Data Science and Machine Learning is an in-depth course designed to equip learners with essential skills in data analysis, predictive modeling, and artificial intelligence. Participants will gain hands-on experience with various data tools and techniques, enabling them to extract valuable insights from complex datasets and make data-driven decisions.

• Understand the fundamental concepts of data science and its applications in real-world scenarios.
• Master the use of programming languages like Python and R for data manipulation and analysis.
• Learn advanced statistical techniques and machine learning algorithms to build predictive models.
• Apply data visualization techniques to effectively communicate insights and findings.
• Gain practical experience by working on real-life data science projects and challenges.

Introduction to Data Science

Overview of data science and its significance in various industries.
Introduction to data analytics tools and technologies.
Data Wrangling and Preprocessing

Data cleaning and preprocessing techniques.
Handling missing data and outliers.
Exploratory Data Analysis (EDA)

Understanding data distributions and patterns.
Data visualization for insights and storytelling.
Supervised Learning Algorithms

Linear regression, logistic regression, and decision trees.
Support vector machines (SVM) and random forests.
Unsupervised Learning Techniques

Clustering methods: k-means, hierarchical clustering.
Dimensionality reduction techniques: PCA, t-SNE.
Model Evaluation and Validation

Performance metrics for assessing model accuracy.
Cross-validation techniques.
Machine Learning Applications

Predictive modeling for classification and regression tasks.
Applying machine learning to real-world problems.

Upon successful completion of the course, a Certificate of Completion will be automatically generated and available for download.