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.


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

  • Introduction to Classification Problems
  • Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines
  • k-Nearest Neighbors (k-NN)
  • Introduction to Regression Problems
  • Linear Regression
  • Polynomial Regression
  • Ridge and Lasso Regression
  • Building Regression Models

  • 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
  • Aggregate Function
  • Group By Having Clause
  • Joins
  • Sub-queries