Diploma in Data Science Course

Description

Uncover the secrets hidden within your data. In today's data-driven world, the ability to extract insights and build intelligent systems is a valuable asset. This comprehensive Diploma in Data Science Bundle equips you with the programming languages, statistical knowledge, and machine learning techniques needed to thrive in this exciting field.

What can you expect from this Bundle?

• Master the fundamentals: Build a strong foundation in programming languages like Python and R, essential tools for data manipulation and analysis.
• Dive into Machine Learning: Explore various machine learning algorithms and techniques for predictive modelling, pattern recognition, and data-driven decision making.
• Embrace Big Data: Learn how to handle and analyse massive datasets using tools like PySpark and Hadoop.
• Become a data-driven professional: Develop the skills and knowledge to solve real-world problems and unlock the power of your data.

Enrol now and become a data-driven problem solver and propel your career to new heights!

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.

CPD

Who is this course for?

Requirements

Career path

Certificates