Course Overview
The Certified Data Scientist program is designed to provide a comprehensive foundation in data science, equipping you with the essential skills needed to analyze and interpret complex data. This course is perfect for those who want to dive into the world of data analytics, predictive modelling, and machine learning, gaining practical knowledge that can be applied in various industries.
Course Highlights
Python Foundation
This module introduces Python programming with a focus on data science essentials. You’ll learn Python syntax, data structures, and control flow, and work with key libraries like Pandas, NumPy, Matplotlib, and Seaborn for data manipulation and visualisation. The module also covers file handling, Jupyter Notebooks, error handling, and includes a mini-project to apply your skills.
Data Science Foundation
The Data Science Foundation module provides essential skills in data exploration, preprocessing, and visualisation using Python libraries like Pandas and Matplotlib. You’ll learn statistical concepts, feature engineering, and data wrangling to handle real-world datasets. The module includes hands-on projects and basic predictive modelling, equipping you with a strong foundation for advanced data science and AI topics..
Machine Learning Introduction
The Machine Learning Introduction module provides a comprehensive foundation in the basics of machine learning. It includes:
➢ Supervised Learning: Understand how algorithms learn from labeled data, including linear and logistic regression.
➢ Unsupervised Learning: Explore clustering techniques like K-means and hierarchical clustering to identify patterns in unlabeled data.
➢ Reinforcement Learning Introduction: Get a basic overview of reinforcement learning and its applications.
➢ Key Algorithms: Dive into decision trees, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM).
➢ Model Evaluation: Learn about metrics like accuracy, precision, recall, and F1-score to evaluate model performance.
➢ Hands-on Projects: Apply concepts in real-world scenarios through practical projects.
➢ Tools and Libraries: Use essential tools like Scikit-learn, Pandas, and NumPy for data manipulation and model building.
Machine Learning Expert
This module deepens your expertise with advanced algorithms like ensemble methods (Random Forest, Gradient Boosting), neural networks, and deep learning techniques (CNN, RNN). It focuses on model optimization, hyperparameter tuning, and performance evaluation. Engage in hands-on projects that mimic real-world scenarios, equipping you with skills to build robust, scalable models for complex data challenges.
SQL & NoSQL
Master data management by learning to manipulate and query data using SQL for relational databases and NoSQL for unstructured data. This section is crucial for understanding how to work with large datasets efficiently.
Power BI
Gain proficiency in Power BI, a powerful tool for data visualisation and business intelligence. Learn to create interactive dashboards and reports that turn data into actionable insights.
Tableau
Develop advanced visualisation skills using Tableau. This module covers connecting to data sources, data blending, and creating sophisticated visualisations to effectively communicate your findings.
This course empowers you with a robust set of data science skills, from basic Python programming to advanced machine learning techniques. By the end, you’ll be well-prepared to tackle data-driven challenges in various business contexts, making informed decisions based on data analysis and predictive modelling.
Learning Outcome
By completing the Certified Data Scientist program, you will gain essential skills in Python, data analysis, machine learning, and AI. You will be proficient in using SQL, NoSQL, Power BI, and Tableau for data manipulation and visualisation. This program equips you to create data-driven models that deliver actionable insights, making you valuable in any data-focused role.
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Enroll in the Certified Data Scientist program today and unlock your potential in the world of data science
