Description

 Course Description;

This Data Science has become increasingly popular in recent years. These courses provide a convenient and flexible way for individuals to acquire the skills and knowledge needed to excel in the field of data science, which is a rapidly growing and highly sought-after area of expertise.

Key Features of Course Divine:

• Collaboration with E‑Cell IIT Tirupati
• 1:1 Online Mentorship Platform
• Credit-Based Certification
• Live Classes Led by Industry Experts
• Live, Real-World Projects
• 100% Placement Support
• Potential Interview Training
• Resume-Building Activities

Career Opportunities After Data Science:

• Data Scientist
• Data Analyst
• Machine Learning Engineer
• Business Intelligence (BI) Analyst
• AI Engineer
• Data Engineer
• Statistician
• Big Data Engineer

Essential Skills you will Develop Data Science:

• Statistical Analysis & Probability
• Data Wrangling & Cleaning
• Machine Learning
• Data Visualization
• Big Data Technologies

Tools Covered:

• Python – Most popular for data analysis, machine learning, visualization.

• R – Especially strong in statistical analysis.

• SQL – Essential for data querying and database operations.

Syllabus:

Module 1: Introduction to Data Science What is Data Science? Lifecycle of Data Science Projects Roles: Data Analyst, Data Engineer, Data Scientist
Tools Overview: Python, Jupyter, Git, SQL.

Module 2: Python for Data Science Python Basics: Variables, Data Types, Loops, Functions Number for Numerical Computing Pandas for Data Manipulation
Seaborn for Visualization.

Module 3: Data Wrangling & Cleaning Handling Missing Data Transformation & Normalization Outlier Detection & Treatment Feature Engineering Techniques.

Module 4: Exploratory Data Analysis (EDA) Descriptive Statistics
Data Visualization Techniques Correlation Analysis Dashboarding with.

Module 5: Databases and SQL Relational Databases & Data Models SQL Basics: SELECT, JOIN, GROUP BY, HAVING Working with PostgreSQL / MySQL Integration with Python.

Module 6: Statistics for Data Science Probability & Distributions Hypothesis Testing
Confidence Intervals Central Limit Theorem.

Module 7: Machine Learning – Supervised Learning Regression Models: Linear, Logistic Classification Algorithms: KNN, SVM, Decision Trees Model Evaluation: Confusion Matrix, ROC, AUC.

Module 8: Machine Learning – Unsupervised Learning Clustering: K-Means, Hierarchical Dimensionality Reduction: PCA Association Rules Anomaly Detection.

Module 9: Introduction to Deep Learning Basics of Neural Networks Tens or Flow &  Introduction Feedforward & Convolutional Neural Networks Real-life Applications.

Module 10: Capstone Project & Deployment End-to-End Data Science Project Model Deployment with Flask GitHub Portfolio Setup Resume & Interview Preparation.

Industry Projects:

• Predictive Maintenance
• Credit Risk Modeling
• Customer Segmentation
• Demand Forecasting

Who is this program for?

• Students and Recent Graduates
• Working Professionals
• IT Professionals
• Researchers and Academicians

 How To Apply:

Mobile: 9100348679

Email: coursedivine@gmail.com