Data Science Course in Pune
With 100% Placement Support

Python is the most widely used language in Data Science. This module builds a rock-solid foundation with a focus on data-centric programming.

• Python basics: variables, data types, operators, loops, conditionals
• Functions, lambda expressions, list comprehensions
• OOP: classes, objects, inheritance, encapsulation
• File handling, exception handling, modules and packages
• NumPy: arrays, vectorized operations, broadcasting
• Pandas: DataFrames, Series, data cleaning, merging, groupby
• Matplotlib & Seaborn: charts, plots, heatmaps, pair plots
• Regular expressions, web scraping with BeautifulSoup
• Jupyter Notebook, Google Colab, best practices

Understanding the mathematical backbone of ML algorithms is essential for any serious Data Scientist.

• Descriptive statistics: mean, median, mode, variance, standard deviation
• Probability theory: conditional probability, Bayes' theorem
• Probability distributions: Normal, Binomial, Poisson, Bernoulli
• Inferential statistics: hypothesis testing, p-value, Z-test, t-test
• Confidence intervals and sampling techniques
• Linear algebra: matrices, vectors, dot product, eigenvalues
• Calculus for ML: gradients, partial derivatives, chain rule
• Correlation, covariance, and chi-square tests
• Statistical analysis with Python (scipy, statsmodels)

Every Data Scientist must be able to extract and manipulate data from databases. This module covers SQL from basics to advanced analytical queries.

• Relational databases: tables, keys, relationships, normalisation
• DDL, DML, DCL, TCL commands
• SELECT, WHERE, GROUP BY, ORDER BY, HAVING
• JOINs: INNER, LEFT, RIGHT, FULL OUTER, CROSS
• Subqueries, CTEs, window functions (ROW_NUMBER, RANK, LAG, LEAD)
• Stored procedures, views, indexing, query optimisation
• Connecting Python to MySQL / PostgreSQL
• NoSQL basics: MongoDB, document storage
• Real-world SQL case studies

Data is never clean in the real world. This module builds expertise in preparing and understanding raw data before modelling.

• Understanding real-world messy data
• Handling missing values: imputation strategies
• Outlier detection and treatment (IQR, Z-score)
• Feature encoding: label, ordinal, one-hot encoding
• Feature scaling: normalisation, standardisation, MinMax
• Data transformation and feature engineering
• Exploratory Data Analysis (EDA) techniques
• Advanced Pandas operations on large datasets
• Univariate, bivariate, multivariate analysis

The heart of Data Science. This module covers all core ML algorithms with theory, implementation, and real-world application.

• ML types: supervised, unsupervised, reinforcement
• Linear Regression, Multiple Regression, Ridge, Lasso
• Logistic Regression, Decision Trees, Random Forests
• Support Vector Machines (SVM)
• Naive Bayes, K-Nearest Neighbours (KNN)
• Gradient Boosting, XGBoost, LightGBM, CatBoost
• Unsupervised: K-Means, DBSCAN, Hierarchical Clustering
• PCA, t-SNE, dimensionality reduction
• Model evaluation: accuracy, precision, recall, F1, ROC-AUC
• Cross-validation, hyperparameter tuning (GridSearchCV, RandomizedSearchCV)
• Scikit-learn pipelines and best practices

Deep Learning is the technology behind AI breakthroughs. This module covers neural networks from scratch to advanced architectures.

• Introduction to neural networks: perceptron, activation functions
• Forward propagation, backpropagation, gradient descent
• Artificial Neural Networks (ANN) with Keras/TensorFlow
• Regularisation: dropout, batch normalisation, L1/L2
• Convolutional Neural Networks (CNN) for image tasks
• Recurrent Neural Networks (RNN), LSTM, GRU
• Transfer Learning: VGG, ResNet, EfficientNet
• Object detection: YOLO basics
• Autoencoders and Generative Adversarial Networks (GANs)
• PyTorch vs TensorFlow comparison

NLP powers chatbots, sentiment analysis, and text-based AI. A critical skill in the age of LLMs.

• Text preprocessing: tokenization, stemming, lemmatization, stopwords
• Bag of Words, TF-IDF, N-grams
• Word embeddings: Word2Vec, GloVe, FastText
• Sentiment analysis and text classification
• Named Entity Recognition (NER)
• Topic modelling (LDA)
• Sequence-to-sequence models
• Transformers architecture (BERT, GPT overview)
• Hugging Face library: fine-tuning pre-trained models
• Real project: Customer review analyser

Communicating insights is as important as finding them. This module covers industry-standard BI tools used by top companies.

• Advanced Matplotlib, Seaborn, Plotly, Bokeh
• Interactive dashboards with Plotly Dash / Streamlit
• Power BI: data modelling, DAX formulas, reports, dashboards
• Tableau basics: charts, calculated fields, stories
• Excel for data analysis: pivot tables, power query, charts
• Building KPI dashboards for business use cases
• Data storytelling principles
• Connecting BI tools to SQL databases and Excel

GenAI is the hottest skill in the market. This module makes TechnoScripts' curriculum unique and ahead of competitors.

• Introduction to Generative AI, LLMs and their evolution
• GPT architecture: how ChatGPT works
• Prompt engineering: zero-shot, few-shot, chain-of-thought
• OpenAI API: integration with Python applications
• LangChain framework: agents, chains, memory
• Retrieval Augmented Generation (RAG) pipelines
• Vector databases: Pinecone, Chroma, FAISS
• Fine-tuning LLMs on custom data
• Building AI-powered chatbots and Q&A systems
• AI ethics, hallucination, and responsible AI

• Time series components: trend, seasonality, noise
• Stationarity and differencing
• ARIMA, SARIMA, ARIMAX models
• Exponential smoothing: Holt-Winters
• Facebook Prophet for forecasting
• LSTM-based time series prediction
• Anomaly detection in time series
• Real project: Stock price / sales forecasting

Building a model is only 20% of the job. Deploying and maintaining it is the other 80% — this is what companies need.

• ML lifecycle: experiment, train, evaluate, deploy
• Flask and FastAPI for serving ML models
• REST API creation for ML inference
• Docker containerisation of ML models
• CI/CD pipelines for ML (GitHub Actions basics)
• Model versioning with MLflow
• Cloud deployment: AWS EC2, AWS SageMaker (overview), Heroku
• Streamlit app deployment
• Monitoring model drift and performance in production

Projects run from day one alongside each module. The final capstone ties everything together.

• End-to-End Data Science Project (capstone): problem statement → EDA → modelling → deployment
• Industry domain projects: Healthcare, Finance, E-commerce, Retail, HR Analytics
• Resume building workshop (ATS-optimised data science CV)
• LinkedIn profile optimisation
• GitHub portfolio setup
• Technical mock interviews (ML / Python / SQL)
• HR round preparation and soft skills
• Aptitude and coding test practice
• Company-specific preparation (Infosys, TCS, product startups)
• Direct referrals to 572+ hiring partners
• Campus and off-campus drives