Data Science
Turn Data Into Insights. Build Your Data Science Skills.
Learn Python, Statistics, Machine Learning and Natural Language Processing through structured online training with practical learning and project-based experience.
Duration:
50 Sessions
Mode:
Online
Level:
Beginner to Intermediate
Course Fee:
₹35,000
📘 COURSE OVERVIEW
Build Your Foundation in Data Science
The Data Science course is designed to help learners develop practical knowledge in Python, Statistics, Statistical Modeling, Machine Learning and Natural Language Processing (NLP).
The training begins with Python fundamentals and data handling, then progresses into statistical concepts, machine learning algorithms, model evaluation, overfitting and regularization, followed by NLP concepts and applications.
The course also includes project work, recorded class videos, interview discussions and doubt-clearing sessions.
🎯 COURSE OBJECTIVES
By the end of the course, learners will develop knowledge of:
✔️ Python programming fundamentals
✔️ NumPy and Pandas
✔️ Data visualization
✔️ Descriptive & Inferential Statistics
✔️ Linear & Logistic Regression
✔️ Machine Learning algorithms
✔️ Model evaluation techniques
✔️ Overfitting & Regularization
✔️ Tree-based algorithms
✔️ Unsupervised Machine Learning
✔️ Natural Language Processing
✔️ Text Classification & Sentiment Analysis
✔️ Topic Modeling
✔️ Data Science project implementation
📚 COURSE CURRICULUM
🤖 INTRODUCTION TO MACHINE LEARNING
- Introduction to Machine Learning
- Machine Learning Fundamentals
- Overview of Machine Learning Workflow
🐍 PYTHON FOR DATA SCIENCE
Python Fundamentals
- Introduction to Python
- Basic Data Structures
- Slicing & Dicing Data Structures
- Loops
- if / elif / else Conditions
- User-Defined Functions
- Lambda Functions
Data Manipulation
- Map
- Apply
- Applymap
- NumPy Arrays
- Pandas Series
- Pandas DataFrames
Data Visualization
- Visualization Techniques
- Data Analysis Through Visualization
📊 STATISTICS
Descriptive Statistics
- Understanding Data
- Measures of Central Tendency
- Data Distribution
- Descriptive Analysis
Inferential Statistics
- Introduction to Inferential Statistics
- Drawing Insights From Sample Data
- Statistical Interpretation
📈 STATISTICAL MODELING
Linear Regression
- Introduction to Linear Regression
- Assumptions
- Evaluation of Linear Models
- Data Preparation
- Model Fitting
- Feature Prediction
Logistic Regression
- Introduction to Logistic Regression
- Assumptions
- Classification Concepts
- Confusion Matrix
Model Evaluation
- Accuracy
- Precision
- Recall
- F1 Score
- AUC
- ROC Curve
⚙️ OVERFITTING & UNDERFITTING
- Overfitting
- Underfitting
- Identifying Overfitting
- Handling Overfitting
- Regularization
- Model Generalization
🌳 MACHINE LEARNING MODELS
Tree-Based Algorithms
- Decision Tree
- Random Forest
- Gradient Boosting Classifier
- XGBoost
- LightGBM
Other Machine Learning Algorithms
- K-Nearest Neighbors (KNN)
- Support Vector Machine (SVM)
💻 PROJECT
Practical Data Science Project
Apply the concepts learned throughout the course through project-based learning.
Minimum Project Duration: 3 Hours
The project section can help learners understand how different Data Science concepts are brought together in a practical workflow.
SSH Access Issues
- Resource Limitations
- Basic Troubleshooting Approach
AWS Billing & Cost Management
- AWS Pricing Fundamentals
- Usage Tracking
- Budget Monitoring
- Budget Alarms
🔬 Hands-On Lab
✔️ Monitor EC2 performance
✔️ View CloudWatch metrics
✔️ Create a CPU utilization alarm
🔵 UNSUPERVISED MACHINE LEARNING
Topics
- Introduction to Unsupervised Learning
- Unsupervised Machine Learning Algorithms
- Working with Unlabelled Data
- Practical Applications
💬 NATURAL LANGUAGE PROCESSING (NLP)
Introduction to NLP
- What is Natural Language Processing?
- NLP Applications
- NLP Workflow
Libraries
- spaCy
- NLTK
Text Processing
- Tokenization
- Stemming
- Lemmatization
Using spaCy & NLTK
Phrase Matching
- Phrase Matching Using spaCy
NLP Applications
- Text Classification
- Sentiment Analysis
- Topic Modeling
💻 PRACTICAL LEARNING
Learn by Practicing Data Science
🐍 Python Practice
Work with Python data structures, functions and data manipulation.
📊 Data Analysis
Use NumPy and Pandas to work with datasets.
📈 Statistical Modeling
Build and evaluate statistical models.
🤖 Machine Learning
Explore different machine learning algorithms and model evaluation techniques.
💬 NLP Practice
Work with text processing and NLP concepts.
💻 Project Work
Apply your learning through a practical Data Science project.
- Tools & Technologies
- 🐍 Python
- 🔢 NumPy
- 🐼 Pandas
- 📊 Data Visualization
- 🤖 Machine Learning
- 💬 spaCy
- 📝 NLTK
- 📈 Statistical Modeling
👥 WHO IS THIS COURSE FOR?
Ideal For
🎓 Students
💻 IT Beginners
📊 Aspiring Data Science Professionals
🐍 Python Learners
🔄 Career Switchers
- Frequently Asked Questions
What is the duration of the Data Science course?
The course consists of 50 training sessions.
Do I need Python knowledge before joining?
No advanced Python knowledge is required. Python fundamentals are included in the course.
Which topics are covered in Machine Learning?
The course covers Linear Regression, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, XGBoost, LightGBM, KNN, SVM and Unsupervised Machine Learning.
Is project work included?
Yes. The curriculum includes a practical project with a minimum duration of 3 hours.
Is placement assistance available?
No. LearnCircles currently provides online training only and does not offer placement assistance.