Python Programming
Master Python from scratch — functions, OOP, file handling, and libraries — the essential foundation for any data science or AI career path.
₹5,000 50 Classes
AI & Data Science Training
Data Science with Python
Course in Howrah & Kolkata
Master Data Science with Python — the most powerful combination driving the AI revolution. From NumPy and Pandas for data manipulation to Matplotlib for visualization, Scikit-Learn for machine learning, NLP, time series forecasting, and real-world data projects — become a fully job-ready data scientist in 40 expert-guided classes.
Python 3
Pandas
NumPy
Scikit-Learn
Matplotlib
Seaborn
40
Classes
40h
Duration
5
Modules
Real
Projects
10–15
Batch Size
Course Details
What You Get
Everything you need to become a job-ready data scientist — from Python fundamentals and exploratory analysis to machine learning, NLP, and deploying real data science models — all in one structured, hands-on course.
40 Classes · 40 Hours
A comprehensive, expert-paced programme covering every aspect of data science using Python — from NumPy and Pandas data manipulation to ML algorithms, NLP, time series, and building complete predictive models for real-world problems.
ISO & MSME Certificate
Earn a government-recognized, ISO-certified completion certificate that adds genuine credibility to your resume and proves professionally verified Data Science with Python training to employers and data-driven companies across India.
Real-World Data Projects
Build predictive models, data visualization dashboards, classification systems, NLP sentiment analyzers, and more — leaving with a portfolio of data science work that proves your skills to employers, freelance clients, and on Kaggle.
Small Batch Sizes
Only 10–15 students per batch ensures personal attention, faster code reviews, and a genuinely productive learning environment where every student masters every algorithm and technique with full understanding.
Exploratory Data Analysis
Master EDA — the most critical and creative phase of data science. Uncover patterns, detect outliers, handle missing values, and generate visual insights with Matplotlib and Seaborn that tell compelling data stories.
Machine Learning with Scikit-Learn
Learn supervised and unsupervised machine learning — regression, classification, clustering, dimensionality reduction — using Scikit-Learn, the industry-standard Python ML library powering data science across every industry.
Full Curriculum
Course Syllabus
5 comprehensive modules — Python & DS Foundations, EDA & Visualization, Machine Learning, Advanced Techniques, and Real-World Projects.
Module 1: Python & Data Science Foundations
Begin your data science journey from the ground up. Understand what data science is and why Python is the world's leading language for AI and analytics. Set up your complete Python data science environment with Jupyter Notebooks. Master the essential libraries — NumPy for numerical computing, Pandas for data manipulation, and the data collection and cleaning workflow that underpins every real data science project.
5 LessonsPythonNumPyPandasJupyterData Cleaning
01
Overview of Data Science & PythonWhat is data science — the intersection of statistics, programming, and domain expertise. The complete data science workflow: problem definition → data collection → cleaning → EDA → modelling → deployment. Why Python dominates data science: readability, ecosystem, community. Overview of the data science job landscape in India — data analyst, ML engineer, BI analyst, data scientist. Setting up Python and understanding its role in AI, analytics, and business intelligence.
02
Setting Up Python for Data ScienceInstalling Python 3 and Anaconda distribution — the complete data science toolkit. Setting up and navigating Jupyter Notebooks — cells, markdown, code execution, and magic commands. Installing and importing libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn. Understanding virtual environments for project isolation. Introduction to Google Colab as a cloud-based alternative for GPU-accelerated experimentation and collaboration.
03
NumPy — Numerical Computing FoundationUnderstanding NumPy arrays vs. Python lists — performance and memory advantages. Creating arrays: np.array(), np.zeros(), np.ones(), np.arange(), np.linspace(). Array shapes, dimensions, and reshaping. Indexing, slicing, and fancy indexing. Vectorized operations — element-wise arithmetic, broadcasting rules. Statistical functions: mean, median, std, var, min, max, percentile. Linear algebra basics with NumPy — dot products and matrix operations used throughout machine learning.
04
Pandas — Data Manipulation & AnalysisPandas Series and DataFrame — the core data structures of data science. Loading data from CSV, Excel, JSON, and SQL databases with pd.read_csv() and pd.read_excel(). Exploring DataFrames: df.head(), df.info(), df.describe(), df.shape. Selecting columns and rows with loc and iloc. Filtering data with boolean conditions. Sorting, renaming columns, and dropping unnecessary fields. Grouping and aggregation with groupby(). Merging and joining DataFrames — merge(), concat(), join() for combining data from multiple sources.
05
Data Collection, Cleaning & PreparationUnderstanding the data quality problem — why real-world data is messy, incomplete, and inconsistent. Handling missing values: detecting with isnull(), filling with fillna(), dropping with dropna(). Detecting and treating outliers using IQR and z-score methods. Data type conversion and fixing inconsistent categorical values. String cleaning and feature extraction. Data normalization and standardization — MinMaxScaler and StandardScaler. Creating a clean, analysis-ready dataset from raw, messy real-world data files.
Module 2: Exploratory Data Analysis & Visualization
Data storytelling is at the heart of data science. Master the full EDA toolkit — understand your data's structure, distribution, and relationships before building any model. Create compelling, professional visualizations with Matplotlib and Seaborn that communicate insights clearly to technical and non-technical audiences alike. This is where intuition meets analysis.
4 LessonsEDAMatplotlibSeabornStatisticsImputation
01
Introduction to EDA & Its ImportanceWhat is Exploratory Data Analysis — and why every data scientist starts here before building models. Understanding data distributions — symmetric, skewed, bimodal. Central tendency vs. spread: mean, median, mode, range, IQR, variance, standard deviation. Understanding data types: numerical (continuous vs. discrete) and categorical (ordinal vs. nominal). The five-number summary and its role in understanding your dataset's shape. Identifying patterns, anomalies, and trends through exploratory thinking before formal analysis.
02
Data Visualization with Matplotlib & SeabornMatplotlib fundamentals — Figure, Axes, subplots, figure size, DPI, labels, titles, legends, and color customization. Line charts, bar charts, histograms, scatter plots, and pie charts with Matplotlib. Seaborn's statistical visualizations: heatmaps for correlation analysis, box plots for outlier detection, violin plots for distribution comparison, pair plots for multi-variable relationships, count plots for categorical data. Choosing the right chart type for the right data story. Styling plots for professional presentations and reports.
03
Summary Statistics & Understanding Data DistributionComputing and interpreting descriptive statistics with Pandas and NumPy. Correlation analysis — Pearson and Spearman correlation coefficients. Reading correlation heatmaps to identify feature relationships and multicollinearity. Understanding skewness and kurtosis — what they reveal about your data. Probability distributions — normal, uniform, binomial, Poisson — and when they appear in real datasets. Using statistical summaries to form hypotheses and guide feature engineering decisions for machine learning.
04
Handling Missing Values, Outliers & Data ImputationDeep dive into missing data — MCAR, MAR, MNAR — and why the type of missingness determines the treatment. Imputation strategies: mean/median/mode imputation, forward fill, backward fill, and interpolation. Advanced imputation with KNN imputation and iterative imputation. Outlier detection: visual methods (box plots, scatter plots), statistical methods (z-score, IQR), and domain-knowledge-based approaches. Deciding when to remove outliers vs. cap them vs. transform the data. Building a reproducible data preprocessing pipeline for production use.
Module 3: Machine Learning with Scikit-Learn
Machine learning is the engine of modern data science. Learn the complete ML workflow — from splitting data and training models to evaluating performance and improving accuracy. Master supervised learning algorithms for regression and classification, understand the bias-variance tradeoff, and use Scikit-Learn's powerful API the way professional data scientists and ML engineers do every day.
5 LessonsScikit-LearnRegressionClassificationModel Evaluation
01
Introduction to Machine Learning & Scikit-LearnWhat is machine learning — learning from data rather than explicit rules. The ML taxonomy: supervised, unsupervised, and reinforcement learning. The complete ML pipeline: data → preprocessing → train/test split → model selection → training → evaluation → tuning → deployment. Scikit-Learn's consistent API: fit(), predict(), transform(), score(). Understanding features (X) and targets (y). Using train_test_split() and cross_val_score() for robust model evaluation. The importance of reproducibility: setting random_state for consistency.
02
Supervised vs. Unsupervised LearningSupervised learning in depth — labeled data, training, and generalization. Regression problems (predicting continuous values) vs. classification problems (predicting categories). Unsupervised learning overview — finding hidden patterns in unlabeled data: clustering, dimensionality reduction, anomaly detection. Semi-supervised and self-supervised learning concepts. Feature engineering — creating meaningful input features from raw data. One-hot encoding and label encoding for categorical variables. Pipelines in Scikit-Learn for clean, reproducible workflows.
03
Regression Algorithms — Linear & PolynomialSimple Linear Regression — theory, assumptions, and the ordinary least squares method. Multiple Linear Regression — multiple features, interpretation of coefficients, and assumptions. Polynomial Regression — fitting non-linear relationships with polynomial features. Ridge Regression (L2) and Lasso Regression (L1) for regularization — preventing overfitting in high-dimensional datasets. Evaluation metrics for regression: MSE, RMSE, MAE, R² score. Visualizing regression lines and residual plots to diagnose model fit problems.
04
Classification Algorithms — Logistic Regression & Decision TreesLogistic Regression — the foundation of classification, sigmoid function, decision boundary, and probability thresholding. Decision Tree Classifier — how trees split data using Gini impurity and information gain. Random Forest — ensemble of decision trees, feature importance, and handling overfitting. k-Nearest Neighbors (KNN) — the instance-based learning algorithm and choosing k. Support Vector Machine (SVM) basics — maximum-margin classifiers. Handling imbalanced classes — oversampling, undersampling, and class weights.
05
Model Evaluation & ValidationClassification evaluation metrics: accuracy, precision, recall, F1-score, ROC-AUC. Confusion matrix — understanding true/false positives and negatives for real-world impact. Cross-validation strategies: k-fold, stratified k-fold, leave-one-out. The bias-variance tradeoff — understanding underfitting and overfitting. Hyperparameter tuning with GridSearchCV and RandomizedSearchCV. Learning curves for diagnosing model performance. Building an end-to-end ML project from raw data to a final evaluated, tuned predictive model.
Module 4: Advanced Data Science Techniques
Go beyond standard ML and tackle the advanced techniques that distinguish senior data scientists. Analyze time-ordered data with time series forecasting. Process human language with NLP. Group unlabeled data with clustering algorithms. Reduce dimensionality with PCA. These are the techniques driving innovation in finance, healthcare, NLP applications, and AI products across every industry in 2025.
4 LessonsTime SeriesNLPClusteringPCA
01
Time Series Analysis & ForecastingWhat is time series data — sequences where the order matters. Decomposing time series into trend, seasonality, and residual components. Rolling averages and exponential smoothing for noise reduction. Stationarity testing with the ADF test and differencing to achieve stationarity. ARIMA modelling — AutoRegressive Integrated Moving Average — for univariate forecasting. Evaluating forecast accuracy: MAE, RMSE, MAPE. Real-world applications: sales forecasting, stock price trends, weather prediction, and web traffic analysis.
02
Natural Language Processing (NLP)What is NLP — teaching machines to understand human language. Text preprocessing pipeline: tokenization, stopword removal, stemming, and lemmatization. Bag of Words (BoW) and TF-IDF vectorization — converting text to numerical features. Sentiment analysis — classifying text as positive, negative, or neutral using Logistic Regression and Naive Bayes. Named Entity Recognition (NER) overview. Word embeddings introduction — Word2Vec and GloVe concepts. Building a real NLP project: movie review sentiment classifier or product review analyzer using Python and Scikit-Learn.
03
Clustering Algorithms — K-Means & HierarchicalUnsupervised learning in practice — when you have data but no labels. K-Means Clustering — the centroid algorithm, choosing k with the Elbow Method and Silhouette Score. K-Means in practice: customer segmentation, document grouping, and image compression. DBSCAN — density-based clustering for discovering clusters of arbitrary shape and identifying noise points. Hierarchical Clustering — agglomerative approach and dendrogram interpretation. Evaluating clustering quality: inertia, silhouette score, and Davies-Bouldin index. Real applications: market segmentation, anomaly detection, and recommendation system preprocessing.
04
Dimensionality Reduction — PCA & Feature SelectionThe curse of dimensionality — why high-dimensional data hurts ML model performance. Principal Component Analysis (PCA) — the math behind it and practical application with Scikit-Learn. Choosing the number of principal components using explained variance ratio. Visualizing high-dimensional data in 2D and 3D with PCA and t-SNE. Filter methods for feature selection: variance threshold, correlation-based selection, chi-square test. Wrapper methods: Recursive Feature Elimination (RFE). Embedded methods: feature importance from tree-based models and Lasso regularization for automatic feature selection.
Module 5: Real-World Data Science Projects
All the theory and techniques come alive in this capstone module. You'll build complete end-to-end data science projects — from raw dataset to insights, models, and visualizations. Each project covers a different industry use case and skill set, and together they form a compelling portfolio that demonstrates to employers and clients that you can solve real data problems from day one.
Multiple ProjectsPortfolio-ReadyEnd-to-EndIndustry Use Cases
Project Approach: Problem-First ThinkingEvery project begins with a real-world problem statement — understanding what business or research question we're answering. We then collect and explore the dataset, clean and engineer features, select and train models, evaluate and tune performance, and finally interpret and communicate findings. This mirrors exactly how professional data scientists work.
Project 01
Exploratory Data Analysis Dashboard
Full EDA on a real-world dataset (e.g., sales, housing, or health data) — cleaning, statistical summaries, correlation analysis, and 10+ visualizations telling a complete data story.
Project 02
House Price Prediction Model
Predict residential property prices using regression — data cleaning, feature engineering, Linear & Ridge Regression, hyperparameter tuning, and final model evaluation with RMSE and R².
Project 03
Customer Churn Classifier
Predict which customers will leave a subscription service using classification — Logistic Regression, Random Forest, and evaluation with precision, recall, F1, and ROC-AUC on imbalanced data.
Project 04
Sentiment Analysis — NLP Project
Build a text sentiment classifier on movie or product reviews — text preprocessing, TF-IDF vectorization, Naive Bayes / Logistic Regression, and evaluation on a held-out test set.
Project 05
Customer Segmentation — Clustering
Segment retail customers into distinct groups using K-Means clustering on transactional data — RFM analysis, elbow method for optimal k, and a business-ready segmentation report.
Project 06
Building & Deploying a DS Model
Train a complete ML model, save it with joblib/pickle, and create a simple Flask API or Streamlit app to serve predictions — experiencing the full lifecycle from training to deployment.
What You'll Learn
Learning Outcomes
Graduate with the data science skills needed for analyst, ML engineer, and data scientist roles — with a portfolio of real projects to prove your capabilities.
Analyze Data with Python
Use NumPy, Pandas, and Jupyter Notebooks to collect, clean, manipulate, and analyze real-world datasets with the same tools used by professional data scientists at top companies across India and globally.
Create Compelling Visualizations
Design clear, professional, and informative visualizations with Matplotlib and Seaborn — histograms, heatmaps, pair plots, and dashboards — that communicate insights to technical and business audiences.
Build Machine Learning Models
Train, evaluate, and tune supervised and unsupervised ML models using Scikit-Learn — regression, classification, clustering — and understand how to select the right algorithm for any business problem.
Process Text with NLP
Build NLP pipelines for text classification, sentiment analysis, and information extraction — skills in high demand for social media analytics, chatbot development, and AI content systems.
Deliver a Data Science Portfolio
Leave with 6 end-to-end data science projects — EDA dashboards, prediction models, sentiment analyzers, customer segmentation — that you can showcase on GitHub, LinkedIn, and in job interviews.
Become Job-Ready in Data Science
Walk into data analyst, ML engineer, data scientist, and AI trainee roles with full confidence — able to process data, build models, and extract actionable insights from real-world datasets from day one.
Who Should Join?
This Course Is For You
Whether you're a complete beginner, a Python programmer, or a working professional — this course gives you complete Data Science mastery for real-world career success in the fastest-growing tech field.
🎓
Students & Freshers
BCA, B.Tech, B.Sc, and Statistics students who want to add data science and ML skills that give them an immediate advantage in the job market and open doors to high-paying analyst and AI roles.
💼
Career Changers
Professionals from accounting, marketing, finance, or operations who want to leverage their domain expertise with data science skills — becoming data-driven decision makers in their own industry.
🐍
Python Developers
Python programmers who want to expand from web or app development into the data science, machine learning, and AI field — using the language they already know to build intelligent, data-driven systems.
📊
Business Professionals
Managers, analysts, and entrepreneurs who want to go beyond Excel — using Python and machine learning to extract deeper insights, build predictive models, and make data-driven strategic decisions.
FAQ
Frequently Asked Questions
What is the fee for the Data Science with Python course at PBA Institute?
The batch class fee is ₹6,000 for the complete 40-class Data Science with Python course (40 hours). One-to-One personalized sessions are available at a higher rate with dedicated instructor attention and a fully flexible schedule. Both options include study materials, software installation support, and an ISO-certified certificate.
Do I need Python or programming experience before joining?
Basic Python familiarity is helpful but not strictly required. If you are a complete beginner to programming, we recommend taking our Python Programming course first. If you already know basic Python syntax and functions, you are ready to join this Data Science course directly. The course covers all necessary Python concepts specific to data science from the ground up.
What real-world projects will I build during the course?
You will build multiple real-world end-to-end data science projects: 1) Exploratory Data Analysis Dashboard, 2) House Price Prediction Model using Regression, 3) Customer Churn Classifier, 4) Sentiment Analysis NLP project, 5) Customer Segmentation using K-Means Clustering, and 6) Building and Deploying a Data Science Model with Flask or Streamlit. Each project covers a different industry domain and skill set.
Is Data Science with Python a good career choice in 2025?
Absolutely. Data science is the #1 fastest-growing profession globally according to the World Economic Forum. In India, demand for data analysts, ML engineers, and data scientists has grown over 40% year-on-year. Python data science skills open doors to roles at tech companies, financial firms, e-commerce, healthcare, and every data-driven industry — with salaries ranging from ₹4–25 LPA depending on experience and specialization.
Can I take the Data Science course online from outside Howrah?
Yes. PBA Institute offers both online and in-person classes from our Howrah campus. Online sessions are conducted live with the same instructor — full screen sharing, live code demonstrations, Jupyter Notebook walkthroughs, and real-time doubt resolution in every class. Students from all over West Bengal and India join online and receive the same ISO certificate upon completion.
What certificate will I receive after the Data Science course?
You will receive a completion certificate from PBA Institute — ISO 9001:2015 Certified and MSME Government Registered. This credential adds genuine value to your resume and LinkedIn profile, demonstrating professionally verified Data Science with Python training to employers, data-driven companies, and educational institutions across India.
Start Your Data Science Career Today
Ready to Master Data Science?
Join PBA Institute's Data Science with Python course in Howrah. Learn NumPy, Pandas, Machine Learning, NLP, and build real projects — earn an ISO certificate, build a portfolio, and unlock data science careers across India.
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