Course Description

This is a comprehensive program designed to equip leaners with the necessary knowledge and skills
to become proficient data scientists. With a duration long enough to cover all the necessary topics,
it’s the perfect choice for anyone seeking an in-depth understanding of these subjects.

Course Objectives:

• Develop critical thinking and problem-solving skills to tackle complex data science problems
• Understand and apply mathematical concepts and computational techniques required for Data Science and Machine Learning.
• Understand the Data Science workflow and its components.
• Analyze and interpret data using wide range of statistical techniques such as descriptive statistics, probability, statistical distributions, and statistical inference
• Demonstrate Proficiency in programming languages such as Python and R for data science and machine learning
• Apply principles of data visualization and storytelling to create impactful data presentations.
• Understand and implement best practices for data preprocessing and cleaning.
• Perform exploratory data analysis and gain insights from the data.
• Develop machine learning models and algorithms for predictive analytics and decision making
• Understand math behind popular machine learning algorithms and optimization.
•  Apply advanced concepts in deep learning, natural language processing, and computer vision to solve real-world problems
• Deploy machine learning models in production environments.
• Work collaboratively on mini projects and case studies to analyze data and generate insights.

Learning Outcomes:

After successful completion of this program, a learner will be able to

• Identify and formulate Data Science problems.
•  Design and implement data-driven solutions for business problems using machine learning and artificial intelligence techniques
• Analyze and interpret complex data sets using a range of statistical techniques, including descriptive statistics, probability distributions, and hypothesis testing.
• Analyze and visualize data using various software tools, such as Excel, R, Python, Tableau, and Power BI
• Create compelling data visualizations and use data storytelling techniques
•  Communicate effectively data-driven insights with non-technical stakeholders and present business strategies and recommendations
• Analyze data using SQL and retrieve relevant information for business decisions.
• Clean and preprocess data to prepare it for analysis and modelling
•  Perform exploratory data analysis to identify patterns and trends in data.
• Work with popular machine learning libraries and frameworks
• Build predictive and prescriptive models using regression, classification, clustering, dimensionality reduction and deep learning algorithms.
•  Perform feature engineering, and model selection for machine learning applications.
• Build and evaluate deep learning models for computer vision, image recognition, object detection, and classification.
• Use text analytics and natural language processing to extract meaningful insights from unstructured data and build AI systems
• Apply time series analysis techniques to forecast future trends
• Create and present reports on machine learning projects
• Deploy machine learning models using various deployment methods and tools