DATA SCIENCE DIPLOMA COURSE INFO

Data Science Course Description

Course Title: Data Science Certification Program

Course Duration: 12 months (approx. 300 hours)

Course Overview:

The Data Science Certification Program is designed to equip students with the skills and knowledge required to extract insights from data and drive informed decision-making in various industries. This comprehensive program covers the fundamentals of data science, including data preparation, visualization, machine learning, and statistical modeling.

Course Objectives:

Upon completing this course, students will be able to:

1. Collect, process, and analyze large datasets to extract meaningful insights.
2. Apply statistical and machine learning techniques to solve real-world problems.
3. Visualize data to communicate findings effectively to stakeholders.
4. Design and implement data-driven solutions to drive business growth.
5. Work with various data science tools and technologies, including Python, R, SQL, and Tableau.

Course Outline:

Module 1: Data Fundamentals (20 hours)

1. Introduction to data science
2. Data types and structures
3. Data visualization basics
4. Data quality and preprocessing

Module 2: Statistics and Probability (30 hours)

1. Descriptive statistics
2. Inferential statistics
3. Probability theory
4. Hypothesis testing

Module 3: Data Visualization (20 hours)

1. Data visualization principles
2. Visualization tools (Tableau, Power BI, D3.js)
3. Storytelling with data

Module 4: Machine Learning (40 hours)

1. Supervised learning (regression, classification)
2. Unsupervised learning (clustering, dimensionality reduction)
3. Model evaluation and selection
4. Deep learning basics

Module 5: Data Mining and Text Analytics (20 hours)

1. Data mining techniques
2. Text preprocessing and analysis
3. Sentiment analysis

Module 6: Big Data and NoSQL Databases (20 hours)

1. Introduction to big data
2. NoSQL databases (MongoDB, Cassandra)
3. Data warehousing and ETL

Module 7: Data Science with Python (30 hours)

1. Python basics
2. NumPy, Pandas, and Matplotlib
3. Scikit-learn and TensorFlow

Module 8: Data Science with R (20 hours)

1. R basics
2. Data manipulation and visualization
3. Statistical modeling and machine learning

Module 9: Capstone Project (40 hours)

1. Real-world project implementation
2. Data collection and analysis
3. Model development and deployment

Module 10: Industry Applications and Career Development (20 hours)

1. Data science in various industries
2. Career paths and job opportunities
3. Resume building and interview preparation

Learning Outcomes:

Upon completing this course, students will:

1. Understand the data science lifecycle and workflow.
2. Apply statistical and machine learning techniques to solve real-world problems.
3. Visualize data to communicate findings effectively.
4. Design and implement data-driven solutions.
5. Work with various data science tools and technologies.
6. Develop a portfolio of projects demonstrating data science skills.

Career Opportunities:

1. Data Scientist
2. Data Analyst
3. Business Analyst
4. Data Engineer
5. Machine Learning Engineer
6. Business Intelligence Developer
7. Quantitative Analyst
8. Research Scientist

Prerequisites:

1. Basic math and statistics knowledge
2. Familiarity with programming concepts (Python or R)
3. High school diploma or equivalent

Target Audience:

1. Professionals seeking to transition into data science roles
2. Students pursuing a career in data science
3. Business analysts and decision-makers seeking data-driven insights
4. Researchers and academics interested in data science applications

Certification:

Upon completing the course, students will receive a Data Science Certification.