Course List

• R Programming for Data Science:

• Master the fundamentals of R programming, a powerful language for data analysis and visualisation.
• Learn how to import, clean, and manipulate data using R.

• Data Science & Machine Learning with Python:

• Gain proficiency in Python, a versatile language widely used in data science applications.
• Explore core machine learning concepts and build basic prediction models using Python libraries.

• Python Data Science:

• Deepen your understanding of Python for data science, including data structures, libraries like NumPy and Pandas, and data wrangling techniques.
• Learn how to implement data visualisation tools like Matplotlib and Seaborn to create compelling data stories.

• SQL for Data Science, Data Analytics and Data Visualisation:

• Master SQL, a crucial language for querying and retrieving data from relational databases.
• Learn how to integrate SQL with other data science tools for efficient analysis.

• Quick Data Science Approach from Scratch:

• Gain a foundational understanding of the data science workflow, from data acquisition to model deployment.
• Learn how to approach data science problems in a structured and efficient manner.

• Computer Science With Python:

• Build a solid foundation in computer science principles, including algorithms, data structures, and problem-solving techniques.
• Learn how to apply these concepts to solve data science challenges using Python.

• Big Data Analytics with PySpark Power BI and MongoDB:

• Explore big data technologies like PySpark for distributed data processing and MongoDB for handling NoSQL databases.
• Learn how to integrate these tools with Power BI for effective data visualisation and business intelligence.

• SQL NoSQL Big Data and Hadoop:

• Gain a comprehensive understanding of different database types, including SQL, NoSQL, and Big Data frameworks like Hadoop.
• Learn how to choose the appropriate database technology for different data science projects.

• Machine Learning Basics:

• Demystify core machine learning concepts like supervised and unsupervised learning algorithms.
• Learn how to evaluate and interpret the performance of machine learning models.

• Machine Learning for Aspiring Data Scientists: Zero to Hero:

• Progress from beginner to intermediate in machine learning, building more complex models and exploring advanced techniques.
• Learn how to apply machine learning to solve real-world data science problems.

• Business Intelligence and Data Mining:

• Discover best practices for extracting valuable insights from data for business decision making.
• Learn data mining techniques to uncover hidden patterns and trends in large datasets.

• Deep Learning & Neural Networks Python - Keras:

• Delve into the world of deep learning and artificial neural networks, a powerful technique for complex data analysis.
• Learn how to implement deep learning models using popular libraries like Keras in Python.

• Project on Deep Learning – Artificial Neural Network:

• Apply your deep learning knowledge to solve a practical data science problem through a hands-on project.
• Gain experience in building and deploying deep learning models.

• Essential Training on Deep Learning Heuristic using R:

• Explore deep learning concepts and implement algorithms using the R programming language.
• Learn how to leverage R's capabilities for specific deep learning tasks.

• Statistics & Probability for Data Science & Machine Learning:

• Develop a strong foundation in statistical concepts essential for data analysis and machine learning models.
• Learn how to interpret data, conduct hypothesis testing, and assess the reliability of your results.