Master in Data Analytics & Machine Learning
Focus on expertise & impact -
Next Cohort
Course Duration
440 Hrs
Course Overview
Master in Data Analytics & Machine Learning program is a career-oriented certification course that blends the essential building blocks of modern data-driven decision-making with advanced machine learning techniques. Designed for graduates, professionals, and aspiring data scientists, this program develops job-ready expertise in data preparation, visualization, statistical modeling, and predictive analytics using Python, Excel, SQL, Power BI, Tableau, and key machine learning frameworks.
Key Features
- End-to-end coverage: From data analysis to machine learning deployment
- Taught by experienced industry practitioners
- Career-focused structure for analysts, engineers, and data scientists
- Hands-on capstone project with real-world dataset
Skills Covered
- Python-based data wrangling and analysis
- Hands-on with BI tools (Excel, Power BI, Tableau)
- Feature Engineering & Model Tuning
- Time Series Forecasting
- Predictive Modeling & Deployment
- Data Cleaning & Preprocessing
Course Curriculum
Rooman IT Ignite
Module 1 - Python Programming
- Introduction to Python and Basic Syntax Understand Python installation, syntax rules, and writing your first program.
- Control Structures Learn decision-making (if/else), loops (for, while), and flow control techniques.
- Functions Define and call functions, understand parameters, return values, and scope.
- Data Structures Work with lists, tuples, dictionaries, and sets for real-world data manipulation.
- File Handling Read, write, and manage text and CSV files using Python.
- Modules & Libraries Import built-in and custom modules; explore popular libraries like math, datetime, etc.
- Exception Handling Handle errors gracefully using try-except blocks, raising exceptions, and best practices.
- Object-Oriented Programming Learn classes, objects, inheritance, polymorphism, and encapsulation in Python.
- Python programming with Databases Connect to databases (e.g., SQLite, MySQL), perform CRUD operations using Python.
- Hands-On Practice and Hands On Guided Project Apply concepts through coding exercises, quizzes, and a mentor-led capstone project.
Module 2 - Networking Essentials
- Introduction to Networking Understand the basics of computer networks, types (LAN, WAN, MAN), and networking devices like routers, switches, hubs, etc.
- TCP/IP Model and IP Addressing Explore the layers of the TCP/IP model and learn about IPv4/IPv6 addressing schemes and classes.
- Subnetting and DHCP Learn how to calculate subnets, subnet masks, and understand how DHCP automates IP assignment.
- Application Layer Protocol Study key protocols like HTTP, FTP, DNS, SMTP, and how they function within the TCP/IP model.
- Network Troubleshooting and Basic Security Get introduced to common network issues, diagnostic tools (ping, traceroute), and foundational security practices like firewalls, antivirus, and access control.
- Introduction to OSI Layer Learn the 7-layer OSI model that standardizes network communication from physical to application level.
- Transmission Medium Explore different mediums like cables, fiber optics, and wireless used to transmit data across networks.
- Topology Study network layouts such as star, bus, ring, and mesh and how they affect performance and reliability.
- Ranges of Classes Explore IP address classes (A, B, C, etc.) and their usage based on the size and scope of networks.
- Wireless Technology Learn how Wi-Fi and other wireless standards work to enable cable-free connectivity.
- IPv4 Study the widely used 32-bit IP addressing system and its limitations in modern networking.
- IPv6 Understand the advanced 128-bit addressing system designed to replace IPv4 with greater scalability.
Module 3 - Linux Basics
- Introduction of Linux
- Linux Graphical User Interface
- Accessing the Command Lines
- Working with Files Managing Local Users and Groups
- Controlling Access to Files
- Monitoring and Managing Linux Processes
- Managing Software Packages
- Managing Basic Storage
- Managing Logical Volumes
- Controlling the Boot Process
- Managing Network settings
- Linux Fundamentals and Basic Administration
- System Administration
- Security and Scripting
- RHCA Specific Topics and Introduction to Cloud Computing
Module 4 - Cyber Security
- Intro to Cyber Security Understand the core principles of cybersecurity, its importance, and key concepts like CIA Triad (Confidentiality, Integrity, Availability).
- Cyber Attacks Learn about common threats including malware, phishing, DoS/DDoS attacks, ransomware, and social engineering.
- Security Devices (Firewall, IDS, IPS) Explore how firewalls, Intrusion Detection Systems (IDS), and Intrusion Prevention Systems (IPS) protect networks and systems.
- OS Hardening Secure your OS through user account controls, patch management, disabling unnecessary services, and enforcing security policies.
- Web & Application Security Basics Gain foundational knowledge on securing web applications, understanding vulnerabilities like XSS, SQL injection, and the OWASP Top 10.
Module 5 - Introduction to Cloud Computing
- Introduction to Cloud Computing Learn the fundamentals of cloud computing, deployment models (Public, Private, Hybrid), and service models (IaaS, PaaS, SaaS).
- Core Cloud Services Explore key services such as compute (EC2), storage (S3), networking (VPC), and databases (RDS) offered by major cloud providers like AWS, Azure, or GCP.
- Introduction to Cloud DevOps Understand the principles of DevOps, CI/CD pipelines, Infrastructure as Code (IaC), and automation tools like Docker, Jenkins, and Terraform.
- Advanced Cloud Computing Dive deeper into cloud architecture, security best practices, monitoring, load balancing, auto-scaling, and multi-cloud strategies.
- Hands-on Guided Project Work on real-world projects simulating production environments, with mentorship on choosing roles like Cloud Engineer, DevOps Engineer, or SRE.
- Career Pathways and Hands-On Labs Practice skills in real cloud environments using sandboxed labs for deploying, securing, and managing cloud infrastructure.
Module 6 - Data Analytics
- Introduction to Data Analysis and Tools Learn the fundamentals of data analytics, its applications, and key tools used by professionals (Excel, SQL, Power BI, Python, etc.).
- Data Cleaning and Preprocessing Understand how to handle missing data, remove duplicates, normalize formats, and prepare raw data for analysis.
- Advanced Excel Master pivot tables, advanced formulas, charts, lookups, and data modeling techniques for actionable insights.
- Power BI Build interactive dashboards, create visual reports, and connect to multiple data sources for dynamic business intelligence.
Module 7 - Gen AI Tools and Usage
- Introduction to GenAI & Prompt Engineering Understand the fundamentals of Generative AI, including how large language models (LLMs) like GPT work. Learn the basics of crafting effective prompts to guide AI responses for different tasks.
- ChatGPT for Learning Explore how to use ChatGPT as a smart learning companion — for explaining concepts, summarizing topics, solving doubts, and preparing for interviews or assignments.
- Coding & Research Leverage GenAI tools to write, debug, and understand code more efficiently. Discover how AI can assist in research, documentation, and rapid prototyping.
- GitHub Copilot & Code Assistants Gain hands-on experience with tools like GitHub Copilot, Tabnine, and other AI-powered code assistants to boost productivity, write better code, and speed up development
- Prompt Engineering Deep Dive for Coding Master advanced prompt techniques specifically tailored for coding tasks — such as generating algorithms, refactoring code, translating languages, or writing test cases effectively with AI tools.
- Introduction to Python and Basic Syntax Understand Python installation, syntax rules, and writing your first program.
- Control Structures Learn decision-making (if/else), loops (for, while), and flow control techniques.
- Functions Define and call functions, understand parameters, return values, and scope.
- Data Structures Work with lists, tuples, dictionaries, and sets for real-world data manipulation.
- File Handling Read, write, and manage text and CSV files using Python.
- Modules & Libraries Import built-in and custom modules; explore popular libraries like math, datetime, etc.
- Exception Handling Handle errors gracefully using try-except blocks, raising exceptions, and best practices.
- Object-Oriented Programming Learn classes, objects, inheritance, polymorphism, and encapsulation in Python.
- Python programming with Databases Connect to databases (e.g., SQLite, MySQL), perform CRUD operations using Python.
- Hands-On Practice and Hands On Guided Project Apply concepts through coding exercises, quizzes, and a mentor-led capstone project.
- Introduction to Networking Understand the basics of computer networks, types (LAN, WAN, MAN), and networking devices like routers, switches, hubs, etc.
- TCP/IP Model and IP Addressing Explore the layers of the TCP/IP model and learn about IPv4/IPv6 addressing schemes and classes.
- Subnetting and DHCP Learn how to calculate subnets, subnet masks, and understand how DHCP automates IP assignment.
- Application Layer Protocol Study key protocols like HTTP, FTP, DNS, SMTP, and how they function within the TCP/IP model.
- Network Troubleshooting and Basic Security Get introduced to common network issues, diagnostic tools (ping, traceroute), and foundational security practices like firewalls, antivirus, and access control.
- Introduction to OSI Layer Learn the 7-layer OSI model that standardizes network communication from physical to application level.
- Transmission Medium Explore different mediums like cables, fiber optics, and wireless used to transmit data across networks.
- Topology Study network layouts such as star, bus, ring, and mesh and how they affect performance and reliability.
- Ranges of Classes Explore IP address classes (A, B, C, etc.) and their usage based on the size and scope of networks.
- Wireless Technology Learn how Wi-Fi and other wireless standards work to enable cable-free connectivity.
- IPv4 Study the widely used 32-bit IP addressing system and its limitations in modern networking.
- IPv6 Understand the advanced 128-bit addressing system designed to replace IPv4 with greater scalability.
- Introduction to Linux and Basic Commands Learn Linux architecture, terminal usage, and essential commands for navigation, file handling, and system interaction.
- User and File Permissions Management Understand Linux user roles, groups, and permission settings (read, write, execute) using chmod, chown, and usermod.
- Process Management and Shell Scripting Basics Manage processes using ps, top, kill; write basic shell scripts for automation and task scheduling.
- Package Management and Disk Management Install, update, and remove software using package managers like apt, yum; manage partitions and disk space using df, du, and fdisk.
- Networking Basics and System Monitoring Configure IP settings, check connectivity, and monitor system performance using tools like netstat, ping, vmstat, and htop.
- Intro to Cyber Security Understand the core principles of cybersecurity, its importance, and key concepts like CIA Triad (Confidentiality, Integrity, Availability).
- Cyber Attacks Learn about common threats including malware, phishing, DoS/DDoS attacks, ransomware, and social engineering.
- Security Devices (Firewall, IDS, IPS) Explore how firewalls, Intrusion Detection Systems (IDS), and Intrusion Prevention Systems (IPS) protect networks and systems.
- OS Hardening Secure your OS through user account controls, patch management, disabling unnecessary services, and enforcing security policies.
- Web & Application Security Basics Gain foundational knowledge on securing web applications, understanding vulnerabilities like XSS, SQL injection, and the OWASP Top 10.
- Introduction to Cloud Computing Learn the fundamentals of cloud computing, deployment models (Public, Private, Hybrid), and service models (IaaS, PaaS, SaaS).
- Core Cloud Services Explore key services such as compute (EC2), storage (S3), networking (VPC), and databases (RDS) offered by major cloud providers like AWS, Azure, or GCP.
- Introduction to Cloud DevOps Understand the principles of DevOps, CI/CD pipelines, Infrastructure as Code (IaC), and automation tools like Docker, Jenkins, and Terraform.
- Advanced Cloud Computing Dive deeper into cloud architecture, security best practices, monitoring, load balancing, auto-scaling, and multi-cloud strategies.
- Hands-on Guided Project Work on real-world projects simulating production environments, with mentorship on choosing roles like Cloud Engineer, DevOps Engineer, or SRE.
- Career Pathways and Hands-On Labs Practice skills in real cloud environments using sandboxed labs for deploying, securing, and managing cloud infrastructure.
- Introduction to Data Analysis and Tools Learn the fundamentals of data analytics, its applications, and key tools used by professionals (Excel, SQL, Power BI, Python, etc.).
- Data Cleaning and Preprocessing Understand how to handle missing data, remove duplicates, normalize formats, and prepare raw data for analysis.
- Advanced Excel Master pivot tables, advanced formulas, charts, lookups, and data modeling techniques for actionable insights.
- Power BI Build interactive dashboards, create visual reports, and connect to multiple data sources for dynamic business intelligence.
- Introduction to GenAI & Prompt Engineering Understand the fundamentals of Generative AI, including how large language models (LLMs) like GPT work. Learn the basics of crafting effective prompts to guide AI responses for different tasks.
- ChatGPT for Learning Explore how to use ChatGPT as a smart learning companion — for explaining concepts, summarizing topics, solving doubts, and preparing for interviews or assignments.
- Coding & Research Leverage GenAI tools to write, debug, and understand code more efficiently. Discover how AI can assist in research, documentation, and rapid prototyping.
- GitHub Copilot & Code Assistants Gain hands-on experience with tools like GitHub Copilot, Tabnine, and other AI-powered code assistants to boost productivity, write better code, and speed up development
- Prompt Engineering Deep Dive for Coding Master advanced prompt techniques specifically tailored for coding tasks — such as generating algorithms, refactoring code, translating languages, or writing test cases effectively with AI tools.
Data Analytics
Module 1 - Python for Data Science
- Python Learn the fundamentals of Python including data types, loops, functions, and OOP concepts. Build a strong programming base for data analysis and automation tasks.
- NumPy Master numerical operations using NumPy arrays, broadcasting, and vectorization. Essential for high-speed mathematical computing in data science.
- Pandas Analyze and manipulate structured data with DataFrames and Series. Perform filtering, grouping, merging, and reshaping of large datasets.
- EDA Uncover patterns and trends using descriptive statistics and summary visuals. Learn to derive insights and prepare data for modeling.
- Data Cleaning & Preprocessing Handle missing values, duplicates, and inconsistent data. Apply transformations like scaling, encoding, and normalization for clean datasets.
- Data Visualization (Matplotlib & Seaborn) Create impactful visualizations like bar charts, histograms, and heatmaps. Tell compelling data stories using customizable plotting tools.
- Web scraping Extract data from websites using BeautifulSoup, Requests, and Selenium. Learn DOM parsing, handling dynamic content, and data exporting.
Module 2 - Database Basics
- SQL (Joins, Triggers, ER models) Learn to write efficient queries, use joins to combine data across tables, and create ER models to design relational schemas. Automate tasks using triggers for event-based database operations.
- NoSQL (MongoDB) Explore MongoDB’s flexible, document-oriented data model. Perform CRUD operations and manage unstructured data using collections and dynamic schemas.
- Normalization Understand how to organize data in relational databases by eliminating redundancy. Learn 1NF, 2NF, 3NF, and beyond to optimize database structure.
Module 3 - Data Analytics with Excel & Power BI
- Excel (formulas, charts, pivots) Master Excel for data organization, calculations, and reporting. Learn formulas, data visualization with charts, and dynamic analysis using pivot tables.
- Power BI (DAX, dashboards, transformations) Create interactive dashboards and reports in Power BI. Use DAX for custom calculations and Power Query for data cleaning and transformation.
Module 4 - BI Tools Overview
- Tableau (core to advanced) Learn to build interactive dashboards, charts, and data stories using Tableau. Advance to calculated fields, parameters, level of detail (LOD) expressions, and performance optimization.
- Cognos or QlikView overview Get an overview of enterprise BI tools like Cognos or QlikView. Understand their interface, reporting features, and how they compare with modern self-service BI platforms.
- Python Learn the fundamentals of Python including data types, loops, functions, and OOP concepts. Build a strong programming base for data analysis and automation tasks.
- NumPy Master numerical operations using NumPy arrays, broadcasting, and vectorization. Essential for high-speed mathematical computing in data science.
- Pandas Analyze and manipulate structured data with DataFrames and Series. Perform filtering, grouping, merging, and reshaping of large datasets.
- EDA Uncover patterns and trends using descriptive statistics and summary visuals. Learn to derive insights and prepare data for modeling.
- Data Cleaning & Preprocessing Handle missing values, duplicates, and inconsistent data. Apply transformations like scaling, encoding, and normalization for clean datasets.
- Data Visualization (Matplotlib & Seaborn) Create impactful visualizations like bar charts, histograms, and heatmaps. Tell compelling data stories using customizable plotting tools.
- Web scraping Extract data from websites using BeautifulSoup, Requests, and Selenium. Learn DOM parsing, handling dynamic content, and data exporting.
- SQL (Joins, Triggers, ER models) Learn to write efficient queries, use joins to combine data across tables, and create ER models to design relational schemas. Automate tasks using triggers for event-based database operations.
- NoSQL (MongoDB) Explore MongoDB’s flexible, document-oriented data model. Perform CRUD operations and manage unstructured data using collections and dynamic schemas.
- Normalization Understand how to organize data in relational databases by eliminating redundancy. Learn 1NF, 2NF, 3NF, and beyond to optimize database structure.
- Excel (formulas, charts, pivots) Master Excel for data organization, calculations, and reporting. Learn formulas, data visualization with charts, and dynamic analysis using pivot tables.
- Power BI (DAX, dashboards, transformations) Create interactive dashboards and reports in Power BI. Use DAX for custom calculations and Power Query for data cleaning and transformation.
- Tableau (core to advanced) Learn to build interactive dashboards, charts, and data stories using Tableau. Advance to calculated fields, parameters, level of detail (LOD) expressions, and performance optimization.
- Cognos or QlikView overview Get an overview of enterprise BI tools like Cognos or QlikView. Understand their interface, reporting features, and how they compare with modern self-service BI platforms.
Machine Learning & Deep Learning
Module 1 - Mathematics & Statistics for Machine Learning
- Hypothesis Testing Understand how to make data-driven decisions using statistical hypothesis testing.Learn concepts like null/alternative hypotheses and significance levels.
- Chi-Square Testing Use the chi-square test to assess relationships between categorical variables. Helpful in feature selection and A/B testing.
- IQR(Interquartile Range) Analyze data spread and detect outliers using IQR. Learn how to visualize distribution with box plots.
- Range and central tendency Explore basic statistics like mean, median, mode, and range. Build a foundation for analyzing data distribution and variability.
- P-value Learn how to interpret p-values to determine statistical significance in hypothesis testing. Essential for model validation.
- Linear Algebra Understand vectors, matrices, and transformations—core mathematical tools behind ML models, especially deep learning.
- Calculus Learn basic derivatives and gradients used in model optimization and backpropagation in machine learning.
- Matrices Explore matrix operations like multiplication, inversion, and eigenvalues—key to representing and computing in ML algorithms.
Module 2 - Machine Learning Fundamentals
- Supervised learning (Regression, Classification) Learn to train models on labeled data to predict outcomes using regression and classification techniques. Understand real-world use cases like price prediction and sentiment analysis.
- Scikit-learn Use Python’s most popular ML library to build, train, and evaluate models. Learn tools for preprocessing, model selection, and performance evaluation.
- Model metrics Evaluate model performance using metrics like accuracy, precision, recall, F1-score, and RMSE. Choose the right metric based on problem type.
- Unsupervised Explore clustering and dimensionality reduction techniques like K-Means and PCA. Discover hidden patterns in data without predefined labels.
- Reinforcement machine learning Understand how agents learn optimal actions through rewards and penalties. Apply concepts like Q-learning and Markov Decision Processes.
- Feature engineering Transform raw data into meaningful features that boost model accuracy. Learn encoding, scaling, interaction features, and more.
- Ensemble models Combine multiple models to improve accuracy using methods like bagging, boosting, and stacking. Explore Random Forest, XGBoost, and others.
- Pipelines Build reusable ML workflows with Scikit-learn Pipelines. Chain together preprocessing and modeling steps for clean, efficient code.
- Cross-validation Improve model generalization by validating it on different data subsets. Learn k-fold cross-validation and train-test-split strategies.
Module 3 - Advanced Machine Learning
- XGBoost Learn XGBoost, a powerful gradient boosting algorithm known for speed and accuracy. Widely used in ML competitions and real-world applications.
- CatBoost Explore CatBoost, a gradient boosting library optimized for categorical features. Efficient, scalable, and less sensitive to parameter tuning.
- Hyperparameter tuning Improve model performance by optimizing hyperparameters using techniques like Grid Search and Random Search with cross-validation.
- Model interpretability Understand how models make predictions using tools like SHAP, LIME, and feature importance. Essential for building trust in ML decisions.
Module 4 - Time Series Forecasting & Analysis
- Trend/seasonality Identify long-term trends and recurring seasonal patterns in time series data. Essential for accurate forecasting and business planning.
- Moving average Smooth out short-term fluctuations to reveal trends using rolling averages. Used in signal processing and stock market analysis.
- ARIMA (AutoRegressive Integrated Moving Average) Model time series data with trends and noise using ARIMA. Combines autoregression, differencing, and moving averages for powerful forecasting.
- SARIMA(Seasonal ARIMA) Extend ARIMA to handle seasonal patterns explicitly. Ideal for monthly, quarterly, or cyclic data with repeating trends.
- Prophet(by Meta) Use Facebook Prophet for quick, flexible time series forecasting. Handles seasonality, holidays, and missing data with ease.
- Seasonality decomposition Break down time series into trend, seasonality, and residual components. Helps visualize and analyze each influence separately.
Module 5 - Deep Learning Applications
- Image classification Train deep learning models to recognize and categorize images using CNNs (Convolutional Neural Networks). Applied in areas like facial recognition and medical imaging.
- Sentiment analysis Use NLP and deep learning to determine the sentiment (positive, negative, neutral) behind textual data such as reviews, tweets, or feedback.
- Tabular DL models Apply deep learning techniques to structured/tabular data using models like TabNet. Optimize performance on datasets typical in business and finance.
- Transfer learning Leverage pre-trained models to accelerate training and improve accuracy on smaller datasets. Useful in computer vision and NLP tasks.
- Batch norm Improve training speed and stability by normalizing inputs across layers. Helps reduce internal covariate shift in deep networks.
- Dropout Prevent overfitting by randomly disabling neurons during training. Enhances generalization by promoting model robustness.
- Optimizers Learn how optimizers like SGD, Adam, and RMSProp adjust weights during training to minimize loss and improve convergence.
- Loss functions Measure model prediction error using functions like Cross-Entropy or MSE. A crucial part of training and guiding model learning.
Module 6 - Neural Networks
- Neurons Understand the basic computational unit of neural networks, inspired by the human brain. Learn how neurons process inputs to produce activations.
- Perceptron Explore the simplest type of neural network used for binary classification. Learn how perceptrons form the basis for deeper architectures.
- Activation functions Use functions like ReLU, Sigmoid, and Tanh to introduce non-linearity in models. Crucial for learning complex patterns in data.
- Gradient descent Master the optimization algorithm that minimizes model error by adjusting weights. Understand learning rate, cost function, and convergence.
- Convolutional Neural Networks (CNNs) Build models for image processing using convolutional layers, pooling, and filters. Widely used in visual recognition tasks.
- Recurrent Neural Networks (RNNs) Basics Understand how RNNs handle sequential data like text or time series. Learn about loops, memory, and limitations like vanishing gradients.
- TensorFlow/PyTorch setup Set up the two most widely used deep learning frameworks. Learn environment setup, GPU use, and writing your first model.
- Hypothesis Testing Understand how to make data-driven decisions using statistical hypothesis testing.Learn concepts like null/alternative hypotheses and significance levels.
- Chi-Square Testing Use the chi-square test to assess relationships between categorical variables. Helpful in feature selection and A/B testing.
- IQR(Interquartile Range) Analyze data spread and detect outliers using IQR. Learn how to visualize distribution with box plots.
- Range and central tendency Explore basic statistics like mean, median, mode, and range. Build a foundation for analyzing data distribution and variability.
- P-value Learn how to interpret p-values to determine statistical significance in hypothesis testing. Essential for model validation.
- Linear Algebra Understand vectors, matrices, and transformations—core mathematical tools behind ML models, especially deep learning.
- Calculus Learn basic derivatives and gradients used in model optimization and backpropagation in machine learning.
- Matrices Explore matrix operations like multiplication, inversion, and eigenvalues—key to representing and computing in ML algorithms.
- Supervised learning (Regression, Classification) Learn to train models on labeled data to predict outcomes using regression and classification techniques. Understand real-world use cases like price prediction and sentiment analysis.
- Scikit-learn Use Python’s most popular ML library to build, train, and evaluate models. Learn tools for preprocessing, model selection, and performance evaluation.
- Model metrics Evaluate model performance using metrics like accuracy, precision, recall, F1-score, and RMSE. Choose the right metric based on problem type.
- Unsupervised Explore clustering and dimensionality reduction techniques like K-Means and PCA. Discover hidden patterns in data without predefined labels.
- Reinforcement machine learning Understand how agents learn optimal actions through rewards and penalties. Apply concepts like Q-learning and Markov Decision Processes.
- Feature engineering Transform raw data into meaningful features that boost model accuracy. Learn encoding, scaling, interaction features, and more.
- Ensemble models Combine multiple models to improve accuracy using methods like bagging, boosting, and stacking. Explore Random Forest, XGBoost, and others.
- Pipelines Build reusable ML workflows with Scikit-learn Pipelines. Chain together preprocessing and modeling steps for clean, efficient code.
- Cross-validation Improve model generalization by validating it on different data subsets. Learn k-fold cross-validation and train-test-split strategies.
- XGBoost Learn XGBoost, a powerful gradient boosting algorithm known for speed and accuracy. Widely used in ML competitions and real-world applications.
- CatBoost Explore CatBoost, a gradient boosting library optimized for categorical features. Efficient, scalable, and less sensitive to parameter tuning.
- Hyperparameter tuning Improve model performance by optimizing hyperparameters using techniques like Grid Search and Random Search with cross-validation.
- Model interpretability Understand how models make predictions using tools like SHAP, LIME, and feature importance. Essential for building trust in ML decisions.
- Trend/seasonality Identify long-term trends and recurring seasonal patterns in time series data. Essential for accurate forecasting and business planning.
- Moving average Smooth out short-term fluctuations to reveal trends using rolling averages. Used in signal processing and stock market analysis.
- ARIMA (AutoRegressive Integrated Moving Average) Model time series data with trends and noise using ARIMA. Combines autoregression, differencing, and moving averages for powerful forecasting.
- SARIMA(Seasonal ARIMA) Extend ARIMA to handle seasonal patterns explicitly. Ideal for monthly, quarterly, or cyclic data with repeating trends.
- Prophet(by Meta) Use Facebook Prophet for quick, flexible time series forecasting. Handles seasonality, holidays, and missing data with ease.
- Seasonality decomposition Break down time series into trend, seasonality, and residual components. Helps visualize and analyze each influence separately.
- Image classification Train deep learning models to recognize and categorize images using CNNs (Convolutional Neural Networks). Applied in areas like facial recognition and medical imaging.
- Sentiment analysis Use NLP and deep learning to determine the sentiment (positive, negative, neutral) behind textual data such as reviews, tweets, or feedback.
- Tabular DL models Apply deep learning techniques to structured/tabular data using models like TabNet. Optimize performance on datasets typical in business and finance.
- Transfer learning Leverage pre-trained models to accelerate training and improve accuracy on smaller datasets. Useful in computer vision and NLP tasks.
- Batch norm Improve training speed and stability by normalizing inputs across layers. Helps reduce internal covariate shift in deep networks.
- Dropout Prevent overfitting by randomly disabling neurons during training. Enhances generalization by promoting model robustness.
- Optimizers Learn how optimizers like SGD, Adam, and RMSProp adjust weights during training to minimize loss and improve convergence.
- Loss functions Measure model prediction error using functions like Cross-Entropy or MSE. A crucial part of training and guiding model learning.
- Neurons Understand the basic computational unit of neural networks, inspired by the human brain. Learn how neurons process inputs to produce activations.
- Perceptron Explore the simplest type of neural network used for binary classification. Learn how perceptrons form the basis for deeper architectures.
- Activation functions Use functions like ReLU, Sigmoid, and Tanh to introduce non-linearity in models. Crucial for learning complex patterns in data.
- Gradient descent Master the optimization algorithm that minimizes model error by adjusting weights. Understand learning rate, cost function, and convergence.
- Convolutional Neural Networks (CNNs) Build models for image processing using convolutional layers, pooling, and filters. Widely used in visual recognition tasks.
- Recurrent Neural Networks (RNNs) Basics Understand how RNNs handle sequential data like text or time series. Learn about loops, memory, and limitations like vanishing gradients.
- TensorFlow/PyTorch setup Set up the two most widely used deep learning frameworks. Learn environment setup, GPU use, and writing your first model.
Salary Scale
Maximum
15 LPA
Average
8 LPA
Minimum
4 LPA
Job Role
- Data Analyst
- Business Analyst
- Machine Learning Engineer
- Data Scientist
- BI Analyst
- Data Engineer
Course Certificate
Eligibility Criteria
- Have a minimum of 70% marks throughout their academics
- Possess good English communication skills
- Pass the entrance test conducted by Futureacad
Tools & Technologies
Training Options
Online Training
₹46500
₹
34,000
Including Taxes* -
Structured, Industry-Vetted Curriculum
-
200+ Practical Assignments & Labs
-
Capstone Projects & Real-Time Simulations
-
Training on Industry-Relevant Tools & Platforms
-
AI-Powered LMS with Lifetime Access
-
Placement Assistance, Career Support & Guaranteed Placements
-
Live, Face-to-Face Mentorship
-
Collaborative Peer Learning
-
Hands-On Experience in Physical Labs
-
Offline Hiring Drives & Resume Building Sessions
job guarantee
Classroom Training
₹93000
₹
69,000
Including Taxes* -
Structured, Industry-Vetted Curriculum
-
200+ Practical Assignments & Labs
-
Capstone Projects & Real-Time Simulations
-
Training on Industry-Relevant Tools & Platforms
-
AI-Powered LMS with Lifetime Access
-
Placement Assistance, Career Support & Guaranteed Placements
-
Live, Face-to-Face Mentorship
-
Collaborative Peer Learning
-
Hands-On Experience in Physical Labs
-
Offline Hiring Drives & Resume Building Sessions
job guarantee
Admission Process
Job Readiness Assessment & Communication Test
Clear the qualifier test to be eligible for the program
Complete
Counselling
Only shortlisted candidates go through the counselling
Start
Learning
Unlock your potential with expert-led learning and stand out
Why Join this Program
Earn a job
Receive complete job assistance tailored to your career goals. Get expert placement guidance to confidently step into the industry.
Leverage knowledge from industry experts
Learn directly from seasoned Trainers and Gain real-world insights that go beyond textbooks.
Industry-relevant Tools & Practical Learning
Get hands-on experience with the latest tools used by top companies. Hands-on learning through 200+ exercises and 10+ projects with seamless access to integrated labs.
Structured, industry-vetted curriculum
A curriculum shaped by experts to meet evolving industry demands. Structured learning ensures you're career-ready from day one.
Integrated with Gen AI Modules
The curriculum includes cutting-edge Generative AI modules designed to align with emerging tech trends.
Interview preparation & Placement assistance
Sharpen your interview skills with practical training and expert guidance. Receive complete placement support to connect with top recruiters.
Why Join this Program
Analytics to AI Pathway
Learn everything from Excel dashboards to ML modeling in one track.
Top Tools Covered
Get fluent in Power BI, Tableau, Scikit-learn, and SQL.
Beginner to Expert Friendly
Learn step-by-step from data handling to predictive modeling.
Job-Ready Skills
Prepare for analyst, ML engineer, and data scientist roles.
FAQ
Basic familiarity is helpful; Python is taught from scratch.
Yes — both tools are introduced with hands-on dashboards.
Yes, with datasets from multiple industries.
Yes — with skills and project outcomes highlighted.
Yes — including resume building and mock interviews.
Yes — includes interview prep and real-world case practice.