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Courses for Data Science Certification
- Get Trained by Trainers from IIT, ISB & IIM
- 184+ Hours of Live Classroom or Online Sessions
- 3 Capstone Live Projects
- Free Life-Time eLearning Access
- 100% Free Internship
- 100% Job Placement Assurance
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Data Science Training Program
Data science training empowers individuals with proficiency in data analysis, machine learning, and data visualization. It covers programming languages, statistical analysis, and ethical data handling practices. Through hands-on projects and a strong emphasis on Python programming, participants acquire the essential skills needed for data science positions. The program also offers opportunities for specialization in fields such as natural language processing and computer vision, opening doors to a wide range of career prospects.
Course Objectives
The specific objectives of a data science course can vary depending on the level, duration, and focus of the course. However, here are some common course objectives for a data science program:
Understanding Data Concepts:
- To introduce students to fundamental data concepts, such as structured and unstructured data, big data, data sources, and data formats.
Data Collection and Preprocessing:
- To teach students how to gather and clean data, including techniques for data cleaning, data transformation, and handling missing values.
Exploratory Data Analysis (EDA):
- To provide the skills to perform EDA to understand the characteristics and patterns in data, using descriptive statistics, data visualization, and data summarization techniques.
Statistical and Mathematical Foundations:
- To build a strong foundation in statistics and mathematics, including probability, hypothesis testing, and regression analysis, which are crucial for data analysis.
Machine Learning:
- To introduce machine learning techniques, including supervised and unsupervised learning algorithms, and teach students how to apply them to real-world problems.
Data Visualization:
- To teach data visualization techniques and tools to communicate data insights effectively through charts, graphs, and dashboards.
Feature Engineering:
- To train students on feature selection and extraction techniques to improve the performance of machine learning models.
Model Evaluation and Validation:
- To show how to assess the performance of models and techniques for cross-validation and hyperparameter tuning.
Big Data and Distributed Computing:
- To introduce concepts and technologies related to big data processing, including distributed computing frameworks like Hadoop and Spark.
Deep Learning and Neural Networks:
- To delve into advanced topics such as deep learning and neural networks, including their applications and implementations.
Certification Courses
Data Science
AI & Deep Learning
Data Analytics
Success Stories Of Students
What Leaners Say About Us
Excellent way of teaching ,each and every topic is covered with good understanding -Best for beginners who is changing their domain in this field. - Revision in every class.
