data science
Data Science
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- 4.721 students
- Last updated 25/7/2023
Descriptions
SmartED offers a cutting-edge Data Science course designed to equip learners with in-demand skills across Python programming, data analysis, machine learning, deep learning, natural language processing (NLP), big data, cloud deployment, and more. Guided by industry experts, our hands-on curriculum includes real-world projects such as sentiment analysis, customer segmentation, fraud detection, and time series forecasting. Join thousands of learners who’ve unlocked top career opportunities in tech, analytics, and AI through SmartED.
Key Points
- Python Programming for Data Analysis
- Statistics & Data Visualization
- Machine Learning & Supervised Learning
- Unsupervised Learning & Clustering
- Natural Language Processing (NLP) & Deep Learning
- Model Deployment & Big Data with Cloud
Course Lessons
- Topics: Python syntax, variables, data types, loops, functions, classes, exception handling
- Capstone: Build a simple calculator or text-based game
- Homework: Write Python scripts using control structures and basic functions
- Topics: NumPy, pandas, data cleaning, preprocessing, data visualization with Matplotlib & Seaborn
- Capstone: Analyze and visualize a public dataset
- Homework: Clean messy data and create visual reports
- Topics: SQL queries, joins, aggregations, subqueries, database manipulation
- Capstone: Perform data analysis on a real-world SQL database
- Homework: Extract and analyze data using complex SQL queries
- Topics: Descriptive statistics, probability distributions, hypothesis testing, modeling
- Capstone: Analyze survey or experimental data using statistical methods
- Homework: Solve statistical problems using Python or Excel
- Topics: Handling missing data, outliers, feature selection, dimensionality reduction
- Capstone: Preprocess and transform a messy dataset for ML
- Homework: Engineer new features and perform PCA
- Topics: Regression, classification, Linear & Logistic Regression, Decision Trees, model metrics
- Capstone: Predict housing prices or classify tumor types
- Homework: Train models and evaluate with accuracy, F1 score, and R²
- Topics: Random Forest, SVM, KNN, overfitting, underfitting, regularization, bias-variance tradeoff
- Capstone: Predict customer churn for a telecom company
- Homework: Compare model performance with and without regularization
- Topics: K-means, Hierarchical Clustering, DBSCAN, Association Rules, PCA
- Capstone: Segment customers for a retail company
- Homework: Apply clustering algorithms to unlabeled datasets
- Topics: Tokenization, stemming, lemmatization, Bag-of-Words, TF-IDF, sentiment analysis
- Capstone: Build a sentiment analysis model on movie reviews
- Homework: Preprocess text and classify sentiment
- Topics: Trends, seasonality, stationarity, ARIMA, exponential smoothing
- Capstone: Forecast product demand using time series models
- Homework: Analyze and visualize time-based data
- Topics: End-to-end pipelines, hyperparameter tuning, Flask/Django deployment
- Capstone: Deploy a machine learning model as a web service
- Homework: Create a pipeline and deploy it locally
- Topics: Neural networks, CNNs, RNNs, LSTM, XGBoost, GridSearchCV
- Capstone: Apply deep learning to image or text data
- Homework: Train a neural network with hyperparameter tuning
- Topics: Distributed computing, Apache Hadoop, Apache Spark, large-scale ML
- Capstone: Analyze big data using Spark
- Homework: Perform Spark-based data transformations
- Topics: GANs, VAEs, image detection, segmentation, transfer learning
- Capstone: Build a computer vision system using deep learning
- Homework: Use a pre-trained model for image classification
- Topics: Collaborative filtering, content-based filtering, hybrid systems
- Capstone: Develop a movie or product recommender engine
- Homework: Build a recommendation system with user-item data
- Topics: Anomaly detection techniques, fraud detection using ML
- Capstone: Detect fraudulent transactions in a financial dataset
- Homework: Build a binary classification model for anomaly detection
- Topics: Word2Vec, GloVe, sequence-to-sequence models, advanced preprocessing
- Capstone: Build a language model for machine translation or text generation
- Homework: Create word embeddings and sequence models
- Topics: SARIMA, Prophet, LSTM for time series, trend/seasonality modeling
- Capstone: Forecast sales using LSTM and SARIMA
- Homework: Implement Prophet for time-based prediction
- Topics: MDPs, Q-Learning, Deep Q-Networks, Policy Gradient methods
- Capstone: Build an agent to solve a complex environment
- Homework: Train a model with reinforcement learning algorithms
- Topics: AWS, Azure, GCP, cloud-based model deployment, scalable processing
- Capstone: Deploy an ML model as a scalable web service in the cloud
- Homework: Launch and monitor a cloud-based ML service
Projects
Objective: Build and deploy a full-scale Data Science web application (e.g., job recommendation system, fraud detection dashboard, or AI-based chatbot)
Requirements:
Data ingestion, cleaning, EDA
End-to-end ML pipeline with model training and evaluation
Deployment using Flask/Django on AWS or GCP
- Live dashboard or API with documentation
Teamwork: Collaborative group project with peer code review and final presentation

Instructor

Data Science Expert
This course includes:
- 45+ hours on-demand video
- Full lifetime access
- Access on mobile and TV
- Free Webinar
- Certificate of completion
After the final task and according to the results


After the final task and according to the results
Government Certified
Earn NSDC Certification
Benefits of NSDC Certification:
- Government-Recognized Credential
- Industry-Accepted Validation
- Enhanced Employability
- Alignment with Skill India Mission
- Added Value for Higher Education & International Opportunities
- National Skill Registry Entry