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Data Science Complete Course Vip Hills, Hyderabad - Learnees

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Data Science Complete Course Vip Hills, Hyderabad

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Data Science Complete Course Vip Hills, Hyderabad

Master Data Science with Python, SQL, Statistics, Machine Learning, and Data Visualization through practical learning. Learn to analyze real-world data, build predictive models, and generate data-driven insights. Develop job-ready skills through hands-on projects, industry tools, and real-world applications.

Educator

Live

Classroom (Offline)

STARTS ON

15th Jun

DURATION

6 months

LANGUAGE

English / Hindi

360+ Students Enrolled via Learnees

⚡24/7 Support by Learnees

1. Introduction to Data Science

Topics:

Data Science Fundamentals
Data Science Lifecycle
AI vs ML vs Data Science
Types of Data
Structured & Unstructured Data
Data Science Applications
Industry Use Cases.

Hands-on:

Skills You Will Learn:

2. Python for Data Science

Topics:

Python Fundamentals
Variables & Data Types
Operators
Conditional Statements
Loops
Functions
Lists
Tuples
Sets
Dictionaries
Exception Handling
File Handling.

Hands-on:

Skills You Will Learn:

3. Python Libraries for Data Science

Topics:

NumPy
Pandas
Matplotlib
Seaborn
DataFrames
Series
Array Operations
Data Manipulation
Data Visualization.

Hands-on:

Skills You Will Learn:

4. SQL & Database Management

Topics:

SQL Fundamentals
Databases
Tables
SELECT Queries
WHERE Clause
ORDER BY
GROUP BY
Aggregate Functions
Joins
Subqueries
CTEs
Window Functions
Data Filtering.

Hands-on:

Skills You Will Learn:

5. Statistics for Data Science

Topics:

Descriptive Statistics
Mean
Median
Mode
Variance
Standard Deviation
Probability
Distributions
Normal Distribution
Sampling
Hypothesis Testing
Correlation
Regression.

Hands-on:

Skills You Will Learn:

6. Data Cleaning & Preprocessing

Topics:

Data Collection
Data Quality
Missing Values
Duplicate Data
Outlier Detection
Data Transformation
Encoding
Feature Scaling
Data Validation
Exploratory Data Analysis.

Hands-on:

Skills You Will Learn:

7. Exploratory Data Analysis (EDA)

Topics:

EDA Fundamentals
Univariate Analysis
Bivariate Analysis
Multivariate Analysis
Data Profiling
Correlation Analysis
Pattern Identification
Business Insights.

Hands-on:

Skills You Will Learn:

8. Data Visualization

Topics:

Charts & Graphs
Bar Charts
Line Charts
Pie Charts
Histograms
Box Plots
Scatter Plots
Heatmaps
Dashboard Design
Interactive Visualization.

Hands-on:

Skills You Will Learn:

9. : Machine Learning Fundamentals

Topics:

Machine Learning Concepts
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Training & Testing Data
Features & Labels
Model Training
Model Evaluation.

Hands-on:

Skills You Will Learn:

10. Supervised Machine Learning

Topics:

Linear Regression
Multiple Linear Regression
Logistic Regression
Decision Trees
Random Forest
K-Nearest Neighbors
Support Vector Machines
Naive Bayes.

Hands-on:

Skills You Will Learn:

11. Unsupervised Machine Learning

Topics:

Clustering
K-Means
Hierarchical Clustering
DBSCAN
Dimensionality Reduction
PCA
Association Rules
Anomaly Detection.

Hands-on:

Skills You Will Learn:

12. Machine Learning Model Evaluation

Topics:

Confusion Matrix
Accuracy
Precision
Recall
F1 Score
ROC-AUC
Mean Absolute Error
Mean Squared Error
Cross Validation
Hyperparameter Tuning
Overfitting
Underfitting.

Hands-on:

Skills You Will Learn:

13. Feature Engineering

Topics:

Feature Selection
Feature Extraction
Feature Transformation
Encoding Techniques
Scaling Techniques
Polynomial Features
Feature Importance
Dimensionality Reduction.

Hands-on:

Skills You Will Learn:

14. Deep Learning Fundamentals

Topics:

Neural Networks
Artificial Neurons
Activation Functions
Forward Propagation
Backpropagation
Loss Functions
Optimizers
TensorFlow
Keras
PyTorch Basics.

Hands-on:

Skills You Will Learn:

15. Generative AI Fundamentals

Topics:

Generative AI Concepts
Generative AI Applications
LLM Fundamentals
Transformers
Tokens & Tokenization
Embeddings
Context Windows
AI Models
Generative AI Tools.

Hands-on:

Skills You Will Learn:

16. Prompt Engineering

Topics:

Prompt Engineering Fundamentals
Prompt Structure
Zero-Shot Prompting
Few-Shot Prompting
Role-Based Prompting
Chain-of-Thought
Structured Prompts
Prompt Optimization
Prompt Evaluation.

Hands-on:

Skills You Will Learn:

17. LLMs for Data Science

Topics:

LLM-Assisted Data Analysis
AI-Powered SQL
AI Code Generation
Automated Data Cleaning
Data Summarization
Insight Generation
AI-Assisted Visualization
LLM APIs.

Hands-on:

Skills You Will Learn:

18. RAG & AI Data Applications

Topics:

RAG Fundamentals
Document Processing
Text Chunking
Embeddings
Vector Databases
Semantic Search
Knowledge Bases
RAG Pipeline
Data Question-Answering Systems.

Hands-on:

Skills You Will Learn:

19. Agentic AI for Data Science

Topics:

Agentic AI Fundamentals
AI Agents
Agent Architecture
Tool Calling
Function Calling
Data Analysis Agents
Research Agents
SQL Agents
Autonomous Data Workflows
Multi-Agent Systems.

Hands-on:

Skills You Will Learn:

20. AI-Powered Data Science Projects

Topics:

Sales Data Analysis
Customer Segmentation
Predictive Analytics
Churn Prediction
Recommendation System
AI Data Analyst
RAG Data Assistant
SQL AI Agent
End-to-End Gen AI Data Science Project.

Hands-on:

Skills You Will Learn:

21. Deployment & MLOps

Topics:

Model Deployment
Streamlit
FastAPI
REST APIs
Git & GitHub
Model Serialization
Cloud Deployment Basics
MLOps Fundamentals
Model Monitoring
Version Control.

Hands-on:

Skills You Will Learn:

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Start From

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What you'll learn in Data Science Complete Course Vip Hills, Hyderabad

Course Specifications21

Introduction to Data Science

Python for Data Science

Python Libraries for Data Science

SQL & Database Management

Data Cleaning & Preprocessing

Exploratory Data Analysis (EDA)

Data Visualization

Machine Learning Fundamentals

Supervised Machine Learning

Unsupervised Machine Learning

Machine Learning Model Evaluation

Feature Engineering

Deep Learning Fundamentals

Generative AI Fundamentals

Prompt Engineering

LLMs for Data Science

RAG & AI Data Applications

Agentic AI for Data Science

AI-Powered Data Science Projects

Deployment & MLOps

Career & Interview Preparation

Course Key Components4

Study material

PYQ & Practice test

Performance analysis

1:1 doubt clearing session

Curriculum / Syllabus Overview By Experts

Master the skills employers are looking for with our comprehensive, hands-on curriculum

Introduction to Data Science

7 Topics

Python for Data Science

12 Topics

Python Libraries for Data Science

9 Topics

SQL & Database Management

13 Topics

Statistics for Data Science

13 Topics

Free Career Counselling

We're happy to help you 24/7

1. Introduction to Data Science

Topics:

Data Science Fundamentals
Data Science Lifecycle
AI vs ML vs Data Science
Types of Data
Structured & Unstructured Data
Data Science Applications
Industry Use Cases.

2. Python for Data Science

Topics:

Python Fundamentals
Variables & Data Types
Operators
Conditional Statements
Loops
Functions
Lists
Tuples
Sets
Dictionaries
Exception Handling
File Handling.

3. Python Libraries for Data Science

Topics:

NumPy
Pandas
Matplotlib
Seaborn
DataFrames
Series
Array Operations
Data Manipulation
Data Visualization.

4. SQL & Database Management

Topics:

SQL Fundamentals
Databases
Tables
SELECT Queries
WHERE Clause
ORDER BY
GROUP BY
Aggregate Functions
Joins
Subqueries
CTEs
Window Functions
Data Filtering.

5. Statistics for Data Science

Topics:

Descriptive Statistics
Mean
Median
Mode
Variance
Standard Deviation
Probability
Distributions
Normal Distribution
Sampling
Hypothesis Testing
Correlation
Regression.

6. Data Cleaning & Preprocessing

Topics:

Data Collection
Data Quality
Missing Values
Duplicate Data
Outlier Detection
Data Transformation
Encoding
Feature Scaling
Data Validation
Exploratory Data Analysis.

7. Exploratory Data Analysis (EDA)

Topics:

EDA Fundamentals
Univariate Analysis
Bivariate Analysis
Multivariate Analysis
Data Profiling
Correlation Analysis
Pattern Identification
Business Insights.

8. Data Visualization

Topics:

Charts & Graphs
Bar Charts
Line Charts
Pie Charts
Histograms
Box Plots
Scatter Plots
Heatmaps
Dashboard Design
Interactive Visualization.

9. : Machine Learning Fundamentals

Topics:

Machine Learning Concepts
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Training & Testing Data
Features & Labels
Model Training
Model Evaluation.

10. Supervised Machine Learning

Topics:

Linear Regression
Multiple Linear Regression
Logistic Regression
Decision Trees
Random Forest
K-Nearest Neighbors
Support Vector Machines
Naive Bayes.

11. Unsupervised Machine Learning

Topics:

Clustering
K-Means
Hierarchical Clustering
DBSCAN
Dimensionality Reduction
PCA
Association Rules
Anomaly Detection.

12. Machine Learning Model Evaluation

Topics:

Confusion Matrix
Accuracy
Precision
Recall
F1 Score
ROC-AUC
Mean Absolute Error
Mean Squared Error
Cross Validation
Hyperparameter Tuning
Overfitting
Underfitting.

13. Feature Engineering

Topics:

Feature Selection
Feature Extraction
Feature Transformation
Encoding Techniques
Scaling Techniques
Polynomial Features
Feature Importance
Dimensionality Reduction.

14. Deep Learning Fundamentals

Topics:

Neural Networks
Artificial Neurons
Activation Functions
Forward Propagation
Backpropagation
Loss Functions
Optimizers
TensorFlow
Keras
PyTorch Basics.

15. Generative AI Fundamentals

Topics:

Generative AI Concepts
Generative AI Applications
LLM Fundamentals
Transformers
Tokens & Tokenization
Embeddings
Context Windows
AI Models
Generative AI Tools.

16. Prompt Engineering

Topics:

Prompt Engineering Fundamentals
Prompt Structure
Zero-Shot Prompting
Few-Shot Prompting
Role-Based Prompting
Chain-of-Thought
Structured Prompts
Prompt Optimization
Prompt Evaluation.

17. LLMs for Data Science

Topics:

LLM-Assisted Data Analysis
AI-Powered SQL
AI Code Generation
Automated Data Cleaning
Data Summarization
Insight Generation
AI-Assisted Visualization
LLM APIs.

18. RAG & AI Data Applications

Topics:

RAG Fundamentals
Document Processing
Text Chunking
Embeddings
Vector Databases
Semantic Search
Knowledge Bases
RAG Pipeline
Data Question-Answering Systems.

19. Agentic AI for Data Science

Topics:

Agentic AI Fundamentals
AI Agents
Agent Architecture
Tool Calling
Function Calling
Data Analysis Agents
Research Agents
SQL Agents
Autonomous Data Workflows
Multi-Agent Systems.

20. AI-Powered Data Science Projects

Topics:

Sales Data Analysis
Customer Segmentation
Predictive Analytics
Churn Prediction
Recommendation System
AI Data Analyst
RAG Data Assistant
SQL AI Agent
End-to-End Gen AI Data Science Project.

21. Deployment & MLOps

Topics:

Model Deployment
Streamlit
FastAPI
REST APIs
Git & GitHub
Model Serialization
Cloud Deployment Basics
MLOps Fundamentals
Model Monitoring
Version Control.

Why Choose TechU Innovation Labs VIP Hills, Hyderabad

Discover the unique advantages that make our learning platform stand out

24x7 Support

One-On-One Learning Assistance
Help Desk Support
Resolve Doubts in Real-time

Hands-On Project Based Learning

Industry-Relevant Projects
Course Demo Dataset & Files
Quizzes & Assignments

Industry Recognised Certification

Learnees Training Certificate
Graded Performance Certificate
Certificate of Completion

Data Science Complete Course Vip Hills, Hyderabad Certification

Master Skills & Earn Your Professional Certificate

Industry-recognized certificate by Learnees Institute

Learn from certified trainers with hands-on sessions

Receive career support and placement guidance

Globally recognized certification for career growth

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Career Opportunities

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Why enroll for Data Science Complete Course Vip Hills, Hyderabad ?

Unlock your potential with Data Science Complete Course Vip Hills, Hyderabad

Most Popular Certification

Data Science Complete Course Vip Hills, Hyderabad certification is rated as one of the most popular and lucrative cloud certifications in IT globally.

Growing Market

The global Data Science Complete Course Vip Hills, Hyderabad market size is expected to grow at a CAGR of 21.2% from 2024 to 2030.

High Salary Potential

The estimated total pay for an Data Science Complete Course Vip Hills, Hyderabad is ₹7,09,000 per year, with an average salary of ₹6,09,000 per year - Glassdoor.

Course Overview: Data Science Complete Course Vip Hills, Hyderabad

Essential details: objectives, prerequisites, schedule, and outcomes

Build a strong foundation in Python, SQL, Statistics, Data Analysis, and Data Visualization. Learn to develop and evaluate Machine Learning models using real-world datasets. Gain practical, job-ready Data Science skills through hands-on projects and industry applications.