Data Science (Ai & ML)
This course structure provides a step-by-step introduction to data analysis, Python, machine learning, deep learning and AI without overwhelming students with technical details. It focuses on practical skills and real-world applications to make the content accessible to those without a technical background.
Course Details
Course Duration | 120 days |
Training Options | Online / onsite |
Course Price | 399 USD |
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About RIAAN IT Data Science (Ai & ML) Course
In this course you will get an introduction to the main tools and ideas which are required for Data Scientist/Business Analyst/Data Analyst/Analytics Manager/Actuarial Scientist/Business Analytic Practitioners. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with.
Curriculum
Introduction to Programming and Python Basics
- What is programming? Introduction to Python.
- Installing Python and an Integrated Development Environment (IDE).
Python Fundamentals
- Writing and running your first Python program (Hello World!).
- Understanding variables and data types. integers, floating-point numbers, strings.
Input and Output
- Basic functions. type(), str(), int(), float(), and round()
- Basic input and output using `input()` and `print()`.
Decision Making and Loops
- Conditional statements. `if`, `elif`, and `else`.
- Introduction to Boolean logic.
- Looping concepts. `for` and `while` loops.
More on Loops and Pattern Printing
- Using loops for repetitive tasks.
- Advanced looping patterns
- Pattern printing exercises
Lists and Functions
- Working with lists. creating, indexing, slicing, and modifying.
- Introduction to functions. defining and calling functions.
- Passing arguments and returning values from functions.
Advanced Data Types
- More about strings. string methods and formatting.
- Introducing tuples and dictionaries.
- Sets and their applications.
File Handling and Exceptions
- Reading and writing files in Python.
- Handling exceptions using `try` and `except` blocks.
- Using `with` statements for better file management.
Libraries and Modules
- Introduction to Python libraries and modules.
- Using built-in modules (e.g., `math`, `random`, `datetime`).
- Exploring external libraries using `pip`.
Getting Started with Data Analysis
- Introduction to Data and Its Importance
- Overview of Data Analysis and Its Applications
- Elements of Python for Data Analysis
- Understanding Data Sources and Types
- Data Cleaning and Data Preparation
Understanding Data Tools and Libraries
- Introduction to Data Analysis Tools (Excel, Google Sheets)
- Introduction to Data Visualization
- Overview of Python Libraries for Data Analysis
- Data Analysis Using Excel
- Visualizing Data with Charts and Graphs
Exploring Data with Pandas
- Introduction to Pandas and Its Importance
- Reading and Interpreting Data Tables
- Basic Data Cleaning Techniques
- Data Filtering and Sorting
- Creating Simple Data Visualizations
Introduction to Descriptive Statistics
- Overview of Descriptive Statistics
- Understanding Measures of Central Tendency
- Exploring Measures of Dispersion
- Visualizing Data Distributions
- Practical Data Analysis Tips and Tricks
Introduction to Python for Data Analysis
- Introduction to Python for Data Analysis
- Setting Up Your Python Environment
- Basic Data Structures in Python (Lists, Dictionaries)
- Reading and Writing Data Files (CSV, Excel) with Python
- Practical Data Cleaning with Python
Data Analysis with Pandas
- Introduction to Pandas and Its Role in Data Analysis
- Reading and Displaying Data with Pandas
- Data Cleaning and Preparation with Pandas
- Data Filtering and Sorting with Pandas
- Visualizing Data Using Pandas
Introduction to Data Visualization
- Understanding Data Visualization Principles
- Basic Data Visualization with Matplotlib
- Creating Charts and Graphs in Python
- Customizing Visualizations and Adding Labels
- Practical Data Visualization Projects
Exploring Descriptive and Inferential Statistics
- Descriptive Statistics with Python
- Measures of Central Tendency and Variability
- Introduction to Probability and Inferential Statistics
- Hypothesis Testing Concepts
- Real-World Data Analysis Case Studies
Understanding Machine Learning
- Introduction to Machine Learning and Its Applications
- Types of Machine Learning (Supervised, Unsupervised)
- Introduction to Scikit-Learn
- Building a Simple Machine Learning Model
- Real-World Machine Learning Use Cases
Machine Learning Fundamentals
- Introduction to Data and Data Preprocessing
- Data Exploration and Visualization
- Data Cleaning and Handling Missing Values
- Feature Engineering and Selection
- Model Evaluation Metrics
Classification
- Introduction to Classification Problems
- Logistic Regression
- Decision Trees and Random Forests
- Support Vector Machines
- k-Nearest Neighbors (k-NN)
Regression
- Introduction to Regression Problems
- Linear Regression
- Polynomial Regression
- Ridge and Lasso Regression
- Building Regression Models
Clustering
- Introduction to Clustering
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN and Other Clustering Methods
- Evaluating Clustering Performance
Dimensionality Reduction
- Dimensionality Reduction Overview
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Implementing Dimensionality Reduction
NLP Basics
- Introduction to Natural Language Processing
- Text Preprocessing
- Text Classification
- Named Entity Recognition (NER)
- Sentiment Analysis
Advanced NLP Topics
- Word Embeddings (Word2Vec, GloVe)
- Sequence-to-Sequence Models
- Chatbots and Conversational AI
- Text Generation with Recurrent Neural Networks (RNNs)
- NLP Applications and Case Studies
Image Basics
- Introduction to Computer Vision
- Image Preprocessing and Enhancement
- Image Segmentation
- Object Detection
- Image Classification
Advanced Topics
- Convolutional Neural Networks (CNNs)
- Transfer Learning in Computer Vision
- Face Recognition
- Image Generation with Generative Adversarial Networks (GANs)
- Computer Vision Applications
Reinforcement Learning Basics
- Introduction to Reinforcement Learning
- Markov Decision Processes (MDPs)
- Q-Learning
- Deep Q-Networks (DQNs)
- Policy Gradient Methods
AI Applications
- Robotics and Autonomous Systems
- Game Playing and AlphaZero
- Autonomous Vehicles
- AI in Healthcare
- Ethical Considerations in AI
Deep Learning Fundamentals
- Introduction to Deep Learning
- Neural Networks and Backpropagation
- Activation Functions and Optimization
- Regularization Techniques
- Implementing Neural Networks
Advanced Deep Learning Topics
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Generative Models (GANs and VAEs)
- Transfer Learning and Fine-Tuning
- Deep Learning Applications
Ethical Considerations
- AI Bias and Fairness
- Privacy and Data Security
- AI and Human Labor
- AI Regulations and Policies
- Responsible AI Development
Future Trends
- Explainable AI (XAI)
- Quantum Computing and AI
- AI in Space Exploration
- AI for Climate Change and Sustainability
- Future Frontiers of AI
- Data Connections and Data Transformation
- Importing data from various sources
- Data cleaning and transformation
- Data Visualization
- Building interactive reports and dashboards
- Using various visualization types (tables, charts, maps, etc.)
- Customizing the appearance and formatting of visuals
- DAX (Data Analysis Expressions)
- Writing and understanding DAX formulas for calculated columns and measures
- Aggregation functions (SUM, AVERAGE, COUNT, etc.) in DAX
- Introduction
- SQL Commands
- DDL, DML, DCL, TCL
- Aggregate Function
- Group By Having Clause
- Joins
- Sub-queries