Data drives every decision today. Our Data Science Training with Certification in Vadodara transforms raw numbers into actionable insights.
You’ll be able to clean data, visualise trends, test hypotheses, and create predictive models. After getting certified, you’ll be able to present a portfolio project with confidence.
Build Analytics Skills with Vadodara’s Best Data Science Training
Turn curiosity into capability. This data science training course in Vadodara is carefully structured, so each week unlocks the next:
- Python and core libraries
- Analysis with Pandas
- Visualisation
- Statistics
- Probability
- Machine learning and specialisations like NLP and time series.
You’ll learn by doing. Mini tasks, guided labs, and one capstone tie it all together like the best data science courses do.
Career Paths After Data Science Training in Vadodara
A solid base opens up many doors – Data Analyst, Business Analyst, Junior Data Scientist, ML Associate, BI Executive, and Operations Analyst.
Jobs vary, but all employers appreciate clean data work, well-thought-out EDA, clear visualisation storytelling, and models that are explainable and reliable.
The capstone and classroom training provide you with real interview tactics.
Why Select Our Data Science Training Class Vadodara
Here are reasons why you must enrol in the best data science courses:
- Classroom-Only Training: Learn faster with live explanations, whiteboard demos, and real-time feedback.
- Job-Rady Curriculum: Focus on skills that local employers demand, including Pandas, EDA, and forecasting.
- Hands-On Learning: Complete labs, mini-projects, and a deployable capstone to prove your skills.
- Career Support: Get guidance on resumes, interview preparation, and showcasing your projects.
- Small Batches: Personalized attention for better learning outcomes.
Who is This Data Science Course Best For?
- Students and freshers who need a solid foundation with live projects.
- Working professionals in operations, finance, marketing, or IT.
- Career changers who prefer real-time training.
- Entrepreneurs who desire data-driven dashboards and trending forecasts.
Join the Top-Rated Data Science Classroom Programme
Seats are limited and batches are small. Visit VTechLabs or call to discuss schedules, fees, and the capstone pathway. You’ll graduate with a working project, a clear story of how you built it, and a data science course with certification that employers can trust.
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Month 1: Python Programming & Data Analysis Basics
Week 1 – Python Fundamentals
- Introduction to Python, Jupyter Notebook
- Variables, Data Types, Operators
- Conditional Statements (if, elif, else)
- Loops (for, while)
- Functions and Modules
Week 2 – Data Structures & Libraries
- Lists, Tuples
- Dictionaries, Sets
- File Handling in Python
- Introduction to NumPy
- NumPy Arrays and Operations
Week 3 – Data Analysis with Pandas
- Pandas Series and DataFrames
- Indexing, Slicing, Filtering Data
- Merging, Joining, Concatenation
- GroupBy and Aggregations
- Handling Missing Data and Duplicates
Week 4 – Data Visualization
- Introduction to Matplotlib
- Line, Bar, Pie Charts
- Histograms, Boxplots
- Seaborn Basics
- Pairplots, Heatmaps
Month 2: Statistics, Probability, and Exploratory Data Analysis (EDA)
Week 5 – Descriptive Statistics
- Measures of Central Tendency (Mean, Median, Mode)
- Measures of Dispersion (Range, Variance, Std Dev)
- Skewness and Kurtosis
- Data Distributions
- Practice with Real Datasets
Week 6 – Probability Concepts
- Basic Probability Rules
- Conditional Probability
- Bayes Theorem
- Probability Distributions
- Normal, Binomial, Poisson Distributions
Week 7 – Inferential Statistics
- Sampling and Central Limit Theorem
- Confidence Intervals
- Hypothesis Testing
- t-test, z-test
- Chi-square, ANOVA
Week 8 – Exploratory Data Analysis (EDA)
- Understanding Dataset & Objectives
- Univariate Analysis
- Bivariate/Multivariate Analysis
- Outlier Detection
- Feature Engineering Techniques
Month 3: Machine Learning (Supervised & Unsupervised)
Week 9 – Machine Learning Basics
- What is ML? Types of ML
- ML Workflow and Model Lifecycle
- Train-Test Split and Cross Validation
- Performance Metrics (Accuracy, Precision, Recall)
- Overfitting and Underfitting
Week 10 – Supervised Learning (Regression)
- Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Evaluation of Regression Models
- Case Study: House Price Prediction
Week 11 – Supervised Learning (Classification)
- Logistic Regression
- K-Nearest Neighbors (KNN)
- Decision Trees
- Random Forests
- Model Evaluation (Confusion Matrix, ROC-AUC)
Week 12 – Unsupervised Learning
- Introduction to Clustering
- K-Means Clustering
- Hierarchical Clustering
- PCA (Dimensionality Reduction)
- Case Study: Customer Segmentation
Month 4: Advanced Topics and Projects
Week 13 – Natural Language Processing (NLP)
- Text Cleaning and Preprocessing
- Tokenization, Stopwords
- Bag of Words, TF-IDF
- Sentiment Analysis
- NLP Mini Project
Week 14 – Time Series Analysis
- Time Series Components
- Moving Averages, Decomposition
- ARIMA Modeling
- Forecasting Techniques
- Time Series Project
Week 15 – Deep Learning Basics
- Introduction to Neural Networks
- Forward and Backpropagation
- Activation Functions
- Model Training using TensorFlow/Keras
- Build Your First ANN
Week 16 – Final Project and Deployment
- Capstone Project Planning
- Data Collection & Cleaning
- Model Building
- Model Evaluation & Tuning
- Deployment using Flask/Streamlit
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Alok Ray
Instructor
Web Development Applications
Bio
Mr. Alok Ray, Whose goal is to maximize potential through individual attention, for whom, students’ bright future comes first. Now he caters 16+ years of experience in Open source PHP, Laravel, WordPress, and MYSQL and is…read more
