Curriculum Designed By Experts
Expertly designed curriculum for future-ready professionals.
Industry Oriented Curriculum
An exhaustive curriculum designed by our industry experts which will help you to get placed in your dream IT company
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30+  Case Studies & Projects
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9+  Engaging Projects
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10+   Years Of Experience
Data Science Training Projects
Become a Data Science Expert With Practical and Engaging Projects.
- Practice essential Tools
- Designed by Industry experts
- Get Real-world Experience
Sentiment Analysis of Tweets
Perform sentiment analysis on tweets using natural language processing (NLP) techniques. Classify tweets as positive, negative, or neutral using libraries like NLTK and TextBlob.
Movie Recommendation System
Build a simple recommendation system using collaborative filtering techniques on a movie dataset. Use user ratings to predict and recommend movies to users.
Exploratory Data Analysis on a Public Dataset
Use a public dataset, such as from Kaggle, to perform basic data cleaning and exploratory data analysis (EDA). Visualize data trends, distributions.
Sales Forecasting
Develop a time series model to forecast sales for a retail store. Use historical sales data and techniques like ARIMA or LSTM networks for predictions.
Credit Card Fraud Detection
Build a classification model to detect fraudulent credit card transactions. Use techniques like logistic regression, decision trees, or random forests to identify anomalies in transaction data.
Churn Prediction
Predict customer churn for a telecom company using classification algorithms. Analyze customer behavior and demographics to identify factors leading to churn.
Reinforcement Learning for Game Playing
Implement a reinforcement learning agent to play and master a game, such as Tic-Tac-Toe, Chess, or Go. Use frameworks like OpenAI Gym.
Deep Learning for Image Generation with GANs
Create a Generative Adversarial Network (GAN) to generate realistic images from noise. Train the GAN on a dataset of images.
Speech Recognition System
Develop a speech recognition model to convert spoken language into text. Use deep learning techniques, recurrent neural networks (RNNs).
Key Features
Practical Training
Global Certifications
Flexible Timing
Trainer Support
Study Material
Placement Support
Mock Interviews
Resume Building
Batch Schedule
Weekdays Regular (Class 1Hr - 1:30Hrs) / Per Session
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11-Nov-2024 Starts Coming Monday ( Monday - Friday) 08:00 AM (IST)
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13-Nov-2024 Starts Coming Wednesday ( Monday - Friday) 10:00 AM (IST)
Weekend Regular (Class 3Hrs) / Per Session
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09-Nov-2024 Starts Coming Saturday ( Saturday - Sunday) 10:00 AM (IST)
Weekend Fast-track (Class 6Hrs - 7Hrs) / Per Session
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10-Nov-2024 Starts Coming Saturday ( Saturday - Sunday) 10:00 AM (IST)
Enquiry Form
- Learning strategies that are appropriate and tailored to your company's requirements.
- Live projects guided by instructors are a characteristic of the virtual learning environment.
- The curriculum includes of full-day lectures, practical exercises, and case studies.
Data Science Training Overview
Future works for Data Science Development
Working with top businesses that employ data science specialists provides doors to rewarding job prospects and professional development. These elite businesses, renowned for their creativity and industry leadership, are looking for highly qualified data scientists to lead their data-driven initiatives and help them make wise decisions. You may access cutting-edge projects and cutting-edge technology by working with such firms, which will broaden your expertise and skill set. Working with top companies gives you access to industry best practices, great networking opportunities, and the chance to help shape solutions that have an influence on a variety of industries, such as technology, healthcare, and finance. This partnership helps you grow professionally and puts you at the vanguard of data science advancements, where your knowledge will influence how data-driven decision-making is made in the future.
Advantages of Enrolling in Our Data Science Development Course
- Comprehensive Curriculum: Our course offers a well-rounded curriculum covering essential data science topics, from fundamental concepts to advanced techniques.
- Hands-On Experience: Take advantage of practical, hands-on projects and real-life examples that enable learners to put your theoretical understanding to genuine data science challenges.
- Expert Instructors: Learn from experienced instructors with extensive industry experience. Our faculty members bring real-world insights and expertise, providing valuable mentorship and guidance throughout the course.
- Industry-Relevant Tools and Technologies: Gain proficiency in the latest data science tools and technologies, including Python, R, SQL, and machine learning libraries like TensorFlow and scikit-learn.
- Career Support and Networking Opportunities: Take advantage of our dedicated career support services, including resume building, interview preparation, and job placement assistance.
Future works for Data Science Development
The future of data science development is primed for dramatic growth, thanks to technological developments and changing industrial demands. As data volume and complexity grow, data science will increasingly rely on cutting-edge technologies like computer vision (AI), deep learning (ML), and sophisticated statistical analysis to extract useful insights from massive datasets. Automation innovations and the integration of real-time data processing will improve decision-making skills in a variety of areas, including healthcare, banking, and marketing. The integration of edge computing and the World Wide Web of Things (IoT) are going to expand data sources, allowing for more accurately and rapid analytics.
Essential Tools for Data Science Development
- Python: Python is a flexible and extensively used programming language in data science, noted for its ease of use and strong libraries like Pandas, NumPy, and SciPy. It is necessary for data processing, statistical analysis, machine learning, and data visualization.
- R: R is a sophisticated computer language used in data visualization and statistical computation. It is an essential tool for data scientists, particularly in academic and research environments, since it provides comprehensive packages for sophisticated statistical analysis, data processing, and graphical depiction.
- Jupyter Notebooks: An interactive environment for scripting, data processing, and visualization is offered by Jupyter Notebooks. Through an integrated interface that supports live code, equations, and visualizations, they enable data scientists to publish their discoveries, interact with others, and document their workflow.
- Tableau: One of the best tools for data visualization is Tableau, which lets users make dashboards that are shared and interactive. Its user-friendly drag-and-drop interface facilitates the visualization of large, complicated data sets, which makes it simpler to identify patterns, trends, and insights.
Career Opportunities After Data Science Training
Data Scientist
Data scientists conduct in-depth analyses of large, complicated data sets to find patterns that inform strategic choices. To understand patterns, create prediction models.
Data Analyst
To assist firms in making wise decisions, data analysts concentrate on reading, analyzing, and interpreting data. They find patterns, produce reports.
Machine Learning Engineer
Machine learning engineers create and use models and algorithms that let computers learn from experience and get better over time.
Data Engineer
The infrastructure necessary for data collecting, storage, and analysis is created and maintained by data engineers.
Business Intelligence Analyst
Business Intelligence (BI) Analysts leverage data to provide insights that inform business strategies and decisions.
Quantitative Analyst
Quantitative Analysts, or “Quants,” apply mathematical and statistical models to financial data to inform investment strategies.
Skill to Master
Programming Languages
Statistical Analysis
Data Wrangling
Data Visualization
Machine Learning
Big Data Tools
Data Mining
Database Management
Cloud Computing
Business Acumen
Communication Skills
Ethics and Data Privacy
Tools to Master
Python
R
Jupyter Notebook
Pandas
NumPy
Scikit-learn
TensorFlow
Tableau
Apache Hadoop
Apache Spark
SQL
Power BI
Learn from certified professionals who are currently working.
Training by
Abhishek , having 7+ yrs of experience
Specialized in: Data Science, AI & Machine Learning, Python
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We are proud to have participated in more than 40,000 career transfers globally.
Data ScienceCertification
While holding a Data Science certification can significantly improve your job prospects, it does not guarantee employment. Employers also consider factors such as practical experience, soft skills, and cultural fit.
Yes, you can pursue multiple Data Science course certifications. This can be advantageous as it allows you to diversify your skill set and demonstrate expertise in various areas of data science. Multiple certifications can make you more versatile and attractive to potential employers.