Apache Pyspark Training
Accelerate Big Data Processing with Our Apache PySpark Training Course! Master distributed data processing using PySpark — the powerful Python API for Apache Spark. This hands-on course covers data manipulation, ETL pipelines, real-time analytics, and machine learning integration on big data platforms. Learn to handle large datasets efficiently and build scalable data solutions for industries like finance, retail, and tech.
1 | 2 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.
Master Distributed Data Processing
Learn how to build powerful web apps from scratch using MongoDB, Express, React, and Node. This course is highly practical and project-based.
Unlock Careers in Data Processing
Master both frontend and backend technologies and become a job-ready full stack developer, capable of handling entire application flows.
Lead Big Data Projects Confidently
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.
Build Scalable Data Pipelines
Gain hands-on experience with Git, GitHub, REST APIs, JWT, MVC architecture, and deployment strategies used in real-world teams.
Starting from₹ 16000₹ 11000
LIVE BATCHKey Highlights
Apache Spark courselearn Apache Spark onlinebest Apache Spark training
Live Projects
1 | 2 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
Basics
Introduction to Python basics like syntax, variables, conditionals, loops, and standard I/O.
Functions
Covers defining and calling functions, along with advanced topics like decorators and generators.
Builtin Data Structures
Explores Python’s core data structures like lists, tuples, sets, and dictionaries.
Object Oriented Programming Models
Covers class creation, inheritance, polymorphism, and encapsulation in Python.
File and Error Handling
Learn how to read/write files and handle errors using Python's try-except-finally blocks.
Iteration Protocol and Generator
Delves into Python’s iteration protocol and building custom iterators/generators.
Asynchronous Programming in Python
Introduces async/await syntax for writing non-blocking code and working with coroutines.
Git & GitHub
Basics of Git/GitHub
Learn version control using Git, and how to collaborate on code using GitHub.
Data Acquisition and Web Automation
Data Acquisition Web Scraping
Learn how to extract structured data from websites using tools like BeautifulSoup.
Data Acquisition using Web API
Covers making HTTP requests to public APIs and parsing the JSON/XML responses.
Data Acquisition Web Crawler using Scrapy
Introduction to Scrapy for building web crawlers that can crawl multiple pages automatically.
Web Automation
Learn how to automate browser actions using Selenium for tasks like testing and scraping.
Mathematical Fundamentals
Numpy
Fundamentals of NumPy for numerical computing and array manipulations.
Linear Algebra
Essential linear algebra topics such as vectors, matrices, eigenvalues, and matrix operations.
Two Dimensional Dynamic Programming
Covers dynamic programming techniques commonly used in algorithmic problem solving.
Data Visualisation
Explore data visualization libraries like Matplotlib and Seaborn to plot and understand data.
Pandas
Hands-on with the Pandas library for data wrangling, manipulation, and analysis.
Probability Distribution & Statistics
Covers fundamental statistical measures and probability distributions used in data science.
Machine Learning Algorithm
Getting Started with Machine Learning
Overview of supervised, unsupervised, and reinforcement learning paradigms.
K-Nearest Neighbours
Learn KNN algorithm for classification and regression with distance-based logic.
Linear Regression
Simple linear regression for predicting continuous outcomes using a single feature.
Linear Regression II multiple features
Extend linear regression to multiple features using vectorized implementation.
ScikitLearn Introduction
Quick introduction to the Scikit-learn library to build and evaluate ML models.
Optimisation Algorithms
Gradient descent and other iterative methods for optimizing ML models.
Locally Weighted Regression (LOWESS)
Non-parametric regression technique using local neighborhood of data points.
Maximum Likelihood Estimation (MLE)
Parameter estimation technique used for statistical modeling and inference.
Logistic Regression
Classification algorithm for binary outcomes using the sigmoid function.
Data Preprocessing and Feature Selection
Techniques like normalization, missing value handling, and feature selection methods.
Principal Component Analysis
Dimensionality reduction technique to tackle the curse of dimensionality.
Natural Language Processing & Naive Bayes
Text processing techniques and Naive Bayes for text classification.
Decision Tree & Random Forests
Tree-based models for classification and regression, and ensemble learning.
Support Vector Machines
Powerful supervised learning model for linear and non-linear classification.
Clustering Fundamentals
Unsupervised learning techniques like K-Means and hierarchical clustering.
Deep Learning
Deep Learning Introduction
Overview of deep learning and its place within machine learning.
Neural Networks MLP's
Multilayer perceptrons and how to build basic feedforward neural networks.
Convolutional Neural Network
CNNs for image processing and visual tasks like classification and detection.
Training Data Loaders, Augmentation, Colab
Use PyTorch DataLoaders and augmentations to prepare data for deep learning models.
Digging Deeper into Convnets
Advanced convolutional layers, architectures, and model tuning.
Transfer Learning
Using pre-trained models to accelerate training and improve accuracy on small datasets.
Markov Chains for Text Generation
Introduction to probabilistic text generation using Markov models.
Recurrent Neural Networks
RNNs for sequential data like time series and natural language.
Word Embeddings Word2Vec
Use Word2Vec for capturing semantic meaning of words in vector space.
Reinforcement Learning
Introduction to Reinforcement Learning
Learn the RL paradigm of agents learning through environment interaction to maximize rewards.
Generative Models
Generative Adversarial Networks
Introduction to GANs and adversarial training for synthetic data generation.
Deep Convolutional GANs
Implementation of GANs with CNNs to generate realistic images.
PyTorch
PyTorch Introduction
Foundational overview of PyTorch, its tensors, autograd, and training models.
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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