Data Science
Course Description
This Data Science course offers a comprehensive introduction to the essential techniques and tools used in the field of data science. You will learn how to extract valuable insights from data through statistical analysis, machine learning, and data visualization. The course covers key topics such as data wrangling, exploratory data analysis, predictive modeling, and more, providing you with the skills needed to analyze complex datasets and make data-driven decisions.
What you'll learn in this course?
In this course, you'll explore various aspects of data science, including data manipulation, statistical analysis, and machine learning algorithms. You will gain hands-on experience with popular tools and libraries such as Python, and SQL, and learn how to apply these skills to real-world data problems. By the end of the course, you'll be adept at turning raw data into actionable insights and building predictive models.
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Perform data cleaning and preprocessing using Python
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Analyze data using statistical methods and data visualization techniques
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Build and evaluate machine learning models for predictive analysis
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Work with databases
By the end of this course, you'll have a solid foundation in data science and be equipped to tackle a variety of data challenges. You will develop a portfolio of projects that demonstrate your ability to analyze data, build models, and present your findings effectively.
Course Curriculum
Explore the world of data with powerful tools and techniques. You'll gain skills in data cleaning, statistical analysis, and machine learning to turn raw data into actionable insights.
- Introduction to SQL and MySQL Database Setup
- Data Types and Operators in SQL
- Data Definition Language (DDL): Creating, Altering, and Truncating Tables
- Implementing Data Constraints for Data Integrity
- Data Manipulation Language (DML): Selecting, Filtering, Inserting, and Updating Data
- Advanced DML Operations: Deleting, Ordering, Grouping, and Filtering Data
- Combining Tables with SQL Joins
- Performing Calculations with Aggregate Functions
- Writing Nested Queries: Subqueries in SQL
- Ensuring Data Consistency with Transactions
- Introduction to Python, Anaconda Setup, and Package Management with pip
- Variables, Data Types, and Basic Operations
- Control Flow: Conditional Statements, Loops, and Iteration
- Type Conversion and Exception Handling in Python
- Working with Lists: Data Structures and Manipulation
- Exploring Tuples and Sets: Immutable Data Structures
- Key-Value Data Storage with Dictionaries
- Object-Oriented Programming (OOP) Fundamentals: Introduction to Classes
- Advanced OOP Concepts: Inheritance, Polymorphism, and Encapsulation
- Organizing Code with Modules and Packages
- Enhancing Code Reusability with Decorators
- Efficient Iteration with Generators
- Introduction to Git: Setup, Initialization, Adding & Committing Changes
- Branching, Tagging, Merging, and Resolving Conflicts in Git
- Collaborative Development with GitHub: Forking and Pull Requests
- NumPy Basics: Arrays, Data Types, and Attributes
- NumPy Array Operations: Indexing, Slicing, Broadcasting, and Mathematical Functions
- Introduction to Pandas: DataFrames, Series, and Basic Functions
- Data Cleaning and Manipulation with Pandas: Advanced Functions and Techniques
- Basic Data Visualization with Matplotlib: Creating Charts and Plots
- Advanced Data Visualization with Matplotlib: Customization and Techniques
- Introduction to Machine Learning: Concepts and Workflow
- Introduction to scikit-learn: Syntax and Basic Usage
- Data Preprocessing for Machine Learning
- Classification Algorithms: Theory and Implementation
- Regression Algorithms: Theory and Implementation
- Model Evaluation and Tuning: Metrics and Techniques
- Clustering Algorithms: Unsupervised Learning
- Dimensionality Reduction: Feature Extraction and Selection
- Feature Engineering: Creating and Transforming Features
- Model Selection and Cross-Validation: Choosing the Best Model
- Ensemble Methods: Combining Multiple Models
- Support Vector Machines (SVM): Theory and Implementation
- Neural Networks: Introduction and Basic Concepts
- Hyperparameter Tuning: Optimizing Model Performance
- Practical Machine Learning Project in Google Colab
- Introduction to Big Data and Apache Spark
- Spark Installation and Setup
- Spark RDDs and DataFrames: Fundamentals and Operations
- Data Processing with Spark: Transformations and Actions
- Introduction to Hadoop and the Hadoop Distributed File System (HDFS)
- MapReduce Basics: Processing Big Data with Hadoop
- Big Data Project and Review: Applying Hadoop Concepts
- Introduction to Power BI: Data Visualization and Business Intelligence
- Data Import and Transformation in Power BI
- Creating Reports and Dashboards with Power BI
- Data Modeling and Analysis in Power BI
- Power BI Dashboard Project: Practical Application
- Introduction to Hugging Face: Natural Language Processing with Transformers
- Installing Hugging Face Transformers and Loading Pretrained Models
- Using Tokenizers for Text Processing
- Fine-tuning Pretrained Models for Text Classification
- Named Entity Recognition with Hugging Face
- Creating Custom Pipelines for NLP Tasks
- Introduction to Streamlit: Building Interactive Web Applications
- Building Basic Web Applications with Streamlit
- Deployment and Sharing of Streamlit Applications
- Project Planning and Data Collection
- Data Analysis and Feature Engineering
- Model Building and Evaluation
- Building Interactive Dashboards
- Project Documentation and Presentation
Reviews
Course includes:
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Level
Expert
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Duration
3 Months
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Certifications
Yes
Payment:
Pay After Placement (Only a registeration fee required)
