In this module, you’ll be introduced to the fundamentals of Python and Machine Learning. You’ll also learn the uses of python and machine learning in data analysis.

Here, you’ll learn the building blocks of Python programming, starting with variables, arithmetic operators, and comparison operators. You’ll also learn to set up your development environment by installing Anaconda and working with Jupyter Notebook, which will serve as your primary tool for coding and data analysis. 

In this module, you’ll explore the fundamental concepts of statistics and applied mathematics, which are essential for data analysis and machine learning. You’ll begin with an overview of basic statistical concepts and fundamentals, helping you understand how data is collected, measured, and interpreted.

In this module, you’ll learn the fundamental building blocks of Python programming, starting with variables—the containers used to store data. Also, you’ll learn about arithmetic operators, which allows you to perform basic mathematical operations.

In this module, you’ll learn about comparison operators, which are essential for making logical decisions in Python.

In this module, you’ll explore three essential concepts in Python programming: indexing, tuples which are immutable (unchangeable) collections used to store multiple items in a single variable. and list which is one python’s most flexible data structure.

In this module, you’ll learn about looping, a fundamental concept in Python that allows you to automate repetitive tasks efficiently.

Here, you’ll be introduced to NumPy (Numerical Python), a powerful library for numerical computing in Python.

In this module, you’ll learn about Pandas, one of the most powerful libraries for data manipulation and analysis in Python. Pandas provides easy-to-use data structures like Series and DataFrames, which allow you to handle structured data efficiently.

Here, you’ll explore the statistical capabilities of Pandas, which allow you to perform in-depth data analysis with ease. You’ll start by learning how to use Pandas functions to calculate descriptive statistics, such as me

In this module, you’ll be introduced to Matplotlib, one of the most widely used libraries for data visualization in Python. You’ll start by learning how to install and import Matplotlib and understand its core components, such as Figure, Axes, and Plot.

In this module, you’ll explore the world of AI-assisted programming, where artificial intelligence enhances the development process by providing intelligent code suggestions, debugging assistance, and automation.

In this project, you’ll apply your data analysis and visualization skills to explore relationships between various HR-related factors within an organization.

In this project, you’ll analyze different modes of transport (MOT) for Liquefied Natural Gas (LNG) in Canada using data analytics and visualization techniques.

In this project, you’ll conduct a comparative hazard analysis of Crude Oil and Highly Volatile Liquids (HVL) using data analytics and visualization techniques.

In this module, you’ll gain a foundational understanding of Machine Learning (ML)—a branch of artificial intelligence that enables computers to learn from data and make predictions without explicit programming.

In this module, you’ll dive into regression algorithms, which are used to predict continuous numerical values based on input data. Regression is a fundamental concept in machine learning, commonly applied in fields like finance, marketing, and healthcare for tasks such as predicting sales, stock prices, and medical outcomes.

In this module, you’ll explore classification algorithms, a core component of machine learning used to predict categorical outcomes or labels. Classification is widely used in applications such as spam detection, image recognition, medical diagnosis, and more.

In this module, you’ll explore dimensionality reduction, a crucial technique in machine learning used to reduce the number of features (variables) in a dataset while retaining important information. This process helps in simplifying models, improving computational efficiency, and mitigating issues like overfitting and multicollinearity.

In this module, you’ll dive into k-Means Clustering, one of the most popular and widely used unsupervised machine learning algorithms. k-Means is a clustering algorithm that groups data into a specified number of clusters (k) based on their similarity.

In this module, you’ll be introduced to Weka, a powerful open-source software suite for data mining and machine learning.

In this module, you’ll gain a deeper understanding of the relationship between Machine Learning (ML) and Artificial Intelligence (AI), and how these technologies work together to create intelligent systems capable of solving complex problems

In this final module, you’ll apply everything you’ve learned throughout the course in a comprehensive Capstone Project. This project will challenge you to solve a real-world problem using machine learning, data analysis, and AI techniques.

In this segment of the training, We will help you prepare for technical and HR interviews for Data Analysis Roles. Also, we will provide you with training in CV writing skills and LinkedIn Optimization. Everything you need to grab any job opportunity.

In this segment, you will have a Ask-Me-Anything session with a top data scientist to guide you further.

In this segment, a top freelancer will share with you a blueprint that guarantees you to make at least $1,000 Monthly working Remotely As A Data Scientist… Even if you know nothing about remote work.

The Skilled Worker visa allows qualifying individuals to come to the UK to work for an approved sponsor in an eligible role. We will give you a Guide on How To Apply for Skilled Worker Visa and Tech Nation Visa.

Register today to enjoy our Workspace with Ultra Fast  Satellite Internet (Starlink) for FREE.