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Curriculam Designed By Experts
Expertly designed curriculum for future-ready professionals.
Industry Oriented Curriculam
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
Iris Dataset Classification
Irises can be divided into several categories: petal length, width, and length. Preprocessing the data, fundamental classification techniques (such as K-nearest neighbours and logistic regression), and search criteria.
Titanic Survival Prediction
Utilize variables like fare, passenger class, age, and gender to estimate how long individuals on the Titanic will live. Data cleansing, binary classification, feature engineering, and model analysis.
Movie Recommendation System
Create a user-based movie recommendation system. demographics using collaborative filtering or content-based techniques. Basic recommendation algorithms, similarity measurement, and data preprocessing.
Customer Segmentation
To divide your client base into distinct groups according to purchasing patterns or other pertinent attributes, use clustering algorithms like K-Means or DBSCAN techniques for clustering, dimensionality reduction, and data preprocessing.
Fraud Detection
Use anomaly detection or classification algorithms to look for fraudulent transactions in financial data sets. Feature engineering, anomaly detection, and classification algorithms.
Churn Prediction
Use historical data and a range of predictive modeling approaches to forecast user attrition for a subscription-based business.
Image Classification with Deep Learning
Classify photos into categories to demonstrate deep learning (e.g., MNIST digit recognition or CIFAR-10 object classification). Deep learning methods (such as TensorFlow and PyTorch) and convolutional neural networks (CNNs).
Predictive Maintenance for Industrial Equipment
Utilizing sensor data and machine learning algorithms, forecast device malfunctions or maintenance requirements. Feature engineering, anomaly detection, and time series prediction.
Automated Machine Learning (AutoML) System
Create an AutoML application that does not require human assistance to train images, choose features, or preprocess data. Model optimization, hyperparameter tuning, and pipelines for machine learning.
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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16-Sep-2024 Starts Coming Monday ( Monday - Friday) 08:00 AM (IST)
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11-Sep-2024 Starts Coming Wednesday ( Monday - Friday) 10:00 AM (IST)
Weekend Regular (Class 3Hrs) / Per Session
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14-Sep-2024 Starts Coming Saturday ( Saturday - Sunday) 10:00 AM (IST)
Weekend Fast-track (Class 6Hrs - 7Hrs) / Per Session
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14-Sep-2024 Starts Coming Saturday ( Saturday - Sunday) 10:00 AM (IST)
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- 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
Data Science Programmer’s Potential Career Paths
Different career routes are available to data science programmers, and each presents a unique opportunity to use their skills. One of their roles might be data analysis, which assesses data to produce actionable insights for business choices. As data engineers, they organize and manage data pipeline procedures to guarantee data quality and availability. Machine learning engineers create AI apps and prediction systems using sophisticated models and algorithms. With many trials and critical analyses, data scientists use statistical analysis and machine learning to solve complicated issues.
What Are the Requirements for an Data Science Course Course?
- Basic Education: At minimum, a high school diploma or its equivalent is required for most data science courses. Typically, specific graduate or degree programs may call for a bachelor's degree in a related field like computer science, accounting, mathematics, engineering, or finance.
- Prerequisite Knowledge: Basic knowledge of statistics, calculus, and algebra. Familiarity with computer languages like R or Python. Specific courses might presuppose a fundamental comprehension of coding principles.
- Programming Skills: In data science, programming skills are a must. You should be competent in computer languages like Python and R, frequently used in data research. It's also beneficial to be familiar with libraries and software like PyTorch, Pandas, NumPy, Scikit-learn, TensorFlow, and NumPy Problem-Solving.
- Problem-Solving Abilities: Powerful analytical and problem-solving abilities to understand facts and draw conclusions. Capacity to effectively convey discoveries and understandings to both technical and non-technical stakeholders.
Reasons to Consider Enrolling in Data Science Course
Enroll in a data science course to elevate your career possibilities and remain competitive in today's data-driven world. The focus is on obtaining meaningful insights from intricate data; these courses offer a systematic approach to developing fundamental skills in data analysis, machine learning, and programming. They typically combine practical and problem-solving abilities with real-world applications to enhance understanding. Networking possibilities, coaching, and testimonials that improve your resume and make it more appealing to employers are other advantages of customized courses.
Techniques and Trends in Data Science Development
- Machine Learning and Deep Learning: Overlabeled data is processed using algorithms like support vector machines and regression trees. Techniques like dimensionality reduction and clustering can identify patterns in unlabeled data. Neural networks, especially convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are highly effective at tasks like speech recognition and image processing.
- Natural Language Processing (NLP): Human speech is interpreted and produced using sentiment analysis, named entity recognition, and topic modelling approaches. By facilitating more complex comprehension and text production, sophisticated models like BERT and GPT are transforming natural language processing.
- Big Data Technologies: The simplicity and efficiency of extensive data handling across machine clusters with frameworks like Apache Hadoop and Apache Spark provide a reassuring practicality. Databases that manage unstructured data well and offer flexible schema settings are MongoDB and Cassandra
- Data Visualization: Data visualization that is interactive and intelligent is made possible by programs like Tableau, Power BI, and D3.js. Techniques for employing advanced plots, including heatmaps and network graphs, to visualize complex data structures and trends.
The Most Recent Data Science Tools
The newest data science instruments aim to boost output, expandability, and insight production. Intelligent artificial intelligence (AI) platforms, including Google's Bard and OpenAI's GPT-4, facilitate sophisticated data creation and examination. Databricks and Snowflake are examples of data engineering technologies that help with scalable storage solutions and flexible data management. H2O.ai and Google AutoML are two examples of autoML platforms that make model generation and optimization easier. Integrated development environments (IDEs) like VS Code and JupyterLab offer strong coding and data visualization capabilities.Furthermore, with improved networking features, data visualization tools like Tableau and Power BI are becoming more popular. DVC and mellow offer reliable ways to control machine learning performance and interpretation. These resources work together to support data science business growth and innovation.
Career Opportunities After Data Science Training
Data Scientist
Data scientists examine extensive, complicated data sets to obtain valuable insights and support decision-making. They employ statistical approaches, machine learning algorithms.
Machine Learning Engineer
A machine learning specialist uses machine learning models and algorithms to create prediction algorithms and artificial intelligence (AI) apps. They concentrate on making algorithms more efficient.
Data Analyst
The importance of gleaning insightful information from complex A data analyst's job is to evaluate and analyze intricate details so businesses can make wise judgments. This position entails gathering, analyzing.
Data Engineer
Data engineers build, manage, and maintain data pipelines and infrastructure. They guarantee smooth data transfer from various sources to data warehouses or pools, enhancing dependability and efficiency. Proficiency in big data technologies like Hadoop or Spark, SQL and ETL.
Business Intelligence (BI) Analyst
A business intelligence analyst concentrates on analyzing corporate data to enhance strategic decision-making and boost productivity. They provide intelligent reports on important company metrics and trends, execute ad hoc analytics, and create and manage BI tools and dashboards.
Quantitative Analyst (Quant)
A quantitative analyst creates statistical models to assess financial markets, control risk, and direct investment strategies. Statistical techniques are also applied to forecast market trends and generate business plans. Proficiency in programming, statistical analysis.
Skill to Master
Statistical Analysis
Programming
Data Cleaning and Preprocessing
Machine Learning
Data Visualization
Big Data Technologies
Data Engineering
Natural Language Processing (NLP)
Database Management
Data Storytelling
Problem-Solving and Critical Thinking
Business Acumen
Tools to Master
Python
R
Jupyter Notebook
SQL
Tableau
Power BI
Apache Spark
Hadoop
TensorFlow
Keras
Git
D3.js
Learn from certified professionals who are currently working.
Training by
Rahul , having 8 yrs of experience
Specialized in: Python Programming, Data Manipulation, Machine Learning, and Algorithm Development.
Note: Rahul is an expert user of pandas and Scikit-learn, two of Python's data science packages. His experience in machine learning and algorithm development helps students gain the useful abilities required for efficient data analysis and modelling.
We are proud to have participated in more than 40,000 career transfers globally.
Data ScienceCertification
By confirming your abilities and competencies in a rapidly expanding field, obtaining a data science certification can significantly improve your employment prospects. Employers can see you possess the technical know-how and real-world experience to tackle challenging data-driven problems.
Although obtaining a data science certification validates your knowledge and skills and improves your professional prospects, it does not guarantee employment. The ability to execute pertinent duties, your practical experience, and how well you meet potential employers’ needs are all critical factors in landing a job, yet having a certification can provide you with a competitive advantage at work.
The data science certification exam usually requires