Data Science Course

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Data science is a multidisciplinary field that leverages statistical analysis, programming, and domain expertise to extract valuable insights from large and complex datasets. It involves collecting, cleaning, and processing data to discover patterns, trends, and correlations. Machine learning and artificial intelligence are often used to build predictive models and automate decision-making processes.

Course Overview

Data Science has revolutionized industries by enabling data-driven decision-making and automation. It combines statistics, machine learning, and programming to extract valuable insights from data. The demand for skilled data scientists is growing rapidly, making Data Science certification courses highly valuable for career advancement. Our Data Science course provides a comprehensive understanding of data analysis, visualization, machine learning, and AI, using an extensive and well-structured curriculum. This course equips learners with the essential skills needed to tackle real-world data challenges efficiently and effectively.

Why Learn Data Science?

Widely used

Describe what data science and machine learning are, their applications & use cases, and various types of tasks performed by data scientists

Lucrative Salary

The average salary for a Data Science is 6 LPA+ according to Indeed.

Create with Ease

Create your own customizable, full-featured applications using the flexible and reliable interface of the DataScience

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DataScience Training Syllabus

Module 1: Lists & Tuples
Module 2: Dictionary & Sets
Module 3: Numpy Fundamentals
Module 4: Data Manipulation with Pandas & Data Visualization
Module 5: Introduction to SQL & Basic Querying
Module 6: Advanced SQL Concepts & Data Manipulation
Module 7: Fundamentals of Statistics & Probability

Accordion Content

Module 8: Advanced Statistics & Hypothesis Testing

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Module 9: Introduction to Machine Learning and Regression Basics

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Module 10: Multiple Linear Regression & Model Evaluation

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Module 11: Logistic Regression and Classification Metrics

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Module 12: Support Vector Machines (SVM) and K-Nearest Neighbors (KNN)

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Module 13: Decision Trees & Ensemble Methods

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Module 14: Model Evaluation & Validation Techniques

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Module 15: Django / Flask

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Module 16: Recommendation Engines
Module 17: Unsupervised Learning
Module 18: Time Series Modelling with ARIMA and SARIMA
Module 19: Introduction to Deep Learning
Module 20: Deep Learning Architectures and Training
Module 21: Open CV (Computer Vision)

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Module 22: Natural Language Processing (NLP)

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Module 23: Data Science Project-1

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Module 24: Data Science Project-2

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Module 25: Power BI

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Module 26: Tableau

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Module 27: Introduction to Generative AI, Transformers & LLMs

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Module 28: Training and Fine-Tuning LLMs

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Module 29: Advanced Fine-Tuning and Model Evaluation

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Module 30: Building a Real-Life Chatbot

Accordion Content

Languages & Tools Covered in Data Science course

Weekdays

1hr/day

10Am to 7Pm

45 day

Online/Offline

Weekdays

1hr/day

10Am to 7Pm

45 day

Online/Offline

Industry recognized & government approved DataScience certification

Angular JS was built on the concept of efficiency of performance and fast responses, and it has been very useful in single-page application development, because it is easy to use and completely free, making it accessible to programmers across the world.

Frequently Asked Questions

1. What is Data Science?

Data Science is a field that combines statistical methods, programming skills, and domain expertise to analyze and interpret complex data. It helps organizations make data-driven decisions and solve real-world problems.

2. Do I need programming knowledge to start this course?

While prior programming experience is helpful, it is not mandatory. The course includes a beginner-friendly introduction to Python, one of the most commonly used languages in Data Science.

3. How long does it take to complete this Data Science course?

The course typically takes 4 to 6 months, depending on your learning pace and the schedule of the training program.

4. What tools and technologies will I learn in this course?

You will learn Python, SQL, Tableau, Power BI, and Big Data tools like Hadoop and Spark, along with cloud platforms such as AWS or Google Cloud for data science applications.

5. What career opportunities can I pursue after completing the course?

You can pursue roles such as Data Scientist, Data Analyst, Machine Learning Engineer, Business Analyst, or AI Specialist across industries like finance, healthcare, e-commerce, and technology.

 
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