100% Placement Assistance
Master Data Science Course
- Learn Machine Learning & AI Concepts
- Master Python, SQL & Data Visualization
- Work with Real-World Datasets & Models
- Build Predictive Models & Data Solutions
Top Performers Get Job Support & a Chance to Work with Wynara Analytics!
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Understanding Data Science
What is Data Science?
Data Science is the field that combines statistics, programming, and business knowledge to extract meaningful insights from large and complex datasets.
It helps organizations understand trends, predict outcomes, and make smarter, data-driven decisions.
By using tools like Python, SQL, Power BI, and Machine Learning algorithms, Data Science transforms raw data into actionable insights that drive innovation and growth across industries such as business, healthcare, finance, and technology.
In simple terms —
Data Science turns data into decisions.
What Does a Data Scientist Do?
A Data Scientist collects, analyzes, and interprets large volumes of data to identify trends, patterns, and insights that help organizations make data-driven decisions.
They use programming languages such as Python and SQL, along with statistical models and machine learning techniques, to solve real-world business problems.
A Data Scientist is also responsible for cleaning and preparing data, building predictive models, and visualizing results through reports and dashboards.
In simple terms, a Data Scientist transforms raw data into actionable insights that guide business growth and innovation.
Who Can Learn Data Science?
Data Science is open to everyone — whether you're starting your career or looking to upskill in one of the most in-demand tech fields.
Students
Learn the fundamentals of programming, statistics, and machine learning to start your career in data science.
Working Professionals
Upskill to transition into roles such as Data Analyst, Data Scientist, or Machine Learning Engineer.
Entrepreneurs
Leverage data insights to make smarter business decisions, forecast trends, and optimize operations.
Career Changers
Shift from non-technical or traditional roles into the data science field with hands-on, practical training.
Data Science Program
Choose your learning mode — Offline or Online. Both include hands-on training and a paid internship.
Offline Program
Classroom-based learning
Program Duration
7 Months Training + 5 Months Paid Internship
Total Duration: 12 Months
Batch Timings (Weekdays)
9:00 AM – 11:00 AM
7:00 PM – 9:00 PM
Online Program
Live instructor-led sessions
Program Duration
7 Months Training + 5 Months Paid Internship
Total Duration: 12 Months
Batch Timings (Weekdays)
9:00 AM – 11:00 AM
7:00 PM – 9:00 PM
Data Science Training Process
A step-by-step journey designed to transform your data science skills into real-world expertise.
Project-Based Training
Practical Assignments
Paid Internship
100% Job Assistance
Data Science Technologies & Tools
Python Programming
Overview: Python is one of the most popular and beginner-friendly programming languages, widely used for data analysis, automation, web development, and machine learning. Its simplicity and vast ecosystem of libraries make it an essential tool for every data professional.
Key Topics Covered:
- Python Basics (Data Types, Variables, Loops, and Functions)
- Working with Lists, Tuples, Dictionaries, and Sets
- File Handling and Exception Management
- Introduction to Libraries: NumPy, Pandas, and Matplotlib
- Writing Modular and Reusable Code
Practical Exercises:
- Write Python Programs for Data Manipulation
- Perform Exploratory Data Analysis using Pandas
- Create Simple Data Visualizations with Matplotlib
Data Analysis using Python
Overview: Data Analysis with Python involves using powerful libraries and tools to collect, clean, process, and visualize data. It helps uncover hidden patterns, correlations, and insights for informed business decision-making. Python’s flexibility and rich ecosystem make it a top choice for analysts and data scientists.
Key Topics Covered:
- Data Importing and Cleaning with Pandas
- Exploratory Data Analysis (EDA) Techniques
- Data Aggregation, Grouping, and Merging
- Data Visualization using Matplotlib and Seaborn
- Handling Missing Values and Outliers
- Feature Engineering for Machine Learning
Practical Exercises:
- Analyze Real-World Datasets using Pandas
- Create Visual Reports and Dashboards with Seaborn
- Perform Sales and Customer Behavior Analysis
Advanced Statistics
Overview:Advanced Statistics provides the foundation for data-driven decision-making and predictive modeling. It focuses on understanding data distributions, relationships, and variability through rigorous mathematical and analytical techniques. This knowledge is crucial for building reliable models and interpreting real-world data.
Key Topics Covered:
- Probability Distributions and Hypothesis Testing
- Sampling Methods and Confidence Intervals
- Analysis of Variance (ANOVA)
- Correlation and Regression Analysis
- Statistical Inference and Significance Testing
- Time Series Analysis and Forecasting
Practical Exercises:
- Conduct Hypothesis Tests using Real Datasets
- Build and Interpret Regression Models
- Perform Time Series Forecasting for Business Trends
Database (SQL) + Reporting Tool (Power BI)
Overview: SQL and Power BI together form a powerful combination for managing, analyzing, and visualizing data. SQL is used to store, query, and manipulate large datasets efficiently, while Power BI helps transform this data into interactive reports and dashboards for better decision-making..
Key Topics Covered:
- SQL Basics: SELECT, WHERE, GROUP BY, and JOINS
- Data Filtering, Aggregation, and Subqueries
- Database Relationships and Normalization
- Introduction to Power BI Interface and Data Modeling
- Creating Visual Reports and Dashboards
- DAX (Data Analysis Expressions) and Calculated Fields
Practical Exercises:
- Write SQL Queries to Extract and Analyze Business Data
- Connect Power BI to SQL Databases
- Build Interactive Dashboards for Sales and Performance Insights
Machine Learning
Overview: Machine Learning enables systems to learn patterns from data and make intelligent predictions. It’s a key part of modern data science.
Key Topics Covered:
- Understanding Machine Learning Concepts
- Data Preprocessing and Feature Engineering
- Model Evaluation Metrics
- Overfitting and Regularization Techniques
- Real-world ML Applications
Practical Exercises:
- Build basic prediction models
- Evaluate models using accuracy and precision
Supervised Learning
Overview: Supervised Learning uses labeled data to train algorithms that predict outcomes based on input variables.
Key Topics Covered:
- Linear and Logistic Regression
- Decision Trees and Random Forests
- Support Vector Machines (SVM)
- Model Tuning and Cross-Validation
Practical Exercises:
- Predict customer churn using classification models
- Apply regression models for forecasting
Unsupervised Learning
Overview: Unsupervised Learning identifies patterns and relationships in unlabeled data to uncover hidden structures.
Key Topics Covered:
- Clustering Techniques (K-Means, Hierarchical)
- Dimensionality Reduction (PCA)
- Anomaly Detection
- Association Rule Mining
Practical Exercises:
- Segment customers using clustering
- Perform PCA on high-dimensional data
Deep Learning
Overview: Deep Learning is a subset of Machine Learning that uses multi-layered neural networks to learn from complex data. It powers applications like speech recognition, recommendation systems, and image understanding.
Key Topics Covered:
- Neural Network Fundamentals and Architecture
- Activation Functions and Backpropagation
- Optimization Algorithms (SGD, Adam)
- Overfitting, Dropout, and Regularization
- Deep Neural Network (DNN) Implementation
Practical Exercises:
- Build and train neural networks using TensorFlow/Keras
- Experiment with different network architectures
CNN & Computer Vision
Overview: Convolutional Neural Networks (CNNs) specialize in image processing and computer vision tasks. They are used in applications like object detection, face recognition, and self-driving cars.
Key Topics Covered:
- Convolution and Pooling Layers
- Image Classification and Recognition
- Object Detection and Image Segmentation
- Transfer Learning with Pre-trained Models (VGG, ResNet)
- Visualizing CNN Layers and Filters
Practical Exercises:
- Build CNN models for image classification
- Apply object detection using OpenCV and TensorFlow
Natural Language Processing (NLP)
Overview: NLP enables machines to process and understand human language, making it possible to build tools like chatbots, translators, and voice assistants.
Key Topics Covered:
- Text Cleaning and Tokenization
- Sentiment Analysis and Text Classification
- Word Embeddings and Sequence Models
- Transformers and Attention Mechanisms
Practical Exercises:
- Perform sentiment analysis using NLP libraries
- Build text classifiers and translation models
Generative AI (Gen AI)
Overview: Generative AI focuses on creating new content — text, images, or audio — using advanced AI models like GPT, DALL·E, and Stable Diffusion. It’s revolutionizing creativity, automation, and decision-making.
Key Topics Covered:
- Introduction to Large Language Models (LLMs)
- Prompt Engineering and Fine-Tuning
- Generative Text and Image Models
- Ethical AI and Responsible Use
- Applications in Business and Automation
Practical Exercises:
- Generate text using pre-trained LLMs (e.g., GPT models)
- Create AI-generated visuals using diffusion models
✅ Learning Outcomes
- Use Python to analyze and model real-world datasets.
- Query and manage databases using SQL.
- Visualize data using Tableau, Power BI, and Python libraries.
- Build machine learning, deep learning, and computer vision models.
- Apply NLP and Generative AI techniques for real-world applications.
Frequently Asked Questions
Answers to common questions about our Data Science program.