Data Science Training
Advance Your Career with Our Data Science Training Course! Learn essential data science skills like data analysis, machine learning, data visualization, and predictive modeling through real-world projects and hands-on practice. Master tools like Python, Pandas, NumPy, and scikit-learn, and discover how data science is driving innovation in business, healthcare, finance, and technology. Build job-ready expertise to solve real data challenges and make data-driven decisions.
5 | 6 monthsCourse duration
Classroom | OnlineMode of Delivery
09Capstone projects
Why should you do this course?
Learn and grow as a developer with our project based courses.
Transform Raw Data into Insights
Learn how to build powerful web apps from scratch using MongoDB, Express, React, and Node. This course is highly practical and project-based.
Discover Insights, Drive Innovation
Master both frontend and backend technologies and become a job-ready full stack developer, capable of handling entire application flows.
Build Predictive Data-Driven Models
MERN stack is one of the most popular stacks used by top companies. This course prepares you for full stack developer roles with strong job prospects.
Unlock Career Growth in Data Science
Gain hands-on experience with Git, GitHub, REST APIs, JWT, MVC architecture, and deployment strategies used in real-world teams.
Starting from₹ 35000₹ 25000
LIVE BATCHKey Highlights
data science online coursedata science certification in delhidata science course in india
Live Projects
5 | 6 months duration
Certificate of Excellence/Completion
Placement assistance
Syllabus
Quickstart
Overview of the Journey
We offer live classes with expert instructors, weekly assignments to reinforce your learning, and fully practical training focused on real-world skills. You’ll work on hands-on projects throughout the course to build experience and confidence.
Python Programming Language
Datatype Variable & Conditional Statement
This module introduces participants to essential programming concepts, focusing on data types, variables, and conditional statements. Designed for working professionals, it provides a clear understanding of how data is stored, managed, and manipulated in modern programming. Through practical examples and hands-on exercises, participants will learn to implement decision-making logic, enabling them to write efficient, dynamic, and adaptable code for real-world business applications.
Loops and Functions
Loops and functions are essential programming tools that enhance code efficiency, readability, and reusability. This course segment equips working professionals with the skills to automate repetitive tasks using loops and structure programs through well-defined functions. Through practical, business-oriented examples, participants will learn to streamline processes, reduce errors, and develop clean, maintainable code suited for real-world applications.
File Handling & Exception Handling
File handling and exception handling are critical skills for managing data and ensuring robust, error-free applications. This course segment empowers working professionals to efficiently read, write, and manage files, while implementing exception handling techniques to gracefully manage unexpected errors. With practical scenarios and business-focused examples, participants will learn to build reliable, user-friendly programs that maintain data integrity and enhance operational stability
Object Oriented Programming
Object Oriented Programming (OOP) introduces a structured, scalable approach to software development by organizing code into reusable objects. This course segment enables working professionals to design and develop modular applications using core OOP principles like classes, objects, inheritance, encapsulation, and polymorphism. Real-world examples and practical exercises help participants apply these concepts to build efficient, maintainable, and business-ready solutions.
Webscraping
Web scraping equips professionals with the ability to extract valuable data from websites for analysis and decision-making. This course segment covers techniques to access, parse, and retrieve structured information from web pages using popular libraries, while addressing ethical considerations and best practices. Practical exercises help participants automate data collection and streamline reporting tasks for real-world business needs.
Numpy Library
NumPy is a powerful Python library designed for efficient numerical computations and data manipulation. This course section introduces working professionals to essential NumPy concepts, including arrays, mathematical operations, and advanced data processing techniques. Hands-on examples demonstrate how to handle large datasets with speed and precision, enabling data-driven solutions in business environments
Pandas Library
Pandas provides a versatile framework for data analysis and manipulation, widely used in data science and business analytics. This part of the course helps working professionals master the creation, modification, and analysis of structured data using DataFrames and Series. Through practical case studies, participants will learn to clean, transform, and analyze data effectively for insightful business reporting and decision support
Data Visualization using Seaborn & Matplot
Data visualization is a vital skill for presenting complex information clearly and effectively. This course segment introduces working professionals to Seaborn and Matplotlib, two powerful Python libraries for creating insightful, publication-quality charts and graphs. Participants will learn to visualize trends, patterns, and relationships in data through a range of plots, enabling better business reporting, storytelling, and data-driven decision-making
Python Projects
The Python project segment offers working professionals an opportunity to apply their programming knowledge to real-world business challenges. Participants will design, develop, and implement a practical solution using Python, integrating concepts like data handling, conditional logic, loops, functions, and libraries such as Pandas, NumPy, and Matplotlib. This hands-on experience strengthens problem-solving skills and prepares learners to tackle data-driven tasks confidently in professional environments.
Data Analytics Using Pandas
Analysing datasets and case studies
Data analytics using Pandas equips professionals with the ability to efficiently manage, analyze, and interpret structured data. This course segment focuses on leveraging Pandas’ powerful DataFrame and Series structures for data cleaning, transformation, aggregation, and analysis. Through practical, business-focused examples, participants will learn to extract insights from data, enabling informed decision-making and streamlined reporting in real-world scenarios
Flask Web Framework
PyTorch Introduction
Foundational overview of PyTorch, its tensors, autograd, and training models.
MYSQL Database
Basic MYSQL & Constraints
Basic MySQL and constraints form the foundation for effective database management and data integrity. This course segment introduces working professionals to essential MySQL concepts, including database creation, querying, and the implementation of constraints such as primary keys, foreign keys, and unique constraints. Through practical examples, participants will learn to design robust databases that ensure accuracy, consistency, and reliability in business applications
Data Defination Language
DDL commands are fundamental for defining and managing the structure of a database. Working professionals learn how to create, alter, and drop database objects such as tables, indexes, and schemas. Mastering DDL enables precise control over database architecture, ensuring that business data is stored efficiently and can be accessed reliably as organizational needs evolve.
Data Manipulation Language
DML focuses on the manipulation of data within database tables. This segment trains professionals to perform essential operations such as inserting, updating, deleting, and retrieving data. By mastering DML commands, participants can efficiently manage transactional data, support real-time reporting, and ensure data accuracy critical for informed business decision-making
Data Control Langauge
DCL deals with the security and permission aspects of databases. Professionals learn to grant or revoke user privileges and manage access control, safeguarding sensitive business information. Understanding DCL commands is key to maintaining data confidentiality, enforcing compliance policies, and protecting organizational assets from unauthorized access.
MYSQL GroupBy
GroupBy is a powerful data manipulation technique used to segment data into meaningful groups based on one or more keys. This allows professionals to analyze patterns, trends, and summaries within subsets of data. Mastering GroupBy enables efficient organization and comparison of business metrics, supporting deeper insights and more targeted decision-making.
Aggregate Functions
Aggregate functions perform calculations on multiple rows of data, returning summarized results such as sums, averages, counts, and maximum or minimum values. Working professionals use these functions to quickly derive meaningful statistics from large datasets. Combining aggregate functions with GroupBy operations empowers data analysts to generate concise reports and drive informed business strategies.
MySQL Joins
Joins are essential for combining data from multiple tables based on related columns, enabling comprehensive data analysis across different sources. This segment teaches working professionals how to use various types of joins—such as INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN—to merge datasets effectively. Mastering joins facilitates complex querying and supports integrated business insights by connecting disparate data points.
Sub Queries
Subqueries allow for nested queries within a larger SQL statement, providing a flexible way to perform multi-step data retrieval and filtering. Professionals learn to write subqueries to extract intermediate results that drive more sophisticated and dynamic data analysis. Utilizing subqueries enhances query efficiency and helps solve complex business questions with precision.
Windows Functions
Window functions provide advanced analytical capabilities by performing calculations across a set of table rows related to the current row without collapsing the result into a single output. This technique enables working professionals to compute running totals, ranks, moving averages, and other cumulative metrics within partitions of data. Mastery of window functions allows for sophisticated data analysis and reporting, offering deeper business insights while maintaining detailed row-level information.
Database Connectivity
Python connectivity with MYSQL
Database connectivity enables applications to interact seamlessly with databases, facilitating data retrieval, manipulation, and storage in real-time. This course segment equips working professionals with the skills to establish secure connections between programming environments and databases using standard interfaces and drivers. Mastering database connectivity is essential for developing dynamic applications and ensuring smooth data integration within business workflows
Git & GitHub
Version Control with Git
Learn the fundamentals of Git, including how to track changes, manage repositories, and handle code versions effectively. Understand commands like init, add, commit, log, and diff
Branching and Merging
Explore how to work with multiple branches for feature development, handle merge conflicts, and use branching strategies for team collaboration and clean code workflows.
Collaborating with GitHub
Master using GitHub for remote repositories, pull requests, and collaboration. Learn how to fork projects, contribute to open-source, and use issues, wikis, and project boards.
Introduction to Data Visualization
Introduction to Data Visualization
Understand the importance of data visualization in data science. Learn the principles of creating clear, accurate, and compelling visual stories from data.
Visualization with Matplotlib & Seaborn
Use Python libraries like Matplotlib and Seaborn to create bar charts, histograms, line plots, scatter plots, and heatmaps. Learn customization techniques for colors, labels, and themes.
Advanced Visualizations & Dashboarding
Explore advanced visualizations like pair plots, violin plots, and correlation matrices. Get introduced to interactive tools like Plotly and Dash for creating basic dashboards.
Statistics & Probability
Descriptive Statistics
Learn how to summarize and describe data using measures like mean, median, mode, variance, standard deviation, and data distribution techniques.
Probability Fundamentals
Understand core probability concepts such as independent/dependent events, conditional probability, Bayes’ Theorem, and their applications in data science.
Inferential Statistics
Explore hypothesis testing, confidence intervals, z-tests, t-tests, and p-values. Learn how to draw conclusions and make predictions based on sample data
Predictive Modeling
Understanding the Predictive Modeling Workflow
Overview of supervised, unsupervised, and reinforcement learning paradigms.
Linear & Logistic Regression
Overview of supervised, unsupervised, and reinforcement learning paradigms.
Model Evaluation & Validation
Overview of supervised, unsupervised, and reinforcement learning paradigms.
Supvervised Machine Learning
Introduction to Supervised Learning
Overview of deep learning and its place within machine learning.
Popular Algorithms: Decision Trees, KNN, SVM
Overview of deep learning and its place within machine learning.
Hyperparameter Tuning & Model Optimization
Overview of deep learning and its place within machine learning.
Unsupervised Machine Learning
Introduction to Unsupervised Learning
Learn the RL paradigm of agents learning through environment interaction to maximize rewards.
Clustering Algorithms: K-Means & Hierarchical Clustering
Learn the RL paradigm of agents learning through environment interaction to maximize rewards.
Dimensionality Reduction Techniques
Learn the RL paradigm of agents learning through environment interaction to maximize rewards.
Bagging Boosting and Stacking
Bagging Techniques (Bootstrap Aggregation)
Learn how bagging helps reduce variance by combining multiple models. Understand the working of ensemble methods like Random Forest and their use in classification and regression.
Boosting Algorithms
Explore powerful boosting techniques like AdaBoost, Gradient Boosting, and XGBoost. Understand how they sequentially improve weak learners for higher accuracy.
Stacking Models
Understand the concept of stacking (stacked generalization) to combine predictions from multiple models. Learn how to use meta-models for improved performance.
Feature Engineering
Data Cleaning & Missing Value Treatment
Introduction to GANs and adversarial training for synthetic data generation.
Feature Transformation & Scaling
Introduction to GANs and adversarial training for synthetic data generation.
Feature Selection & Extraction
Introduction to GANs and adversarial training for synthetic data generation.
Cross Validation & Imbalance Dataset
Cross Validation Techniques
Learn how to validate model performance using K-Fold, Stratified K-Fold, and Leave-One-Out Cross Validation. Understand how these techniques help in reducing overfitting.
Understanding Class Imbalance
Explore the challenges of working with imbalanced datasets. Understand metrics like precision, recall, F1-score, and why accuracy can be misleading.
Handling Imbalanced Data
Apply practical methods like SMOTE (Synthetic Minority Over-sampling Technique), Random Over/Under Sampling, and class weighting to balance the data and improve model performance.
Natural Language Processing
Text Preprocessing Techniques
Learn how to clean and prepare text data using tokenization, stopword removal, stemming, lemmatization, and text normalization techniques.
Text Representation: Bag of Words & TF-IDF
Understand how to convert text into numerical form using Bag of Words and Term Frequency–Inverse Document Frequency (TF-IDF) for use in machine learning models.
Introduction to NLP Models & Applications
Explore basic NLP models for text classification, sentiment analysis, and spam detection. Get introduced to modern tools like spaCy and NLTK for real-world NLP tasks.
Model Optimization & Deployement
Model Tuning and Performance Improvement
Learn hyperparameter tuning techniques like Grid Search, Random Search, and Bayesian Optimization to enhance model performance and accuracy.
Model Serialization
Understand how to save and load trained models using libraries like joblib and pickle for reuse in real-world applications and pipelines.
Deployment Using Flask & Streamlit
Build simple APIs and web apps to deploy machine learning models using Flask and Streamlit. Learn how to integrate models into real-time user interfaces.
Linkedin and Resume Building
Creating a Job-Ready Resume
Learn how to craft a clean, professional, and ATS-friendly resume tailored for data science roles. Focus on structuring projects, technical skills, and achievements effectively.
Optimizing LinkedIn Profile
Understand how to build a strong LinkedIn presence with an impactful headline, summary, featured projects, and endorsements to attract recruiters and networking opportunities.
Showcasing Projects & Certificates
Learn how to highlight real-world projects, GitHub links, certifications, and portfolios on both your resume and LinkedIn to stand out to potential employers.
Mock Interview Preparation
Technical Interview Practice
Prepare for real-world data science interviews through coding challenges, algorithm-based questions, and scenario-based discussions on Python, SQL, ML, and statistics.
HR & Behavioral Interview Rounds
Get trained on answering common HR questions, storytelling techniques (STAR method), and presenting your resume and background with confidence and clarity.
Live Mock Interviews & Feedback
Participate in mock interviews conducted by industry professionals. Receive personalized feedback on your strengths, improvement areas, and overall interview readiness.
Why choose Datadrix?
Learn and grow as a developer with our project based courses.
Superb mentors
Best in class mentors from top Tech schools and Industry favorite Techies are here to teach you.
Industry-vetted curriculum
Best in class content, aligned to the Tech industry is delivered to you to ensure you Tech industry.
Project based learning
Hands on learning pedagogy with live projects to cover practical knowledge over theoretical one.
Superb placements
Result oriented courses across all genres, students as well as Working professionals.
Project based learning
Hands on learning pedagogy with live projects to cover practical knowledge over theoretical one.
Superb placements
Result oriented courses across all genres, students as well as Working professionals.
Certificate of completion
Joining DATADRIX means you'll create an amazing network, make new connections, and leverage diverse opportunities.
“Validate Your Expertise and Propel Your Career”
Expand Opportunities: Certifications to unlock new career opportunities, gain credibility with employers, and open doors to higher-level positions.
Continuous Growth: Certifications not only validate your current skills but also encourage continuous learning and professional development, allowing you to stay updated with the latest industry trends and advancements.
Certification: A testament to your skills and knowledge, certifications demonstrate your proficiency in specific areas of expertise, giving you a competitive edge in the job market.
Our Alumni's Are Placed At
See what students have to say
Joining DATADRIX means you'll create an amazing network, make new connections, and leverage diverse opportunities.
I joined Datadrix to learn Python and Data Engineering. Thanks to Om Arora for simplifying coding concepts and providing practical projects to work on.
Datadrix Institute helped me build a solid base in Python and Data Science. Special thanks to Nitin Shrivastav for his clear and practical teaching.
Thanks to Datadrix’s Data Analytics program, I cracked my interview confidently. Nitin Shrivastav’s sessions were insightful and very practical.
Loved learning Python and Data Science here. Datadrix has the best trainers and projects. Special thanks to Om Arora for his real-world examples.
Finally cracked my second job in data science after Datadrix’s training. Nitin Shrivastav’s SQL and Power BI sessions boosted my confidence.
Datadrix Institute made learning Web Development super fun! Om Arora’s support and practical project work made the course so much more valuable.
The Data Analytics course by Datadrix Institute was worth it. Nitin Shrivastav’s explanations on tools like Excel and Power BI made it easy.
Datadrix's Data Science program gave me clarity on statistics and ML. Om Arora explained tough topics in a very simple and relatable way.
Big thanks to Datadrix for helping me master Python programming. Nitin Shrivastav’s approach to teaching made coding fun and easy to follow.
Datadrix's Data Science program gave me clarity on statistics and ML. Nitin sir explained tough topics in a very simple and relatable way.
The Data Analytics course at Datadrix helped me land my job as a data analyst. Nitin Shrivastav’s clear and patient teaching style stood out.
The Python programming training was perfect for beginners. Thanks to Nitin Shrivastav for always clearing doubts patiently and giving real projects.
Frequently Asked Questions
Learn and grow as a developer with our project based courses.
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