Professional Certificate course in Data Science
Learn in Hindi, Tamil and Telugu
E&ICT Academy, IITK career program offers the Data Science Course with E&ICT Academy, IIT-K Certification. Gain job-ready Data Science skills in 5 months through Vernacular upskilling, 360-degree Career Guidance, Globally Recognized Certifications & Placement guidance.
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Duration
5 Months
(Weekend program)
Format
Live Online Class
- With Industry Experts from
About E&ICT Academy, IITK Data Science Certification
E&ICT Academy, IITK provides world-class upskilling experience for job aspirants seeking opportunities in trending tech career domains. The E&ICT Academy, IIT-K Professional Certificate course in Data Science will upgrade your career with flexible boot camp-style upskilling, comprehensive course structure, expert-guided mentorship, real-time data science projects, & industry-recognized skill certifications that catalyze your profile to ace lucrative career in Data Science industry. By bridging the skill gaps between the learners & the industry with top-notch skills, this extensive Data Science course is committed to offering assured job guidance.
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Learners Most Liked
Top Reasons To Choose Data Science as a Career
Growth in Data Science Industry
93,500+ job openings all over our country
(LinkedIn Survey)
Average Salary of Data Scientists in India
₹14.8 LPA
Glassdoor
Top Product-Based Companies Hiring Data Scientists
Avg. Salary in these companies: ₹40 LPA
High Demand Across Industries
E-Commerce
ML & AL
Government
Healthcare
Energy
Finance
Why Choose E&ICT Academy, IITK Data Science Certification?
- Designed for Working Professionals & Students
- Build a Portfolio with 5+ Projects
- Live Online Classes
- Ask-me-Anything Sessions
- Hackathons
- 7-day pre-boot refund policy
- Personalized Feedback on Assignments.
- Available in Hindi
- Doubt clearing sessions with Mentors
- 24-month EMI options
- Value-added sessions on Soft skills.
Get to Know Our Data Science Course Syllabus
This program has been made specially for you by leading experts of the industry that can help you land on a High-paying Job
Python - Basic
This module caters to beginners by acquainting them with the foundational concepts of Python programming. It covers everything from data types and loops to functions and data structures.
- Why python ?
- Python IDE
- Hello World Program
- Variables & Names
- String Basics
- List
- Tuple
- set
- Dictionaries
- Conditional Statements
- For and While Loop
- Built-in-Functions-(Numbers and Math)
- User Defined Function
- Modules and Packages
- Common Errors in Python
Python - Advanced
Building on the Python basics, this module explores more advanced concepts, such as list comprehensions, file handling, and object-oriented programming. It also delves into other important topics like pickling and debugging in Python, offering a comprehensive understanding of advanced Python concepts:
- List Comprehension
- File Handling
- Debugging in Python
- Class and Objects
- Lambda, Filters and Map
- Regular Expressions
- Python PIP
- Read Excel Data in Python
- Iterators, Decorators and Generators
- Pickling
- Python JSON
Algorithmic Thinking with Python
This module covers key concepts in algorithm design, including problem-solving strategies, algorithm analysis, data structures, and algorithmic paradigms.
- Introduction to algorithmic Thinking
- Algorithm Efficiency and time complexity
- Example algorithms – binary search, Euclid’s algorithm
- Data structures – stack, heap, and binary trees
- Memory Management/Technologies
- Best Practices – Keeping it simple, dry code, naming Conventions, Comments, and docs.
SQL Basic
In this module, we will dive into the SQL-based databases. We will learn the basics of SQL queries, schemas, and normalization.
- Database-Introduction and Installation,
- Data Modeling
- Normalization and Star schema
- ACID Transactions
- Data Types
- Data Definition Language (Create,Drop,Truncate,Alter)
- Data Manipulation Language (Select,Delete,Update,Insert)
- Data Control Language (Grant,Revoke)
- Transaction Control language (Commit,Revoke,Rollback)
- SQL Constraints(Primary key, Foreign Key,Unique,Not NULL, CHECK,DEFAULT)
- Operators (Arithmetic, Logical, Bitwise, Comparison,Compound)
- Clauses in SQL(Where,Having,Group by, Order by)
SQL Advanced
we will Continue into the SQL-based databases. We will learn the SQL Advanced queries, Join, Date and Time Functions and SubQueries.
- Joins(Inner,Left,Right,Full Join,Equi Join,Non-Equi Join,Self Join)
- Mathematical functions (SQRT,PI,SQUARE,ROUND,CEILING)
- Conversion functions(changing the data types)
- General functions(COALESCE,NVL,NULLIF)
- Conditional expressions (if,case)
- Date and time functions
- Numeric functions
- String Functions
- Subqueries
- Rank and Window Functions
- Integrating Python with SQL
Pandas
This module addresses the essential need for effective data handling. It introduces the Pandas library, detailing its various functions and features for efficient data manipulation and analysis:
- Introduction to Pandas
- Series Data Structure – Querying and Indexing
- DataFrame Data Structure – Querying, Indexing, and loading
- Merging data frames
- Group by operation
- Pivot table
- Date/Time functionality
- Example: Manipulating DataFrame
Statistics & Probability with Numpy- Basic
We will go through Probability and Statistics which are essential to understanding, process and interpret the vast amount of data. We will deal with the basics of probability and statistics like Probability theory , Bayes theorem, distributions etc and their importance. Besides that we will do hands on with Numpy upon those concepts.
- Why counting and probability theory?
- Basics of sample and event space
- Axioms of probability
- Total Probability theorem and Bayes Theorem
- Random variables, PMF and CDF
- Discrete Distributions – Bernoulli, Binomial and Geometric
- Expectation and its properties
- Variance and its properties
- Continuous Distributions – uniform, exponential and normal
- Sampling from continuous distributions
- Simulation techniques – simulating in NumPy
Statistics & Probability with Numpy- Advanced
We will continue with statistics and probability and we will deal with descriptive and inferential statistics along with Hypothesis testing and lot of other relevant statistics methods
- Inferential statistics – sample vs population
- CLT and its proof
- Chi-squared distribution and its properties
- Point and Interval Estimators
- Estimation technique – MLE
- Interval Estimator of μ with unknown σ
- Examples of estimators
- Hypothesis testing – I
- Hypothesis testing – II
- Hypothesis testing – III
Data Visualization using Python
Data Visualization is used to understand data in visual context so that the patterns , trends and correlations in the data can be understood. We will do a lot of visualization with libraries like Seaborn, Matplotlib etc inturn that leads to effective storytelling.
- Read Complex JSON files
- Styling Tabulation
- Distribution of Data – Histogram
- Box Plot
- Data Visualization – Recap
- Pie Chart
- Donut Chart
- Stacked Bar Plot
- Relative Stacked Bar Plot
- Stacked Area Plot
- Scatter Plots
- Bar Plot
- Continuous vs Continuous Plot
- Line Plot
- Line Plot Covid Data
Data Visualization (Tool) PowerBI/Tableau (Add-on)
This module covers a range of topics essential for mastering Power BI/ Tableau including data preparation, data modeling, data visualization, and report creation.
- POWERBI
- Introduction to PowerBI
- Creating, Managing and filtering Data
- Basic Plots in PowerBI – Trend Analysis, Area,
- Ribbon, Scatterplots and Decomposition trees
- Creating PowerBI reports
- Creating interactive dashboards and deploying the dashboards
- TABLEAU
- Introduction to Tableau
- Connecting, managing and aggregating data
- Visual Analytics in Tableau
- Simple predictive analytics using tableau
- Building Tableau Dashboards
Introduction to Machine Learning
This module provides participants with a solid foundation in machine learning principles, algorithms, and methodologies.
- What is Machine Learning?
- Different types of Machine Learning problems (Supervised, Unsupervised, Reinforcement)
- Applications of Machine Learning
- The Machine Learning Pipeline
Machine Learning: Data Collection
This module covers a wide range of topics related to data collection, including data acquisition strategies.You will learn how to identify relevant data sources, retrieve data from various sources such as databases, APIs, and web scraping.
- Data Sources (Structured, Unstructured)
- Data Collection Techniques (APIs, Web Scraping, Sensors)
- Data Acquisition Ethics
Machine Learning: Data Cleaning & Pre-Processing
This module covers a comprehensive range of topics related to data cleaning and preprocessing, including handling missing values, dealing with outliers, standardizing and scaling numerical features, encoding categorical variables, and feature engineering.
- Data Cleaning Techniques (Handling Missing Values, Outliers)
- Data Transformation (Scaling, Normalization, Encoding)
- Feature Engineering (Feature Selection, Creation)
- Balancing Data (Undersampling, Over Sampling and SMOTE)
Machine Learning: Exploratory Data Analysis
This module covers a wide range of topics related to exploratory data analysis, including data visualization, summary statistics, correlation analysis, and dimensionality reduction techniques.
- Data Visualization Techniques (Histograms, Scatter plots, Box Plots etc )
- Univariate, Bivariate and Multivariate Analysis
- Understanding Data Distribution and Relationships
- Identifying Patterns and Trends
- Feature Importance Analysis
Machine Learning: Model Building
Model building is a crucial stage in the machine learning workflow, where practitioners leverage algorithms to learn patterns and make predictions from data. In this module, you will learn about Supervised learning techniques.
Supervised Learning
- Introduction to Supervised Learning
- Linear Regression (Regression)
- Logistic Regression (Classification)
- Decision Tree (Regression / Classification)
- Random Forest (Regression / Classification)
- Support Vector Machine (Regression / Classification)
- Naive Bayes (Regression / Classification)
- XGBoost (Regression / Classification)
- KNN (Regression / Classification)
- ARIMA (Forecasting)
Machine Learning: Model Building- Continued
We will continue learning into model building and delve into UnSupervised learning techniques & Reinforcement learning.
- UnSupervised Learning
- Introduction to Unsupervised Learning
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN
- PCA
- Reinforcement
- Introduction to Reinforcement Learning
Machine Learning: Model Evaluation & Hyper Parameter Tuning
This module covers a comprehensive range of topics related to model evaluation and hyperparameter tuning. You will learn how to assess the performance of machine learning models using various evaluation metrics.
- Model Evaluation
- Regression (R2, MAE, MSE, RMSE etc)
- Classification( Accuracy, Precision, Recall,F1-Score, AUC-ROC etc)
- Model Hyperparameter Tuning
- Random Search
- Grid Search
- Bayesian Optimization
- Cross Validation
- Early Stopping
Machine Learning: Model Deployment
This module focuses on the final stage of the machine learning pipeline, where trained models are deployed into production environments to make predictions on new data.
- Saving and Loading Models
- Preparing Models for Production Environments
- Model Monitoring and Performance Tracking
- MLFlow
Deep Learning with Pytorch: NN & ANN
This module provides participants with a comprehensive introduction to deep learning concepts and techniques using PyTorch. We will also discuss neural networks(NN), the building blocks of deep learning, and artificial neural networks (ANNs).
- Fundamentals of Neural Networks: Limitations of ML; The Neuron; Linear perceptron as neurons
- Feed Forward Neural Networks: Linear Neurons and limitations; Sigmoid, Tanh and ReLU; Softmax
- Learning-I: Gradient Descent; Delta rule and learning rates; Gradient descent with sigmoidal Neurons
- Learning-II: Backpropagation; Stochastic and minibatch; Test set, validation set, and overfitting
- Preventing overfitting
- PyTorch Basics: Installation and setup of PyTorch; Tensors and operations in PyTorch
- Training Fundamentals: Autograd; Backpropagation; Gradient Descent; Training Pipeline.
- Regression with PyTorch: Linear Regression; Logistic Regression
- Dataset in PyTorch: Dataset and Dataloader; Dataset Transforms.
- Training Pipeline: Softmax and Crossentropy; Activation Functions
Deep Learning with Pytorch: CNN
This module focuses specifically on CNNs, a specialized type of neural network designed to effectively capture spatial hierarchies and patterns present in images.
- Introduction to CNN Architecture
- Image Filter/Image kernel;
- Convolution layer and RGB
- Pooling Layer
Deep Learning with Pytorch: RNN
This module is designed to provide a deep understanding of recurrent neural networks (RNNs) and their applications using PyTorch, a popular deep learning framework.
- Introduction to RNN Architecture
- Language models;
- Generation with RNNs
- Drawback of RNN
Deep Learning with Pytorch: LSTM
This module provides a thorough understanding of Long Short-Term Memory(LSTM) networks, including their architecture, training algorithms, and applications.
- Adding more memory: LSTM architecture
- Applications of LSTM
- Drawback of LSTM
Deep Learning with Pytorch: Transformers & GAN
This module explores advanced deep learning concepts focusing on Transformers and Generative Adversarial Networks (GANs) using PyTorch.
- Introduction to Transformer Architecture
- Self Attention Layer
- Encoder
- Decoder
- Sequence to Sequence
- Transfer Learning (Hugging Face)
Natural Language Processing(NLP)
This module offers participants a comprehensive introduction to the field of natural language processing, focusing on techniques and applications for analyzing and understanding human language data.
- Text Processing:
- Tokenization
- Normalization
- Stop word removal
- Stemming/Lemmatization
- Text Vectorization and Embedding
- Bag-of-Words (BoW)
- TF-IDF
- Word Embeddings
- Sentence Embeddings
Natural Language Processing(NLP)-Continued
In this module, We will continue into NLP techniques and focus on applications of pre-trained models using Hugging Face.
- Applications of Pre-Trained Models (Hugging Face):
- Text Classification: Classifying text into predefined categories (e.g., sentiment analysis, spam detection).
- Machine Translation: Translating text from one language to another.
- Question Answering: Extracting answers to questions from a given context.
- Text Summarization: Condensing lengthy text into a shorter, informative summary.
- Text Generation: Generating different creative text formats like poems, code, scripts, etc. (depending on the model).
Computer Vision: Image Pre-Processing
This module is designed to equip participants with the essential techniques and methodologies for preparing and pre-processing images in computer vision applications.
- Annotation: Marking important parts of the image, like objects or areas of interest.
- Data Augmentation: Making variations of the image by doing things like flipping, rotating, or changing colors. This helps the model learn better by seeing more examples.
- Normalization: Adjusting the brightness and contrast of the image to make it easier for the model to understand.
- Resizing: Making sure all images are the same size so the model can process them easily.
Computer Vision: Image Classification
This module covers Image classification, a fundamental task in computer vision, where the goal is to categorize images into predefined classes or categories based on their visual content.
- Convolutional Neural Networks (CNNs)
- Residual Networks (ResNets)
- Inception Networks
- MobileNets
- EfficientNet
Computer Vision: Object Detection
This module delves into the techniques and methodologies for detecting and localizing objects within images or videos, a fundamental task in computer vision applications.
- Faster R-CNN
- YOLO (You Only Look Once)
- SSD (Single Shot Multibox Detector)
- Mask R-CNN
Computer Vision: Image Segmentation
This module is dedicated to exploring advanced techniques for partitioning images into semantically meaningful regions, known as image segmentation.
- Semantic Segmentation
Cloud Computing using AWS
This module provides a comprehensive understanding of cloud computing principles and practical skills in utilizing Amazon Web Services (AWS), one of the leading cloud service providers.
- Cloud Infrastructure
- Overview of AWS services: compute, storage, networking, databases.
- Key AWS services: EC2, S3, VPC, RDS.
- Cloud Configurations & Services
- IAM for access control.
- CloudFormation for infrastructure as code.
- AWS Lambda for serverless computing.
- Elastic Beanstalk for application deployment.
Cloud Computing using AWS-Continued
Through this module, you will have the skills and knowledge to effectively leverage Amazon SageMaker to build, train, and deploy machine learning & Deep learning models for a variety of use cases. We will understand the end-to-end workflow of model development in SageMaker.
- Building & Deploying ML Model in SageMaker
- SageMaker for ML model building and deployment.
- Data preprocessing and model selection.
- Training, evaluation, and deployment of ML models.
- Building & Deploying DL Model in SageMaker
- Deep learning concepts and architectures.
- SageMaker for building and training DL models.
- Deployment of DL models with SageMaker endpoints.
Cloud Computing using AWS: Hosting
In this module, we will learn how to effectively deploy and host ML/DL applications on AWS infrastructure. Also, we will understand the different deployment options available on AWS and be able to select the most suitable approach based on their application requirements.
- Hosting An ML/DL Application on AWS
- Integrating ML/DL models into web apps.
- Deployment and scaling on AWS infrastructure.
- Monitoring, logging, security, and compliance measures.
Generative AI: Unleashing the Power of Language Models
Generative AI introduces learners to the cutting-edge field of generative artificial intelligence (AI), focusing on the remarkable capabilities of Large Language Models (LLMs) and their applications in various domains. The module provides a comprehensive overview of LLMs, prompt engineering techniques, and fine-tuning strategies.
- LLM (Large Language Model)
- Introduction to Large Language Models
- Description of GPT-3 and chatGPT architecture
- Application of LLMs in various fields
- Basic description of other LLMs
- Learn GenAI with Llama, OpenAI, Gemini, Hugging Face
- Prompt Engineering
- Introduction to Prompt Engineering
- Overview of language models and their capabilities
- Understanding Language Model Responses
- Crafting Effective Prompts
- Controlling Model Output
- FineTuning LLM
- Fine-Tuning Techniques
- Task-specific fine-tuning vs. domain adaptation
- Architecture modifications for task-specific fine-tuning
- Fine-Tuning Techniques
- Dataset selection and curation for fine-tuning
- Implementing fine-tuning pipelines with PyTorch
- Hyperparameter tuning and optimization strategies.
Sharpen your skills in:
Enhance Your Resume with Industry Projects
Product Performance Analysis- Using EDA
The project aims to build an end-to-end automated data processing workflow that handles data uploads from the Order and Returns teams, performs a join operation using Glue & PySpark, stores the joined data in Redshift, and sends notifications about the pipeline's status using SNS.
Enhancing Renewable Energy Forecasting - Using Machine Learning
Utilize machine learning to boost renewable energy forecasting accuracy by analyzing historical data and weather patterns. Our aim is to optimize energy production and grid management, reducing reliance on fossil fuels.
Hotel Room Demand Forecasting- Using Machine Learning
Develop a machine learning model to predict hotel room demand using historical booking data. Hotel chains can employ this model to optimize pricing, revenue strategies, and resource allocation, enhancing occupancy rates.
Wildlife Image Classification- using Deep Learning
Develop a deep learning model to classify wildlife images into different categories such as species, habitats, or behaviors. Ultimately, the goal is to leverage advanced machine learning techniques to contribute to conservation efforts, ecological research, and wildlife management.
Financial News Summarization (Text)- Using NLP
Employ text summarization techniques to produce brief summaries of financial news, earnings reports, and market analyses. This enables investors and analysts to stay abreast of pertinent information and market insights efficiently
Visual Inspection of Solar Panel Defects- Using computer vision
Employ text summarization techniques to produce brief summaries of financial news, earnings reports, and market analyses. This enables investors and analysts to stay abreast of pertinent information and market insights efficiently
Learn From Our Top Data Science Experts
No teacher is better than the best friend who teaches you before the exam. Here, mentors will be your best friends!
Professional Data Science Certification
How will I benefit from this certification?
- E&ICT Academy, IIT-K Professional Certificate course in Data Science
- Get Government Verified Skill India Certification
- Globally recognized Certification
- Complement your abilities in Interviews
- Projects a career growth
Become E&ICT Academy, IITK Certified Data Science Professional
Professional Data Science Certification with Placement Guidance
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Enroll in the E&ICT Academy, IITK - Data Science
Course
- Attend live online classes
- Complete the projects assigned by Industry Experts
-
Attend mock interviews with our industry
experts
- Get guidance to crack the Interviews with lucrative packages
Unlock Your Upskilling Journey @
₹2,10,000
+ GST
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- EMI options available
- Learn at your own Pace
- Join the Prebootcamp (Demo Sessions) with 100% Refund Guarantee
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Who Can Apply for the professional Data Science Certification?
Data Science is constantly ranked as one of the most sought-after fields, year after year, on multiple verticles and rankings. According to Forbes, it’s the most promising Job profession of the 21st century, yielding a better future for every one skilled enough to spin off Data. It offers excellent job prospects, competitive salaries, and opportunities for massive career growth.
Why Choose E&ICT Academy, IIT Kanpur for Learning Data Science?
E&ICT Academy, IITK career programs are project-based online boot camps that focus on bestowing job-ready tech skills through a comprehensive course curriculum instructed in regional languages for the comfort of learning the latest technologies.
E&ICT Academy, IIT-K Certification
Highlight your portfolio with skill certifications from E&ICT Academy, IIT-K that validate your skills in Advanced Programming & Globally recognized certifications in other latest technologies of Data Science.
Vernacular Upskilling
Ease your upskilling journey by learning the high-end skills of Data Science in languages such as Tamil along with Hindi and Telugu.
Industry Experts’ Mentorship
Get 360-degree career guidance from mentors with expertise & professional experience from world-famous companies such as Google, Microsoft, Flipkart & other 600+ top companies.
Frequently Asked Questions
To enroll & pre-book a seat in the E&ICT Academy, IITK Data Science Program, fill in your details & submit here by Paying ₹8000 (Refundable) and Attend the Prebootcamp Session. Get through the Pre-boot camp test & counselling session to customize your learning experience in your preferred native language offered in the E&ICT Academy, IITK main boot camp. And next, follow the steps-
Attend Live online classes + Pursue self-paced learning
Complete the projects assigned by industry experts
Secure a digital portfolio in “Github”
Attend mock interviews with our HR team & technical rounds with Industry Experts
Receive Interview opportunities from top companies
Attend & clear the interview with lucrative packages
Anyone interested in Data Science with at least a graduation degree can pursue the E&ICT Academy, IITK Data Science Program. This program is open for college students, job aspirants & early professionals who wish to switch their careers to data science.
The course duration of the E&ICT Academy, IITK Data Science Program is for 5 months.
The pre-boot camp test will assess your basic data science, coding & aptitude skills. These fundamental skills serve as prerequisites to get started in the E&ICT Academy, IITK Data Science Course.
This program has up to 24-month of EMI options Available for the payment of the course fee. You can start with pre-booking fee of ₹8000 (Refundable), and evaluate your Pre bootcamp performance. If still interested then proceed towards your successful upskilling journey or else stay assured of the ‘7-day pre-boot refund policy’.
Python programming: basics & advanced concepts are included in the E&ICT Academy, IITK Data Science Program with additional Python Libraries.
No, a basic level of understanding in programming is preferred but it is not mandatory to get started in the E&ICT Academy, IITK Data Science Program. You can start learning from scratch & still master Advanced Programming relevant to Data Science.
This is a 100% online course that includes LIVE sessions by Industry experts and Self-paced learning course modules for flexible learning.
Data Science is a lucrative career domain that offers millions of opportunities in various sectors such as banking, finance, insurance, entertainment, telecommunication, automobile, etc. There were about 27,20,000 job listings for Data Science in 2023. About 17,700 openings for data scientists are projected each year, on average, over the decade. With right skills & a strong grip on latest technologies, you can grab your job offer as a well-paid Data Science Professional.
Still have queries? Contact Us
Request a callback. An expert from the admission office will call you in the next 24 working hours. You can also reach out to us at support_ifacet@iitk.ac.in or +91-9219972805, +91-9219972806
