Diploma Courses
.Technology & Innovation.
Diploma in Data Science & Predictive Analytics
Overview
The Diploma in Data Science & Predictive Analytics focuses on building proficiency in analyzing large datasets, developing predictive models, and utilizing machine learning algorithms to generate actionable insights. This program prepares students for dynamic roles in data analytics, business intelligence, and AI-powered solutions.
Duration: 1 to 2 years (Full-time or Part-time options available).
Learning Method: Online, face-to-face, or hybrid.
Objective: Develop critical thinking and technical expertise to extract meaningful insights from data and predict future trends effectively.
Minimum qualification: High school diploma or equivalent.
Recommended Background: Mathematics, statistics, or programming knowledge is advantageous but not mandatory.
1. Curriculum aligned with industry needs, emphasizing hands-on learning.
2. Extensive use of popular tools such as Python, R, SQL, and Tableau.
3. Real-world case studies and projects for practical application.
4. Internship opportunities with top analytics firms and tech companies.
5. Access to mentorship from industry experts and career development support.
1. Gain expertise in data visualization, statistical analysis, and machine learning.
2. Understand data preprocessing, cleaning, and integration techniques.
3. Develop predictive models using regression, classification, and clustering algorithms.
4. Learn advanced analytics concepts like deep learning and time series forecasting.
5. Apply data science to fields such as marketing, finance, healthcare, and technology.
Module Guide
1. Introduction to Data Science: Basics of data science, data lifecycle, and ethical considerations.
2. Programming for Data Analytics: Python and R programming essentials.
3. Mathematics and Statistics for Data Science: Probability, linear algebra, and hypothesis testing.
4. Data Visualization Techniques: Tools like Tableau, Power BI, and Matplotlib.
1. Machine Learning Fundamentals: Supervised and unsupervised learning methods.
2. Predictive Modeling: Building models for forecasting and decision-making.
3. Big Data Analytics: Hadoop, Spark, and cloud-based data platforms.
4. AI and Deep Learning: Neural networks, TensorFlow, and Keras applications.
5. Capstone Project: Design and implement a data-driven solution under expert guidance.
Course Fees
The course fees for domestic students range from $5,000 to $10,000, and mode of study chosen. These fees are designed to be competitive and accessible, ensuring that students receive top-quality education without financial strain. Flexible payment plans may also be available to accommodate individual financial situations.
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