Data Science Training
Master data-driven decision-making with practical, project-based learning on real-world datasets
- Gain expertise in essential tools: Python, SQL, Machine Learning, Power BI, Tableau, and Statistics
- Attend interactive live sessions, both online and offline, guided by top industry experts
- Guaranteed placement support through our career advancement services
- Learn key skills including data cleaning, data exploration, predictive analytics, and business intelligence
World-Class Instructors
1:1 with Industry Mentors
55% Avg. Salary Hike
Interview Preparation
What You’ll Learn Learn
Accelerate your career in data with our Data Science Training at TechPragna in Bangalore. This comprehensive course equips you with the skills and techniques to collect, clean, analyze, and model data using advanced statistical methods and machine learning—perfect for both beginners and working professionals looking to build a successful data science career
Data Science Training – Key Features
- Master Essential Tools: Python, SQL, Power BI, Tableau, Excel, and Scikit-learn
- Live online and offline interactive sessions from top industry professionals
- Guaranteed placement support through our career advancement services
- Benefit from guaranteed placement assistance with dedicated career support services.
- Receive guaranteed placement assistance with resume building and interview preparation
- Attend online and offline sessions led by top data science industry experts.
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Data Science Course Curriculum
Module 1: Introduction to Data Science
What is Data Science and Its Importance
Data Science Lifecycle (CRISP-DM)
Key Roles in Data Science (Analyst, Engineer, Scientist)
Tools and Technologies Overview (Python, R, SQL)
Real-World Applications of Data Science
Career Paths and Certifications
Ethical Considerations in Data Science
Setting Up Your Data Science Environment
Module 2: Python for Data Science
Python Basics (Syntax, Data Types)
Data Structures (Lists, Dictionaries, Pandas DataFrames)
NumPy for Numerical Computing
Pandas for Data Manipulation
Data Visualization with Matplotlib and Seaborn
Introduction to Scikit-Learn
Working with APIs and Web Scraping
Lab: Data Cleaning and Analysis with Python
Module 3: Statistics for Data Science
Descriptive Statistics (Mean, Median, Variance)
Probability Distributions (Normal, Binomial)
Hypothesis Testing (t-tests, Chi-square)
Correlation and Regression Analysis
Bayesian Statistics Basics
A/B Testing and Experimental Design
Statistical Significance and p-values
Lab: Statistical Analysis with Real Data
Module 4: Data Wrangling and Cleaning
Handling Missing Data
Removing Duplicates and Outliers
Data Transformation (Normalization, Standardization)
Feature Engineering Techniques
Text Data Cleaning (NLP Basics)
Time Series Data Handling
Data Integration from Multiple Sources
Lab: Cleaning a Messy Dataset
Module 5: Exploratory Data Analysis (EDA)
Importance of EDA
Univariate and Multivariate Analysis
Visualization Techniques (Histograms, Box Plots)
Identifying Patterns and Trends
Correlation and Heatmaps
Dimensionality Reduction (PCA)
Interactive Visualization with Plotly
Lab: EDA on a Real-World Dataset
Module 6: Machine Learning Fundamentals
Introduction to Machine Learning
Supervised vs. Unsupervised Learning
Linear and Logistic Regression
Decision Trees and Random Forests
Model Evaluation Metrics (Accuracy, Precision, Recall)
Overfitting and Underfitting
Cross-Validation Techniques
Lab: Building Your First ML Model
Module 7: Advanced Machine Learning
Support Vector Machines (SVM)
Clustering Algorithms (K-Means, Hierarchical)
Natural Language Processing (NLP) Basics
Neural Networks and Deep Learning Intro
Ensemble Methods (Bagging, Boosting)
Hyperparameter Tuning (Grid Search, Random Search)
Model Interpretability (SHAP, LIME)
Lab: Advanced ML Project
Module 8: Big Data and Cloud Computing
Introduction to Big Data (Hadoop, Spark)
Working with Large Datasets (Dask, Modin)
Cloud Platforms for Data Science (AWS, GCP, Azure)
SQL and NoSQL Databases
Distributed Computing Basics
Data Pipelines and ETL Processes
Real-Time Data Processing
Lab: Big Data Analysis with Spark
Module 9: Data Visualization and Storytelling
Principles of Effective Data Visualization
Tools (Tableau, Power BI, Python Libraries)
Dashboard Design Best Practices
Storytelling with Data
Interactive Dashboards
Geospatial Data Visualization
Avoiding Misleading Visuals
Lab: Creating a Data Dashboard
Module 10: Deployment and Model Production
Introduction to MLOps
Model Deployment (Flask, FastAPI)
Containerization with Docker
CI/CD for Data Science
Monitoring Model Performance
A/B Testing for Models
Scalability and Performance Optimization
Lab: Deploying a Machine Learning Model
Module 11: Special Topics in Data Science
Time Series Forecasting
Recommender Systems
Computer Vision Basics
Reinforcement Learning Intro
Ethical AI and Bias Mitigation
AutoML Tools
Case Studies from Industry
Research Project: Cutting-Edge Techniques
Capstone Project and Career Preparation
End-to-End Data Science Project
Building a Data Science Portfolio
Resume and LinkedIn Optimization
Technical Interview Preparation
Communication Skills for Data Scientists
Networking in Data Science Communities
Freelancing and Remote Work Opportunities
Final Project Presentation and Feedback
Master In-Demand Skills with Practical, Industry-Based Learning
What Role Does a Data Science Professional Play?
Data Analyst
Collects, processes, and interprets data to generate actionable insights through reports and dashboards that support business decisions.
Business Analyst
Bridges the gap between business objectives and data by analyzing processes and recommending data-driven solutions to improve performance
Data Scientist
Applies advanced statistical techniques, machine learning, and predictive modeling to extract insights, forecast trends, and solve complex problems
Data Engineer
Builds and maintains scalable data pipelines and architectures, ensuring reliable data collection, cleaning, and availability for analysis
Machine Learning Engineer
Develops, tests, and deploys machine learning models into production environments to automate decision-making and predictive tasks.
Data Science Consultant
Advises organizations on best practices, strategies, and technologies to harness data science for business growth and innovation.
Skills Covered
Data Cleaning
Statistical Analysis
Advanced Excel
SQL Querying
Data Visualization
Python Programming
Exploratory Analysis
Machine Learning
Data Storytelling
Business Intelligence
12+ Data Science Tools Covered
Career Services
Placement Assistance
Personalized Guidance
Mock Interview Preparation
One-on-One Mentoring session
Career Oriented Seesions
Resume & LinkedIn Profile Building
How our program works
Enhance Your Skills to Transform Your Career Path
- Gain official proof of your expertise from a trusted institution, increasing your value in the eyes of employers and clients.
- Stand out in competitive job markets and unlock better job roles, promotions, or freelance opportunities.
- Certifications ensure your skills are aligned with current industry standards, making you job-ready from day one.
- Completing a certified course strengthens your belief in your capabilities and readiness to tackle real-world challenges.
- Join a professional community, gain access to hiring managers, and receive support from placement partners and mentors.
Data Science Projects Covered
Land Your Dream Job Our Alumni
Data Science Training FAQs
What are the prerequisites for learning Data Science?
Answer:
Basic math (statistics, linear algebra)
Programming (Python or R)
SQL for databases
No strict prerequisites for beginners; many courses start from scratch.
Which is better for Data Science: Python or R?
Answer:
Python: Versatile, better for production and ML.
R: Strong in statistical analysis and visualization.
Most professionals use Python for its broader ecosystem (TensorFlow, PyTorch).
Do I need a degree to become a Data Scientist?
Answer:
No! Many Data Scientists come from non-traditional backgrounds. Employers value:
Skills (Python, ML, SQL)
Portfolio projects
Certifications (e.g., Google Data Analytics, IBM Data Science)
What tools/languages should I learn first?
Answer:
Python (Pandas, NumPy, Scikit-learn)
SQL (PostgreSQL, MySQL)
Visualization (Matplotlib, Tableau)
Big Data (Spark, Hadoop).
What’s the difference between Data Analyst and Data Scientist?
Answer:
Data Analyst: Focuses on descriptive analytics (SQL, Excel, dashboards).
Data Scientist: Builds predictive models (Python, ML, advanced stats).
Is this course available online or offline?
Tech Pragna are offered in both online and offline
What industries hire Data Scientists?
Answer:
Tech, healthcare, finance, e-commerce, marketing, and more. High demand in:
Finance: Fraud detection
Healthcare: Predictive diagnostics
Retail: Recommendation systems.
What Our Learners Have To Say
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