Your Ultimate Handbook for Mastering Data Sceince & Artificial Intelligence
Data Science and Artificial Intelligence is a course that delves into the techniques, tools, and methodologies used to extract insights from data and build intelligent systems. It focuses on Data Science, which involves data collection, cleaning, analysis, and visualization to support data-driven decision making. The course also explores Artificial Intelligence, enabling machines to mimic human intelligence through machine learning, deep learning, and natural language processing.
Propgram Duration
12 Months
Time Commitment
12-15 Hrs/Week
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900+ Companies
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How Can We Turn You Into an Expert in data science & AI?
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Required Skills
THE ONLY Data science & AI COURSE THATMakes You Industry-Ready & Future-Proof
Prepare for sought-after roles like Data Scientist, Machine Learning Engineer, and AI Specialist with this comprehensive course tailored for the growing data-driven industry.
Train for industry-recognized certifications like TensorFlow Developer and Microsoft AI Engineer, with structured modules, practical projects, and expert mentorship for career success.
Gain real-world experience with live datasets and AI models. Master data analysis, machine learning algorithms, and neural networks through hands-on coding and real-time projects.
Our Curriculum
Expert-Design Course Structure
Preparatory Sessions - Python & Linux
Why Learn This
This module is designed to prepare students with foundational knowledge in Python and Linux, essential for DevOps and cloud environments. It covers basic to intermediate concepts in Python including object-oriented programming, and introduces core Linux commands and environment handling through hands-on practice.
1.Overview of Python programming and setting up development environments
2.Basic syntax, data types, control structures, and functions in Python
3.Understanding classes, objects, inheritance, and other OOP principles in Python
4.Practical exercises to reinforce Python concepts
5.Understanding the Linux operating system and its role in development environments
6.Core Linux commands, file system navigation, and basic shell scripting
7.Practical Linux exercises to build command-line proficiency
Data Analysis With MS-Excel
Why Learn This
This module focuses on using Microsoft Excel for data analysis tasks, including data exploration, visualization, and solving analytical problems. It covers key Excel features and tools used in the data analytics workflow, empowering learners to perform classification, regression, and statistical analysis within Excel.
1.Understanding Excel interface, formulas, functions, and data management basics
2.Using Excel tools for data cleaning, transformation, and exploratory data analysis
3.Creating charts, pivot tables, and dashboards for data insights
4.Introduction to Power Query, Power Pivot, and advanced Excel features
5.Applying Excel techniques to solve classification problems
6.Using Excel to calculate statistical and information-based metrics
7.Performing regression analysis using Excel tools and functions
Data Wrangling with SQL
Why Learn This
This module provides comprehensive training on using SQL for data wrangling tasks. It covers foundational and advanced SQL concepts, including user-defined functions and performance optimization, enabling learners to efficiently manipulate and query data in relational databases.
1.Introduction to SQL syntax, SELECT statements, filtering, sorting, and joining tables
2.Nested queries, window functions, aggregations, and set operations
3.Creating and using UDFs for custom SQL operations
4.Best practices for writing efficient SQL queries and improving database performance
Python With Data Science
Why Learn This
This module introduces the use of Python in data science workflows, focusing on essential libraries and techniques for extracting, transforming, and loading data. Learners will gain hands-on experience in data handling, preprocessing, and visualization using popular Python libraries like NumPy, Pandas, and Matplotlib.
1.Understanding the ETL process using Python for structured data pipelines
2.Efficient numerical operations and array manipulations with NumPy
3.Using Pandas for data cleaning, filtering, grouping, and reshaping
4.Preparing data for modeling through encoding, scaling, and imputation techniques
5.Creating insightful plots and charts using Matplotlib and Seaborn
Linear Algebra and Advanced Statistics
Why Learn This
This module covers the mathematical foundations essential for data science, focusing on linear algebra and advanced statistical methods. Students will learn key concepts in descriptive and inferential statistics, as well as probability theory, which are crucial for building and understanding machine learning models.
1.Summarizing and describing data using mean, median, mode, variance, and standard deviation
2.Understanding basic probability concepts, distributions, and their applications in data science
3.Hypothesis testing, confidence intervals, and drawing conclusions from sample data
Machine Learning
Why Learn This
This module introduces the fundamentals of machine learning, covering both supervised and unsupervised learning techniques. Students will explore key algorithms such as regression, classification, and clustering, along with essential performance metrics to evaluate model effectiveness.
1.Overview of machine learning concepts, types, and real-world applications
2.Understanding and implementing linear and logistic regression models
3.Exploring classification algorithms like decision trees, SVM, and k-NN
4.Applying clustering techniques such as k-means and hierarchical clustering
5.Training models on labeled data using regression and classification methods
6.Discovering hidden patterns in data without predefined labels using clustering
7.Evaluating model performance using metrics like accuracy, precision, recall, and F1-score
READY FOR DATA SCIENCE & AI ROLES
Covering all modules above makes you ready to apply for data science & AI roles
Deep Learning Using TensorFlow
Why Learn This
This module introduces the foundations of deep learning using TensorFlow, covering the basics of artificial intelligence and neural networks. Students will explore how deep learning models work and how to implement and train neural networks using TensorFlow.
1.Introduction to AI, its types, and real-world use cases
2.Understanding perceptrons, hidden layers, activation functions, and backpropagation
3.Building and training deep neural networks using TensorFlow
Power BI
Why Learn This
This module focuses on data visualization and business intelligence using Power BI. Learners will understand the basics of Power BI, work with DAX for data modeling, and build interactive dashboards for analytical insights.
1.Overview of Power BI interface, data connections, and report building
2.Data Analysis Expressions for calculated columns, measures, and advanced analytics
3.Creating dashboards and visuals for data storytelling and insights
Deploying Machine Learning Models With Cloud
Why Learn This
This module introduces the principles of MLOps and focuses on deploying machine learning models using cloud platforms. Learners will gain practical knowledge of operationalizing ML models and integrating them into scalable cloud-based environments.
1.Understanding the MLOps lifecycle, CI/CD for ML, and the importance of model deployment
2.Techniques and tools for deploying ML models on cloud services such as AWS, Azure, or GCP
GIT
Why Learn This
This module focuses on version control using GIT, an essential tool for collaborative software development. Learners will understand how to track changes, manage code versions, and collaborate effectively using GIT repositories.
1.Introduction to version control systems and the importance of code management
2.Using GIT for initializing repositories, committing changes, branching, merging, and working with remote repositories like GitHub
Data Science Capstone Project
Why Learn This
This final module is a culmination of all the skills and concepts learned throughout the course. Students will work on a comprehensive capstone project that involves solving a real-world problem using data science techniques such as data preprocessing, analysis, machine learning, and deployment.
1.Apply data science workflow—from data collection and cleaning to model building, evaluation, and deployment—to a real-world problem
Business Case Studies
Why Learn This
This module covers a variety of business case studies where data science and machine learning techniques are applied to real-world scenarios. Students will work on projects involving recommendation engines, prediction models, object detection, and financial analysis, gaining practical experience in solving complex business problems.
1.Building and evaluating a recommendation system for personalized suggestions
2.Predicting ratings and feedback scores using regression techniques
3.Analyzing census data to derive insights and trends
4.Predicting housing prices and analyzing factors that affect real estate
5.Implementing machine learning models to detect objects in images
6.Analyzing stock market trends and predicting future stock prices
7.Solving a real-world banking problem using machine learning algorithms
8.Building a conversational AI chatbot using natural language processing techniques
Natural Language Processing
Why Learn This
This module introduces the fundamentals of Natural Language Processing (NLP), covering techniques for text mining, cleaning, and pre-processing. Students will explore methods for text classification, sentiment analysis, sequence tagging, and building AI chatbots and recommendation systems using NLP.
1.Techniques for extracting useful information from text data, including cleaning and preparation
2.Using NLTK for text classification tasks and performing sentiment analysis on textual data
3.Understanding sentence structures, tagging sequences, and implementing language models
4.Building AI chatbots using NLP and recommendation engines based on user preferences
Computer Vision
Why Learn This
This module delves into the concepts and techniques used in computer vision, focusing on deep learning models like RBM, DBNs, and Variational Autoencoders. Students will explore object detection, image generation, and reinforcement learning, as well as the deployment of deep learning models for real-world applications.
1.Understanding Restricted Boltzmann Machines (RBM), Deep Belief Networks (DBNs), and Variational Autoencoders for feature extraction and generation tasks
2.Implementing CNNs for detecting objects in images and videos
3.Using neural style transfer to generate artistic images and working with GANs for image creation
4.Utilizing distributed and parallel computing techniques to scale deep learning model training
5.Understanding and implementing reinforcement learning algorithms for decision-making problems
6.Techniques for deploying and scaling deep learning models for production environments
Data Science at Scale with PySpark
Why Learn This
This module introduces the concepts of Big Data processing using PySpark. Students will learn how to scale data science workflows with Spark, work with Resilient Distributed Datasets (RDDs), and explore advanced concepts like integrating Spark with Hive for efficient big data querying.
1.Overview of Big Data concepts and how Apache Spark enables distributed data processing
2.Understanding Resilient Distributed Datasets (RDDs) for distributed data storage and computation
3.Advanced Spark functionalities and integrating Spark with Hive for big data querying and analysis
YOU'RE NOW READY FOR DATA SCIENCE & AI ROLES
Covering all modules above makes you ready to apply for data science & AI roles
Gain Real-World Data Science & AI Experience!
Career-Boosting Projects
Face Detection
Use Python 3.5(64-bit) with OpenCV for face detection. As an important requirement, learners need to ensure that the system detect multiple faces in a single image while working with essential libraries like cv2 and glob.
AI Chatbot
In this interesting project, work with IBM Watson AI Chatbot. Create your own AI Chatbot and see how IBM Cloud platform helps you to create the chatbot on the backs of possibly the most advanced Machine Learning systems available.
Restaurant Revenue Prediction
Work with Ensemble Model for predicting annual restaurant sales using various features like opening date, type of city, type of restaurant. Work with packages like caret, Boruta, dplyr to analyze the dataset and predict the sales.
Work with PySpark & RDD
Work with PySpark which is a Python API for Spark and use the RDD using Py4j package. As an important part of this project, you will also work with SparkConf provides configurations for running a Spark Application.
Build the Book Recommender Application
Work with packages like a recommended to lab, dplyr, tidy, stringr, corpus and many others to create your book recommender engine using the user-based collaborative filtering model that recommends the books based on past ratings.
Census Project
Work with census income dataset from UCI Machine Learning repository that contains information of more than 48k individuals. Use data handling techniques to handle missing values and also predict the annual income of people.
Housing Price Prediction
In this project on housing price prediction, get practical exposure on how to work with house price dataset and predict the sale price for each house with 79 explanatory variables describing every aspect of the residential houses.
HR Analytics
Learn to work with the HR Analytics dataset and understand how the HR can help you to re-imagine HR problem statements. Understand the features of the dataset and in the end, evaluate the model by metric identification process.
Joke Rating Prediction
Work with the dataset taken from the famous jester online Joke Recommender system and successfully create a model to predict the ratings for jokes that will be given by the users (the same users who earlier rated another joke).
Build Recommendation Engine
Create your own recommendation engine using the SVD algorithm to predict the ratings on Netflix based on the past ratings of the user. Work with various packages like NumPy, pandas, matplotlib, plotly to handle missing values from the dataset.
Data Science & AI Curriculum
Your Journey With Careertronic
Onboarding Session
Intro Session
Start in a customized cohort and forge meaningful connections who will be your allies on this journey.
Select the right mentor for guidance and gain invaluable insights to boost your career.
Connect with a Learning Coordinator
Live Learning Experience
Live Classroom
Engage with instructors and connect with your peers in real-time
Practice with
Assignments & Home Works
1:1
Guidance from Pro Mentors
Cloud Sandbox
Hands-on practice in real-world cloud environment
AI-Assisted
Problem-solving support
Situational
Problem & Solution
Teaching-Assistance
1:1 Teaching Assistant over chat & video call
Live Classroom
Engage with instructors and connect with your peers in real-time
Practice with
Assignments & Home Works
1:1
Guidance from Pro Mentors
Cloud Sandbox
Hands-on practice in real-world cloud environment
AI-Assisted
Problem-solving support
Situational
Problem & Solution
Teaching-Assistance
1:1 Teaching Assistant over chat & video call
Practice with
Assignments & Home Works
1:1
Guidance from Pro Mentors
Situational
Problem & Solution
Live Classroom
Engage with instructors and connect with your peers in real-time
Cloud Sandbox
Hands-on practice in real-world cloud environment
AI-Assisted
Problem-solving support
Teaching-Assistance
1:1 Teaching Assistant over chat & video call
Training & Placement Support
Module-Based Mocks
Practically apply your skills through interview simulations post-module.
Resume Building
Build an impactful, professional resume with expert mentorship.
Placement Training
Focused training to excel in tech recruitment processes.
Placement Support
End-to-end assistance to secure your dream job.
Meet Mentors & Instructors
Tap into the wisdom of Data Science & AI Experts
Anshuman Singh
Naman Balla
Anshuman Singh
Anshuman Singh
Aman Sharma
This course is a must for anyone preparing for system design interviews! The real-world case studies on Uber, Netflix, and WhatsApp helped me understand how large-scale applications work. The explanations on microservices and database scaling were crystal clear. Highly recommended!.
Priya Desai
Great content with detailed coverage of caching, message queues, and load balancing. The instructor explained concepts in a structured way, making them easy to grasp. I just wish there were more coding exercises to practice system design problems.
Rahul Verma
As a backend developer, this course helped me improve my architectural thinking. Learning about CAP theorem, database sharding, and security best practices gave me a deeper understanding of system scalability. Definitely worth it!.
Data Science & AI Program
- Skills
- Certification
- Placement Support
Data Science & AI Program + Project Certificate
- Skills
- Certification
- Placement Support
- Portfolio
- 5+ Projects Certificate
Frequently Asked Questions
General
Who can take up this course?
The course is specifically designed for Engineering students doing bachelor and master degree who wish to expand their knowledge in Automation Industrial persons and faculty members who would like to develop capabilities in Automation Individuals seeking career in domains in Industrial automation applications Graduates who seek job in electrical, instrumentation, automation domain.
What is included in your course?
This course is designed to include all requirements for a power electronic / Automation engineer or those required for research level jobs.
What will the student gain from your course?
With the evolution of automation technologies, the importance of Instrumentation, Control and Automation usage is increased significantly. Therefore, it is essential for an electrical / instrumentation engineer to understand this field thoroughly. In this course, students will get detailed theoretical knowledge and design insights with their control schemes. With this knowledge, students will be able to design, simulate and analyze the machine or process better.
How is this course going to help a student get a job?
As mentioned earlier as well, this course is designed to not only cover the basic concepts but also applications in the industrial systems. Further, from basic switching mode converter to details on the modulation scheme principle, all basics are covered in detail. Additionally, the techniques to control the industry scale products are also discussed. The challenges and projects given in this course, which students will be solving are indegineously designed to train them in handling any industrial problem. Therefore, the skill sets obtained by the student as a part of this course will help him to not only crack the entrance or technical interview for such jobs but also to lead any industrial challenge as a part of his job profile related to this field.
What are the job opportunities in this field?
Today, Automation components are prominently used in majorly all industrial system since the industrial revolution in Industry 4.0 has taken by storm. The major players in this area are ABB, Siemens, Fuji, Rockwell, Emerson, Mitsubishi, Alstom, Hitachi etc. These companies supply and use various products like PLC, HMI, DCS, SCADA, HMI, IIoT, Field Instrumentation, Analyzers etc. The techniques taught in this course will be directly applied to design and analyse these systems and thus in above mentioned industries.