DATA SCIENCE COURSE

A Data Science course is designed to equip students and aspiring data scientists with the knowledge and practical skills needed to analyze, interpret, and visualize complex data. It focuses on extracting meaningful insights and enabling data-driven decision-making using modern tools and techniques. Read more

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  • Experienced Experts
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Live Projects
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Course HighlightsDATA SCIENCE COURSE

A Data Science course is designed to provide students or aspiring data scientists with the essential knowledge and practical skills needed to analyze, interpret, and extract meaningful insights from complex datasets. These courses typically cover a broad range of topics including statistics, programming, machine learning, data visualization, and data management.

Participants learn how to collect, clean, and preprocess data to make it suitable for analysis, handling challenges such as missing values, outliers, and inconsistent entries. Core concepts in statistics and probability are emphasized to understand data patterns, distributions, and to make informed data-driven decisions. Additionally, learners gain hands-on experience with tools and techniques for data exploration, modeling, and visualization, preparing them for real-world applications across industries.

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Course Curriculum

  • Introduction to Data Science & Evolution of Data Science
  • Difference between Data analyst & Data Scientist
  • Roles & Responsibilities of Data Scientist
  • Difference between Supervised & Unsupervised learning
  • What is machine learning
  • Deep learning and Artificial Intelligence

  • Probability
  • Bayesian Inference
  • Hypothesis testing
  • Descriptive Statistics
  • Inferential Statistics
  • Hypothesis testing
  • Statistical Distribution(Discrete and continuous)

  • Understanding the core principles of information security (Confidentiality, Integrity, Availability).
  • How to work with Jupiter notebook and Spyder IDE’s
  • Python Data type

  • What is linear regression?
  • What is logistic regression?
  • Difference between Linear and Logistic
  • Difference between Regression and Classification
  • Building a model using Linear and Logistic Regression

  • What is clustering?
  • Difference between K-means and KNN
  • Different Use cases of clustering
  • Building a model using K-means and KNN

  • Data Exploration and Cleaning
  • Optimization Techniques
  • Natural Language Processing (NLP)

  • Data Wrangling
  • Time Series Analysis
  • Big Data Processing
  • Model Deployment
  • Data Ethics and Privacy

  • Descriptive and Inferential Statistics
  • Classification
  • Data Ethics and Privacy
  • Data Storytelling

  • Data Science Tools
  • Domain Knowledge
  • Ensemble Methods
  • A/B Testing

Professional Certificate

Beginner Level

No previous experience required

Course Duration

20 Days | 1 Hour per day

Flexible Schedule

Learn at your own pace

Certification

Receive a professional DATA SCIENCE certificate upon completion

Course Overview

Explore the key features, skills, and job roles covered in this DevOps course

Course Key Features

Emphasizes collaboration and communication between development and operations teams.

Focuses on continuous integration, continuous deployment, and automation.

Skills Covered
  • Automation
  • Version Control
  • Scripting & Coding
Job Roles
  • DevOps Engineer
  • Release Engineer
  • Build Engineer

Training FAQs

Everything you need to know about our training program

This training aims to equip participants with practical DevOps skills, enhance collaboration between development and operations teams, and prepare them for real-world IT projects.

This training is suitable for IT professionals, developers, system administrators, and beginners looking to build a strong foundation in DevOps practices and tools.

No strict prerequisites are required. Basic understanding of programming concepts and familiarity with IT systems is recommended to make the most of the course.