learning Outcome: Understand the basics of data science and its role in various industries.
Real-World Application: Learn how data science is used in personalized recommendations for e- commerce websites.

Hands-on:

Skills You Will Learn:

2. Data Collection and Preprocessing

Topics:

Techniques for collecting data from various sources (databases APIs web scraping)
Data cleaning and preprocessing methods including handling missing values outliers and data transformation.
Learning Outcome: Master data collection and preparation techniques.
Real-World Application: Learn how to clean and prepare customer data for marketing analysis.

Hands-on:

Skills You Will Learn:

3. Introduction to Python

Topics:

Introduction to the Python programming language including data types control flow functions and modules.
Learning Outcome: Become proficient in basic Python programming.
Learning Outcome: Become proficient in basic Python programming.

Hands-on:

Skills You Will Learn:

4. Data Manipulation with Pandas

Topics:

Using the Pandas library for data manipulation cleaning and analysis.
Working with DataFrames handling missing data and performing data aggregation.
Learning Outcome: Effectively manipulate and analyze data using Pandas.
Real-World Application: Analyze sales data to identify top-performing products and customer segments.

Hands-on:

Skills You Will Learn:

5. Data Visualization with Matplotlib and Seaborn

Topics:

Creating informative and visually appealing charts and graphs using Matplotlib and Seaborn.
Customizing plots exploring different visualization techniques and communicating data insights.
Learning Outcome: Visualize data effectively to identify trends and patterns.
Real-World Application: Create visualizations to present survey results to stakeholders.

Hands-on:

Skills You Will Learn:

6. Statistical Analysis

Topics:

Fundamentals of statistical analysis including descriptive statistics probability distributions hypothesis testing and regression analysis.
Learning Outcome: Apply statistical methods to analyze data and draw meaningful conclusions.
Real-World Application: Perform A/B testing to determine the effectiveness of different marketing campaigns.

Hands-on:

Skills You Will Learn:

7. SQL for Data Science

Topics:

Introduction to SQL for querying and manipulating data in relational databases.
Writing SQL queries to extract filter and aggregate data for analysis.
Learning Outcome: Retrieve and manipulate data from databases using SQL.
Real-World Application: Extract customer transaction data from a database for fraud detection analysis.

Hands-on:

Skills You Will Learn:

8. Machine Learning Fundamentals

Topics:

Overview of machine learning concepts including supervised learning unsupervised learning and reinforcement learning.
Introduction to common machine learning algorithms and their applications.
Learning Outcome: Understand the basics of machine learning and its different paradigms.
Real-World Application: Identify use cases for machine learning in various industries.

Hands-on:

Skills You Will Learn:

9. Linear Regression

Topics:

Building and evaluating linear regression models for predicting continuous variables.
Understanding model assumptions interpreting coefficients and assessing model performance.
Learning Outcome: Build and interpret linear regression models.
Real-World Application: Predict housing prices based on features like size location and amenities.

Hands-on:

Skills You Will Learn:

10. Decision Trees

Topics:

Introduction to decision trees including building visualizing and interpreting decision tree models.
Understanding concepts like entropy information gain and pruning.
Learning Outcome: Build and interpret decision tree models.
Real-World Application: Build a decision tree to classify loan applications as high-risk or low-risk.

Hands-on:

Skills You Will Learn:

11. Random Forests

Topics:

Module 12: Random Forests
Learning Outcome: Build and optimize random forest models.
Real-World Application: Predict customer purchase behavior using a random forest model.

Hands-on:

Skills You Will Learn:

12. Support Vector Machines (SVM)

Topics:

Introduction to Support Vector Machines (SVM) for classification regression tasks. Understanding kernels /margins/ and support vectors.
Learning Outcome: Understand and apply Support Vector Machines.
Real-World Application: Classify images using an SVM model.

Hands-on:

Skills You Will Learn:

13. K-Means Clustering

Topics:

Unsupervised learning with K-Means clustering. Understanding the K- Means algorithm choosing the optimal number of clusters and interpreting cluster results.
Learning Outcome: Apply K-Means clustering to segment data.
Real-World Application: Segment customers based on their purchasing patterns.

Hands-on:

Skills You Will Learn:

14. Dimensionality Reduction with PCA

Topics:

Reducing the dimensionality of data using Principal Component Analysis (PCA).
Understanding the PCA algorithm and interpreting the principal components.
Learning Outcome: Reduce the dimensionality of data using PCA.
Learning Outcome: Reduce the dimensionality of data using PCA.

Hands-on:

Skills You Will Learn:

15. Model Evaluation and Selection

Topics:

Metrics for evaluating machine learning models (accuracy precision recall F1-score AUC).
Techniques for model selection including cross- validation and hyperparameter tuning.
Learning Outcome: Evaluate and select the best machine learning model for a given task.
Real-World Application: Compare the performance of different models for predicting customer churn.

Hands-on:

Skills You Will Learn:

16. Natural Language Processing (NLP) Basics

Topics:

Introduction to Natural Language Processing (NLP).
Text preprocessing techniques sentiment analysis and topic modeling.
Learning Outcome: Understand the basics of NLP and its applications.
Real-World Application: Perform sentiment analysis on customer reviews to understand customer satisfaction.

Hands-on:

Skills You Will Learn:

17. Time Series Analysis

Topics:

Analyzing time series data. Time series decomposition forecasting techniques (ARIMA Exponential Smoothing).
Learning Outcome: Analyze and forecast time series data.
Real-World Application: Forecast sales for the next quarter based on historical sales data.

Hands-on:

Skills You Will Learn:

18. Big Data Technologies

Topics:

Introduction to Big Data technologies like Hadoop and Spark.
Understanding the challenges of big data and how these technologies address them.
Learning Outcome: Understand the basics of Big Data technologies.
Real-World Application: Process large datasets using Spark.

Hands-on:

Skills You Will Learn:

19. Cloud Computing for Data Science

Topics:

Using cloud platforms (AWS Azure GCP) for data science tasks. Deploying machine learning models on the cloud.
Learning Outcome: Deploy data science solutions on the cloud.
Real-World Application: Deploy a machine learning model on AWS SageMaker.

Hands-on:

Skills You Will Learn:

20. Data Ethics and Privacy

Topics:

Ethical considerations in data science. Data privacy regulations (GDPR CCPA). Bias in machine learning models.
Learning Outcome: Understand ethical considerations in data science.
Real-World Application: Identify and mitigate bias in a machine learning model.

Hands-on:

Skills You Will Learn:

21. Data Storytelling and Communication

Topics:

Communicating data insights effectively. Creating data visualizations for presentations.
Writing data-driven reports.
Real-World Application: Present data findings to a non-technical audience.

Hands-on:

Skills You Will Learn:

22. Generative AI & Prompt Engineering

Topics:

Learn the fundamentals of Generative AI and Large Language Models like ChatGPT and Gemini and explore how they can be used to build intelligent text-based applications.
This module covers prompt engineering techniques/ hands-on usage of OpenAI APIs/ and ethical considerations like hallucinations and bias.
Learning Outcome: Understand and apply Gen-AI tools to real tasks with confidence.
Real-World Application: Build a mini-project such as an AI-powered Resume Generator/ FAQ Bot/ or Content Assistant using prompts and APIs.

Hands-on:

Skills You Will Learn:

23. Building a Data Science Portfolio & Job Search Strategies

Topics:

Creating a portfolio of data science projects to showcase your skills.
Building a personal website or GitHub repository .Strategies for finding data science jobs.
Resume writing/ interview preparation/ and networking.
Learning Outcome: Prepare for a data science job search.
Real-World Application: Practice answering common data science interview questions.

Hands-on:

Skills You Will Learn:

24. Capstone Project

Topics:

A comprehensive data science project that integrates all the skills and knowledge learned throughout the course.
Learning Outcome: Apply data science skills to solve a real-world problem.
Real-World Application: Complete a capstone project that can be showcased to potential employers.

Hands-on:

Skills You Will Learn:

Career Opportunities

Job roles are being prepared for this course. Please check back later.

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Industry-Relevant Projects
Course Demo Dataset & Files
Quizzes & Assignments

Industry Recognised Certification

Learnees Training Certificate
Graded Performance Certificate
Certificate of Completion

Course Overview

Everything you need to know about DataScience & Gen AI

The Data Science & Generative AI course equips learners with the skills to collect, analyze, and interpret data while leveraging modern AI techniques to build intelligent, automated solutions. It provides hands-on experience with machine learning, deep learning, and generative models, enabling students to apply data-driven insights and create innovative AI applications across real-world scenarios.