Data Science With Python Certified Course

Course Description:

The Data Science with Python Certified Course is a comprehensive and hands-on program designed to equip learners with the essential tools, techniques, and concepts required to excel in the rapidly growing field of data science. This course integrates Python programming with core data science modules, enabling learners to analyze, visualize, and interpret complex data efficiently.

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 With Python:

• Data Scientist
• Data Analyst
• Machine Learning Engineer
• AI Engineer
• Business Intelligence Analyst
• Data Engineer
• Python Developer
• Quantitative Analyst

Essential Skills you will Develop Data Science With Python: 

• Python Programming Fundamentals
• Data Analysis & Manipulation
• Data Visualization
• Statistical Analysis
• Machine Learning Basics

Tools Covered:

• Data Cleaning & Preprocessing
• Open Refine / Python scripts – Data cleaning
• Regular Expressions – Pattern matching and text processing
• Beautiful Soup – Web scraping
• Requests – Accessing APIs and web data
• Database Integration
• SQL – Querying structured databases
• SQLite / PostgreSQL – Basic database operations with Python.

Syllabus:

Module 1: Introduction to Data Science & Python What is Data Science? Applications and career scope Introduction to Python programming Installing Anaconda and Notebook Python data types, variables, operators.

Module 2: Python Programming Essentials Control structures (if-else, loops)
Functions and modules Exception handling Working with files in Python Object-Oriented Programming Basics.

Module 3: Data Analysis with Number Introduction to Number  Arrays and array operations Indexing, slicing, reshaping Mathematical functions Random module and statistics.

Module 4: Data Manipulation with Pandas Introduction to Pandas
Series and Data Frame Importing/exporting data (CSV, Excel) Data wrangling: merge, group, pivot Handling missing data.

Module 5: Data Visualization Introduction Plotting graphs: line, bar, pie, histogram
Seaborn for advanced visualization Styling and customization Real-world visualization examples.

Module 6: Exploratory Data Analysis (EDA) Descriptive statistics Correlation and covariance Outliers and anomalies EDA tools and techniques Case studies with real datasets.

Module 7: Introduction to Machine Learning Supervised vs. Unsupervised Learning
Skills-learn library Regression and classification Model training, testing & evaluation
Cross-validation techniques.

Module 8: Machine Learning Algorithms Linear & Logistic Regression Decision Trees and Random Forest K-Nearest Neighbors (KNN) Support Vector Machines (SVM) Clustering with K-Means.

Module 9: Projects and Case Studies End-to-end ML project using real data Data cleaning and preprocessing Model selection and performance tuning Business problem-solving with ML.

Module 10: Capstone Project & Certification Capstone project (individual or group)
Report writing and presentation Resume and interview preparation Final assessment and certification.

Industry Project:

• Customer Segmentation
• Predicting House Prices
• Disease Prediction
• Sentiment Analysis
• Taxi Fare Prediction

Who is this program for?

• Students and Graduates
• Working Professionals
• IT and Software Developers
• Researchers and Academicians who
• Business Analysts and Statisticians

How To Apply:

Mobile: 9100348679

Email: coursedivine@gmail.com