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About This Course

The Applied Data Science, Analytics & Machine Learning program provides a practical introduction to collecting, cleaning, analyzing, visualizing, and interpreting data.
Students begin with data science foundations and Python programming before progressing to NumPy, Pandas, SQL, statistics, data visualization, and exploratory data analysis. The course then introduces machine learning, including regression, classification, clustering, model evaluation, and responsible AI practices.
Throughout the program, students complete exercises, assignments, quizzes, and portfolio projects using realistic datasets. The course concludes with a capstone project in which each learner solves a complete data problem and presents actionable findings.

What You'll Learn

• By the end of the course, students should be able to:
• Write Python programs for data analysis.
• Clean and transform structured datasets.
• Query relational databases using SQL.
• Perform exploratory data analysis.
• Apply descriptive and introductory inferential statistics.
• Create professional visualizations and dashboards.
• Build regression, classification, and clustering models.
• Evaluate machine-learning models correctly.
• Explain results to technical and nontechnical audiences.
• Complete and present an end-to-end data science project.

Career Opportunities

• After completing the Applied Data Science, Analytics & Machine Learning course, learners can pursue entry-level and junior positions such as:
• Junior Data Analyst
• Business Intelligence Analyst
• Reporting Analyst
• Junior Data Scientist
• Machine Learning Assistant
• Operations Data Analyst
• Marketing Data Analyst
• Financial Data Analyst
• Research Data Analyst
• Product Data Analyst
• Data Visualization Specialist
• SQL Reporting Specialist
• Python Data Analyst
• Data Quality Analyst
• Freelance Data Analyst
• Graduates may work in technology, finance, healthcare, telecommunications, education, retail, logistics, government, nonprofit organizations, research, and consulting.
• Freelance and Entrepreneurial Opportunities
• Graduates can also provide services such as:
• Data cleaning and preparation
• Business reporting
• Sales and customer analysis
• SQL database reporting
• Dashboard and visualization development
• Survey and research analysis
• Predictive analytics
• Spreadsheet automation
• Machine-learning prototypes
• Data-driven business consulting
• Career Progression
• With additional experience, portfolio projects, and advanced study, graduates may progress toward roles such as:
• Data Scientist
• Machine Learning Engineer
• Senior Data Analyst
• Business Intelligence Developer
• Data Engineer
• Analytics Consultant
• AI or Machine Learning Specialist
• Data Analytics Manager
• Career outcomes depend on the learner’s performance, portfolio, experience, and employer requirements; course completion does not guarantee employment.

Prerequisites

• Basic computer literacy.
• no previous programming experience required.

Who Should Take This Course?

• This course is designed for:
• Beginners seeking a career in data science or data analytics.
• Students and graduates interested in technology and data-driven careers.
• Professionals transitioning into data analysis, business intelligence, or machine learning.
• Business owners and managers who want to make data-informed decisions.
• Researchers who need practical data-analysis and visualization skills.
• Developers seeking foundational machine-learning knowledge.
• Entrepreneurs who want to analyze customer, sales, and operational data.
• Anyone with basic computer skills who wants to learn Python, SQL, analytics, and machine learning.
• No previous programming or data science experience is required.

Certificate Information

Certificate of Completion in Applied Data Science, Analytics & Machine Learning
Students who successfully satisfy the course requirements will receive a verifiable SolveTech Academy digital certificate.


Certificate Eligibility Requirements
A student must:
Complete at least 80% of the required lessons.
Submit all compulsory assignments.
Obtain an overall course score of at least 70%.
Pass the final examination.
Complete and pass the capstone project.
Have no outstanding course or administrative requirements.

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