Professional Certificate course in Data Science

Learn in Hindi, Tamil and Telugu

IFACET career program offers the Data Science Course with IIT-K Certification. Gain job-ready Data Science skills in 3-5 months through Vernacular upskilling, 360-degree Career Guidance, Globally Recognized Certifications & Placement guidance.

I’m Interested


3 Months / 5 Months (Weekday/Weekend)


Live Online Class

Hiring Partners

600+ Companies

About IFACET’s Data Science Certification

IFACET provides world-class upskilling experience for job aspirants seeking opportunities in trending tech career domains. The 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.

Our Prestigious Accreditations

Unlock Your Dream Job with Our Certification


Hiring Partners




Doubt Clarification


Students Placed


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


Top Product-Based Companies Hiring Data Scientists

Avg. Salary in these companies: ₹40 LPA

High Demand Across Industries







Data science is a fascinating field that involves extracting insights and knowledge from data. It is also the most demanding profession in the IT industry and also helps industries grow and expand their businesses by extracting valuable data insights from raw information. Several modern methods are used by data scientists to drive profitability and cater to the need for solutions to real-world problems.
This IIT-K Professional Certificate course in Data Science is very well structured for the dynamic and ever-expanding world of data science. It’s a field that holds immense potential to transform industries and our understanding of the world around us. By the end of this course, you’ll learn to build some amazing projects that will add value to your resume and help you in getting a high-paying job at top product-based companies.

Why Choose IFACET's Data Science Certification?

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

We will go through the basics of Python with all essential beginner-friendly concepts of Python programming like datatypes, loops, data structures, and functions, followed by assessments and assignments.

  • Why Python
  • Python IDE
  • Hello World Program
  • Variables & Names
  • String Basics
  • List
  • Tuple
  • Dictionaries
  • Conditional
  • Statements
  • For and While
  • Loop
  • Functions
  • Numbers and
  • Math Functions
  • Common Errors in Python

Python - Advanced

Since we have the essential basics of Python we will see some advanced concepts like Comprehension, File handling, Regular Expressions, Object-oriented Programming, Pickling, and many more essential concepts.

  • Functions as Arguments
  • List
  • Comprehension
  • File Handling
  • Debugging in Python
  • Class and Objects
  • Lambda, Filters and Map
  • Regular Expressions
  • Python PIP
  • Read Excel Data in Python
  • Iterators
  • Pickling
  • Python JSON

Algorithmic Thinking with Python

We will explore the need for Algorithmic Thinking and the necessity of efficient coding; we will drive through Data Structures and Algorithms along with Memory Management Techniques.

  • 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.
  • Assessment

Data handling in Python - Pandas


Since we need to handle huge amounts of data, we will be implementing data handling techniques with Pandas library. And we will explore the different miscellaneous functions of Pandas library in detail.

  • 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



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 - Continued


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,GO TO,NULL)
  • Date and time functions
  • Numeric functions
  • String Functions
  • Subqueries
  • Rank and Window Functions
  • Integrating Python with SQL

Probability and Statistics with NumPy

We will go through Probability and Statistics which are key to understanding, processing, and interpreting 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 on 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
  • Assessment

Probability and Statistics with Numpy - Continued

We will continue with statistics and probability, and we will deal with descriptive and inferential statistics along with Hypothesis testing and a 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
  • Assessment

Data Visualisation in Python (Matplotlib, Seaborn, Plotly)


Data Visualization is used to understand data in a visual context so the patterns, trends, and correlations in the data can be understood. We will do a lot of visualization with libraries like Seaborn, Matplotlib, etc, in turn, 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
  • Assessment

Data Engineering with Python


It is always needed to analyze the data and preprocess it , since the real world data is not always industry ready, so in this Module we will be dealing with a lot of data cleaning and Exploratory data Analysis techniques which is a very crucial stage for any data science project

  • Handling missing data
  • Techniques to impute missing values
  • Encoding the data
  • Outlier detection and correction
  • Meaningful data transformation
  • Assessment

Exploratory Data Analysis with Python


Real world data is always messy and it’s very important to understand the statistical nature of data. Exploratory Data Analysis (EDA) is a critical step in the data analysis process, involving the preliminary examination of data to understand its characteristics, uncover patterns, and identify potential insights.

  • Descriptive Statistics: Measures of central tendency (mean, median, mode); Measures of dispersion (range, variance, standard deviation); Skewness and kurtosis.
  • Univariate Analysis: Histograms, frequency distributions, and kernel density plots; Box plots and violin plots; Probability density functions (PDFs) and cumulative density functions (CDFs).
  • Bivariate Analysis: Scatter plots and correlation analysis; Covariance and correlation coefficients; Pair plots and heatmaps.
  • Multivariate Analysis: PCA, Multivariate Scatter Plot, MANOVA
  • Real World Case Study

Machine Learning with Sklearn


We are going to explore the need of machine learning and its types, Algorithms when to use and how to use essential mathematical intuition along with Evaluation metrics. We will see in detail about regression algorithms.

  • Introduction to machine learning
  • Expert systems and 6 Jars
  • Supervised Learning - Regression and Classification
  • Evaluation metrics and measuring accuracy
  • Introduction to regression
  • Interpreting models
  • Feature selection
  • Regularization - Ridge and Lasso
  • Assessment

Machine Learning with Sklearn - Continued


In continuation to the ML algorithms we are going to see in detail about different classification algorithms along with mathematical intuition and evaluation metrics

  • Introduction to classification
  • Evaluation metrics - TP, FP, and AUC
  • Classification using logistic regression
  • Classification using KNN
  • Assessment

Machine Learning with Sklearn - Continued


We are going to explore classification algorithms like tree based algorithms in detail like how to interpret trees, pruning and ensemble methods like bagging and boosting

  • Introduction to decision trees
  • Building, pruning, and interpreting trees
  • Ensemble techniques - Bagging and boosting
  • Random forests
  • Boosted trees - Gradient boosting
  • Assessment

Machine Learning with Sklearn - Continued


After dealing with a lot of Supervised machine learning algorithms we will compare and get to know when to use what, Besides that we will deal with the do’s and don'ts while training an ML model.

  • Comparison of supervised techniques - when to use what?
  • Do’s and Don’ts while training ML models
  • Handling imbalanced data
  • Undersampling
  • Oversampling
  • Other methods - ROSE, SMOTE, etc.
  • Assessment

Machine Learning with Sklearn - Continued


Now we will explore Unsupervised learning algorithms, why unsupervised ?, when to use it and as well as the essential mathematical intuition

  • Introduction to unsupervised learning
  • Market Basket Analysis
  • K means algorithm
  • Assessment

Deep learning

As we move on to more complex problems, such as object recognition and text analysis, our data becomes extremely high dimensional, and the relationship becomes nonlinear. To accommodate this complexity, we move on to building more complex models that resemble our brain.

  • 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

Deep learning with PyTorch


Now that we have a better theoretical understanding of deep learning models, we will spend this module implementing some of these algorithms in PyTorch

  • 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 continued


Now that we have the basic understanding of PyTorch, we will now dive into discussing the implementation details of a few state-of-the-art deep learning architectures in PyTorch

  • Feed Forward Net: Creating basic Neural net; Load Data and train neural net; Evaluation on test set
  • CNN: Introduction; Image Filter/Image kernel; Convolution layer and RGB; Pooling Layer
  • Transfer Learning
  • Tensorboard
  • Save and Load Models

Natural Language Processing


We are going to explore Natural Language Processing (NLP). Given the fact that we have a decent understanding of Machine Learning and Deep Learning, we can now explore the powerful ways to handle the NLP use-cases

  • Language Understanding: RNNs architecture; RNNs and language models; Generation with RNNs
  • Adding more memory: LSTM architecture
  • Encoder Decoder Model with RNN
  • Self Attention Networks: Transformers
  • Hands on Huggingface: Understanding API integration
  • Using Language Models for various tasks: sentiment analysis; Question Answering; NER; Summarization

Computer Vision


Having a basic understanding of NLP use cases, now we will dive into the Computer Vision Fundamentals. We will discuss state-of-the-art CV problems and their solutions with deep learning.

  • Convolution Architecture: Filters; Stacking Multiple Feature Maps; PyTorch Implementation
  • Pooling Layers: Pytorch Implementation
  • CNN Architectures overview: LeNet-5; AlexNet; GoogLeNet; VGGNet; ResNet; Xception; SENet
  • Implementing a ResNet-34 CNN using PyTorch
  • Using pretrained models with PyTorch
  • Object Detection: Fully Convolutional Networks, YOLO
  • Semantic Segmentation

Model Deployment in AWS Cloud Platform


Having a good understanding of ML, DL and various use
cases, we will now discuss the platforms through which we can securely deploy these powerful models on production level. More specifically; we will discuss the fundamentals of AWS services and how to use them efficiently.

  • Introduction to AWS
  • Cloud Services (EC2, Lambda, S3, RDS etc)
  • Hands-on in EC2 instance
  • Hands-on in Database in AWS
  • Hands-on in S3 storage
  • Deploying ML Model as Application in AWS

Model Deployment in AWS Cloud Platform


This whole Module we are going to work on industry projects which are currently in demand in the guidance of industry experts

  • Case Study - I: Credit Card Fraud detection
  • Case Study - II: Airline Customer segmentation
  • Case Study - III: Product recommendation engine
  • Case Study - IV: Chatbot with Huggingface

Sharpen your skills in:

Enhance Your Resume with Industry Projects

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?

Become IFACET's Certified Data Science Professional

Professional Data Science Certification with Placement Guidance

Unlock Your Upskilling Journey @


Book Your Seat For Our Next Cohort

Our learners got placed in:

Achieve Success like IFACET Learners

Right Away!

Learn More About Our Professional Data Science Certification

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 IFACET for Learning Data Science?

IFACET 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.

  • IIT-K Certification

Highlight your portfolio with skill certifications from 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 IFACET 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 IFACET’ 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 IFACET 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 IFACET Data Science Program is 3 months for the weekday batch & 5 months for weekend batch learners.

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 IFACET 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 IFACET 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 IFACET 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 or +91-9219972805, +91-9219972806