Data Science Master Program
Course Overview
The Data Science Master Program by CourseDeal is a comprehensive course designed to equip learners with the skills to extract insights from complex datasets and make data-driven decisions. You’ll learn core concepts in statistics, data visualization, machine learning, and data manipulation using Python and R. The course emphasizes hands-on experience with real-world datasets, enabling you to build predictive models, conduct exploratory data analysis, and communicate insights effectively. Additionally, you’ll gain exposure to big data tools, SQL, and cloud-based data platforms to work with large-scale data efficiently. By the end of the program, learners will be prepared for careers as data scientists, business analysts, or machine learning engineers, with a portfolio of practical projects to showcase.
Key Highlights
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Master data analysis, visualization, and machine learning techniques
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Hands-on projects using real-world datasets for practical learning
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Learn Python, R, and SQL for data manipulation and analytics
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Exposure to big data technologies and cloud data platforms
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Build predictive models and actionable insights for business decisions
Tools & Technologies Covered
- Python
- R
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- SQL
- Tableau
- Power BI
- Hadoop
- Spark
Curriculum
- 6 Sections
- 0 Lessons
- 15 Hours
- Module 1: Introduction to Data ScienceThis module provides a solid foundation in data science principles, including the data science lifecycle, roles of a data scientist, and industry applications. You’ll learn about different types of data — structured, unstructured, and semi-structured — and understand the importance of data quality and preprocessing. The module introduces statistical concepts such as mean, median, variance, and probability distributions, which are essential for analyzing and interpreting datasets. Through practical exercises, learners gain experience in importing, cleaning, and exploring datasets using Python and R.0
- Module 2: Data Analysis and VisualizationIn this module, you’ll learn to extract meaningful insights from data using Python and R libraries. Topics include data wrangling with Pandas, exploratory data analysis, and visualization techniques using Matplotlib, Seaborn, and ggplot2. You’ll practice creating interactive charts, dashboards, and visual reports that highlight trends and patterns in data. The module emphasizes storytelling with data, teaching you to communicate analytical findings effectively to business stakeholders.0
- Module 3: Statistical Analysis and Hypothesis TestingThis module covers advanced statistical techniques to make inferences from data. You’ll learn about correlation, regression, ANOVA, and hypothesis testing to validate assumptions and analyze relationships between variables. Practical exercises include designing experiments, sampling techniques, and interpreting statistical outputs. These skills are crucial for making informed, data-driven business decisions and building predictive models with confidence.0
- Module 4: Machine Learning FundamentalsExplore machine learning concepts and algorithms, including supervised and unsupervised learning, regression, classification, clustering, and dimensionality reduction. You’ll learn to implement models using Python libraries such as Scikit-learn and evaluate their performance with metrics like accuracy, precision, recall, and F1-score. This module also covers overfitting, underfitting, and model optimization techniques to ensure robust predictions. By the end, learners will be able to build predictive models for real-world applications.0
- Module 5: Big Data and Cloud AnalyticsThis module introduces big data technologies and platforms for handling large-scale datasets. You’ll gain hands-on experience with Hadoop and Spark to process and analyze distributed data efficiently. Additionally, the module covers cloud-based data storage and processing solutions, including AWS and Azure data services. Learners will understand how to integrate big data tools with Python and R for scalable analytics projects.0
- Module 6: Data Science Project and CapstoneIn the final module, you’ll apply all your knowledge to a real-world data science project. The capstone project involves collecting data, preprocessing, performing exploratory analysis, applying machine learning models, and presenting actionable insights. This module also emphasizes project documentation, reproducibility, and creating a professional portfolio. Completing this project equips learners with practical experience to demonstrate their skills to employers and clients in the field of data science.0
Pass Percentage
