Data Analytics & Machine Learning

Course Description

The Data Analytics & Machine Learning short-term course offers a comprehensive introduction to the dynamic fields of data analytics and machine learning, equipping participants with essential knowledge and practical skills to analyze data, build predictive models, and extract valuable insights from complex datasets.

What You’ll Learn From This Course

• Introduction to Data Analytics: Explore the fundamentals of data analysis, learning about data types, data preprocessing, and data visualization techniques to gain a clear understanding of data structures.
• Python for Data Analysis: Develop proficiency in using Python, a powerful programming language widely used in data analytics, and learn to manipulate and analyze data efficiently using popular libraries like Pandas and NumPy.
• Exploratory Data Analysis (EDA): Master EDA techniques to unveil patterns, trends, and outliers in data, gaining valuable insights that drive decision-making.
• Statistical Analysis and Hypothesis Testing: Understand the principles of statistical analysis and learn to apply hypothesis testing to draw meaningful conclusions from data.
• Introduction to Machine Learning: Get introduced to the core concepts of machine learning, including supervised and unsupervised learning, and learn about various algorithms used for classification, regression, and clustering tasks.
• Machine Learning with Scikit-Learn: Gain hands-on experience with Scikit-Learn, a popular machine learning library in Python, to build and evaluate machine learning models.
• Feature Engineering: Learn how to preprocess and engineer features to enhance model performance and interpretability.
• Model Evaluation and Validation: Understand techniques to assess the performance of machine learning models and prevent common pitfalls like overfitting.
• Introduction to Deep Learning: Explore the basics of neural networks and deep learning, including popular architectures like CNNs and RNNs.
• Real-world Data Projects: Engage in practical data analytics and machine learning projects, applying your knowledge to real-world datasets, and presenting your findings.

Certification

Get industry-relevant certificates recognized by  STED Council on successful completion of the course.