Data Science
The Data Science course is a comprehensive, industry-focused program designed to take learners from beginner to advanced level in data analysis, machine learning, and real-world data applications. Over the course duration, students will gain hands-on experience with tools like Python, SQL, Pandas, and machine learning frameworks, enabling them to extract insights, build predictive models, and solve business problems using data.
Expert Instructor
Mayur Tembhare
Mayur Tembhare is a passionate data science educator and technology professional wit…
Course Description
This Data Science course is a comprehensive, industry-oriented program designed to take learners from beginner to advanced level, covering everything from Python programming and data analysis to machine learning and real-world project implementation. Students will learn how to collect, clean, and analyze data using tools like Pandas, NumPy, and SQL, while also developing strong foundations in statistics and data visualization to uncover meaningful insights. As the course progresses, learners will build and evaluate machine learning models using techniques such as regression, classification, and clustering, and gain hands-on experience solving real-world problems through practical projects. By the end of the course, participants will have the skills, confidence, and portfolio required to pursue roles such as Data Analyst, Data Scientist, or Machine Learning Engineer in today’s data-driven industry.
Course Outline
Variables, Data Types, Operators, I/O
Control Flow: if/else, for, while loops
Tools : Python 3, VS Code / Jupyter Notebook
Functions, *args/ **kwargs, lambda
Modules, file I/O, exception handling
List comprehensions
Classes, inheritance, encapsulation
Dunder methods, Python standard library
Descriptive stats: mean, median, std, IQR
Probability, distributions (Normal, Binomial, Poisson)
Vectors, matrices, dot product — NumPy
NumPy arrays, broadcasting, ufuncs
Pandas: DataFrames, merging, GroupBy, missing data
Matplotlib: line, bar, scatter, histogram
Seaborn: heatmaps, pair plots, violin plots
SQL: SELECT, WHERE, JOIN, GROUP BY
Encoding (label, OHE, target), scaling, outlier treatment
Scikit-learn Pipelines, feature selection
Metrics: MAE, RMSE, R²
Linear, Ridge, Lasso, Polynomial Regression
Logistic Regression, KNN, Decision Trees, SVM
Metrics: Accuracy, Precision, Recall, F1, ROC-AUC
Random Forest, Gradient Boosting
XGBoost, LightGBM, CatBoost
Bagging vs boosting; hyperparameter tuning
K-Means (elbow method), DBSCAN, Hierarchical Clustering
PCA, t-SNE for dimensionality reduction
Window functions: ROW_NUMBER, RANK, LEAD/LAG
CTEs, stored procedures, query optimisation
Cross-validation deep dive: k-Fold, Stratified k-Fold
Perceptrons, activation functions, backpropagation
Optimisers: SGD, Adam; ANN with Keras
Regularisation: Dropout, Batch Norm, Early Stopping
Conv2D, MaxPooling, Flatten, Dense layers
Transfer learning: ResNet50, VGG — image classification
Data augmentation, ImageDataGenerator
Tokenisation, stemming, lemmatisation, stop words
TF-IDF, Word2Vec, GloVe embeddings
Sentiment analysis pipeline
Attention mechanism overview
Fine-tuning BERT / DistilBERT with HuggingFace
Text classification and Q&A tasks
Stationarity, ADF test, differencing
ARIMA, SARIMA — ACF/PACF order selection
LSTM for sequences; Facebook Prophet
LSTM for sequences; Facebook Prophet
Effect size, statistical power, Type I/II errors
Multiple testing correction: Bonferroni, BH
FastAPI REST API: endpoints, Pydantic validation
Model serialisation: pickle, joblib, ONNX
Docker: Dockerfile, images, containers
Streamlit: widgets, state, caching, multi-page apps
Plotly: interactive charts, choropleth maps
Deploy to Streamlit Cloud / Heroku
MLflow: experiment tracking, model registry, staging → production
DVC: data and model versioning with remote storage
GitHub Actions: CI/CD, automated tests, linting
AWS S3 (storage) · EC2 (compute) · SageMaker (managed ML)
GCP Vertex AI · Cloud Storage
BigQuery / Redshift for large-scale analytics
Full pipeline: data ingestion → EDA → modelling → API + dashboard
Peer review and mentor feedback session
Stakeholder-style demo presentation (10 slides)
Mock technical + case-study interview
Portfolio review: GitHub, Kaggle, LinkedIn
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Program Instructors
Mayur Tembhare
Mayur Tembhare is a passionate data science educator and technology professional with a strong background in building scalable software systems and data-driven solutions. With hands-on experience in real-world projects across web development, automation, and analytics, he brings a practical, industry-focused approach to teaching data science—from foundational concepts to advanced machine learning techniques. Mayur is dedicated to simplifying complex topics, empowering students with job-ready skills, and guiding learners to think critically, solve real problems, and build impactful data-driven applications.
Why Choose This Course?
50%
Demand for data scientists in India has increased by 50% over the last five years, with NASSCOM projecting 7 million data-related jobs by 2025
1M+
India will need over 1 million data science and AI professionals by 2026, with the Indian data science market growing at a CAGR of over 33%
21%
Data science-related job postings in India have grown 21% year-on-year according to a 2025 Naukri.com report, with companies aggressively hiring for ML, AI, and analytics roles
77%
77% of data science upskilling learners in 2025 came from non-technology industries — BFSI, energy, manufacturing, and healthcare — showing the field's reach beyond core IT
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- Comprehensive curriculum
- Practical learning through projects
- Expert guidance from professionals
- Recognized certification
- Boost career prospects
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