Learn programming for Free
Learn to program with our beginner-friendly tutorials and examples. Read tutorials, try examples, write code and learn to program.
Choose what to learn
Start learning the best programming languages.
Learn &
Level Up your skills
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
- 50+ courses
- Certificate
- Project & Assignments
Beginner's Guide to Data Science
These tutorials will provide you with a solid foundation in Data Science and prepare you for your career goals.
Introduction to Data Science
- What is Data Science?
- History and Evolution of Data Science
- Applications of Data Science
- Difference Between Data Science, Machine Learning, and AI
- Role of a Data Scientist
- Data Science Workflow and Lifecycle
Data Collection and Preprocessing
- Types of Data: Structured, Unstructured, Semi-structured
- Data Sources: Databases, APIs, Web Scraping
- Data Cleaning and Preprocessing
- Handling Missing Data
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Data Transformation Techniques
Mathematics and Statistics for Data Science
- Probability Basics
- Descriptive Statistics: Mean, Median, Mode, Variance
- Inferential Statistics: Hypothesis Testing, p-value
- Linear Algebra for Data Science
- Calculus in Machine Learning
- Bayesian Statistics
- Random Variables and Distributions
Programming for Data Science
- Python Basics for Data Science
- Libraries: NumPy, Pandas, Matplotlib, Seaborn
- Writing Functions and Loops in Python
- Handling DataFrames and Series
- R Programming Basics
- SQL for Data Science
- Writing SQL Queries
- Joins and Subqueries
- Aggregations and Window Functions
Data Visualization
- Principles of Data Visualization
- Creating Charts and Graphs Using Python
- Tools: Matplotlib, Seaborn, Plotly
- Dashboard Development Using Tableau/Power BI
- Advanced Visualizations: Heatmaps, Pair Plots, and Geographic Plots
Machine Learning Basics
- Introduction to Machine Learning
- Supervised vs. Unsupervised Learning
- Regression Analysis: Linear and Logistic Regression
- Classification Algorithms: Decision Trees, SVM, k-NN Clustering: k-Means, Hierarchical
- Clustering Dimensionality Reduction Techniques: PCA, t-SNE
Advanced Machine Learning
- Ensemble Learning Techniques: Bagging, Boosting
- Random Forests and Gradient Boosting Machines
- Neural Networks Basics
- Deep Learning Fundamentals
- Reinforcement Learning
- Model Tuning: Grid Search, Random Search, Hyperparameter Optimization
Big Data Technologies
- Introduction to Big Data
- Hadoop Ecosystem: HDFS, MapReduce
- Apache Spark Basics
- Data Streaming with Kafka
- Working with NoSQL Databases
Natural Language Processing (NLP)
- Introduction to NLP
- Text Preprocessing: Tokenization, Lemmatization, Stemming
- Sentiment Analysis
- Topic Modeling with LDA
- Text Classification
- Word Embeddings: Word2Vec, GloVe
Time Series Analysis
- Introduction to Time Series Data
- ARIMA and SARIMA Models
- Forecasting Techniques
- Seasonal Decomposition of Time Series (STL)
- Handling Stationarity in Time Series
Deep Learning and Neural Networks
- Introduction to Neural Networks
- Convolutional Neural Networks (CNNs) for Image Data
- Recurrent Neural Networks (RNNs) for Sequential Data
- Autoencoders and Variational Autoencoders
- Transfer Learning with Pre-trained Models
- Frameworks: TensorFlow and PyTorch Basics
Ethics and Bias in Data Science
- Ethical Considerations in Data Handling
- Privacy Concerns and GDPR Compliance
- Identifying and Addressing Bias in Data and Models
- Building Explainable Models
Data Science Tools and Frameworks
- Jupyter Notebooks for Prototyping
- Working with Google Colab
- Using Docker for Data Science Projects
- Cloud Platforms for Data Science: AWS, Azure, Google Cloud
- Version Control with Git and GitHub
Capstone Projects
- Predictive Modeling with Real-world Data
- Customer Segmentation for E-commerce
- Sentiment Analysis on Social Media Data
- Time Series Forecasting for Stock Prices
- Building a Recommendation System
Python Tutorial
Python Data Type
Python Tutorial
Python Data Type
Python Tutorial
Python Data Type
Python Tutorial
Python Data Type
Why Choose StackSool?
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
Assured Job Interview Opportunities
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
Assured Job Interview Opportunities
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
Assured Job Interview Opportunities
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
Assured Job Interview Opportunities
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
Assured Job Interview Opportunities
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
Our Comprehensive Training Programs
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
Get Certified. Get Hired
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
Start today
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
Start today
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
Start today
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
Get Your Doubts Clarified
Faster than in Offline Classes
Get your doubts clarified faster than in offline classes with 24/7 live mentor support at StackSkool.
1500+ Mentors to Resolve Your Doubts
“Access a network of over 1,500 mentors ready to help you resolve your doubts and enhance your learning experience.”
