Data Scientist Certification Preparation Course Syllabus

Course Overview

The Data Scientist Certification Preparation course is designed to equip students with the skills and knowledge required to excel in data science roles. This comprehensive program covers key areas such as data analysis, machine learning, statistical methods, data visualization, and data engineering. The course prepares students for industry-recognized certification exams and helps them build a strong foundation in data science.

Course Benefits

• Acquire in-depth knowledge of data science principles and techniques.
• Learn from experienced, state-competent instructors.
• Prepare for and pass industry-recognized certification exams.
• Access to Google Slides and extensive learning resources.
• Enhance career opportunities and earning potential.

Course Outcomes

Upon completion of the course, students will be able to:

• Analyze and interpret complex data sets.
• Apply machine learning algorithms to solve real-world problems.
• Use advanced statistical methods for data analysis.
• Develop data visualizations to communicate insights.
• Prepare for and pass relevant certification exams.

Course Audience

• Aspiring data scientists and analysts.
• IT professionals seeking to specialize in data science.
• Students and graduates in computer science, mathematics, or related fields.
• Individuals looking to transition into a data science career.

Course Content

Module 1: Introduction to Data Science

• Lesson 1: Overview of Data Science
• Lesson 2: Data Collection and Cleaning
• Lesson 3: Exploratory Data Analysis

Module 2: Statistical Methods for Data Science

• Lesson 1: Descriptive Statistics
• Lesson 2: Inferential Statistics
• Lesson 3: Hypothesis Testing

Module 3: Machine Learning

• Lesson 1: Supervised Learning Algorithms
• Lesson 2: Unsupervised Learning Algorithms
• Lesson 3: Model Evaluation and Selection

Module 4: Data Visualization

• Lesson 1: Principles of Data Visualization
• Lesson 2: Visualization Tools (e.g., Matplotlib, Seaborn)
• Lesson 3: Creating Effective Visualizations

Module 5: Data Engineering

• Lesson 1: Data Warehousing Concepts
• Lesson 2: ETL Processes
• Lesson 3: Big Data Technologies (e.g., Hadoop, Spark)

Module 6: Advanced Topics in Data Science

• Lesson 1: Deep Learning Fundamentals
• Lesson 2: Natural Language Processing
• Lesson 3: Time Series Analysis

Course Duration

• Duration: 16 weeks

Mode of Instruction

• Online or Onsite Live Instructor-led

Class Schedule

• Monday and Wednesday: 10:00 AM – 1:00 PM EST / 6:30 PM – 8:30 PM EST
• Tuesday and Thursday: 10:00 AM – 1:00 PM EST / 6:30 PM – 8:30 PM EST
• Friday and Saturday: 10:00 AM – 12:00 PM EST

Registration Information

• Address: 2070 Sugarloaf Parkway Suite 600, Lawrenceville, GA 30045
• Phone: 678.919.1212
• WhatsApp: 678.900.2323
• Email: info@isbfinstitute.com
• Cost: $700 per month for USA residents, $450 for overseas students

Prerequisites

• Basic knowledge of programming (e.g., Python or R).
• Familiarity with fundamental statistical concepts.

Resources

• Books: “Data Science for Business” by Foster Provost and Tom Fawcett, “Python for Data Analysis” by Wes McKinney.
• Internet Practice Sites: Kaggle, Coursera, edX.

Materials Required

• Laptop with internet access
• Notebook and pen for taking notes
• Calculator

Assessment

• Weekly quizzes
• Midterm and final exams
• Project assignments
• Participation in discussions

Types of Hiring Companies

• Tech companies (e.g., Google, Amazon, Microsoft)
• Financial institutions (e.g., banks, insurance companies)
• Healthcare organizations
• Government agencies
• Consulting firms
• Retail companies
• Telecommunications companies
• Startups
• Academic and research institutions

Certification Exam

• Exam Types: ISBF Certified Data Scientist Exam, Data Science Council of America (DASCA) Senior Data Scientist (SDS) Certification, Microsoft Certified: Azure Data Scientist Associate
• Exam Body: ISBF Certification Board, DASCA, Microsoft
• Duration: 3 hours
• Number of Questions: 100 multiple-choice questions
• Cost: $150 (ISBF), $300 (DASCA), $165 (Microsoft)

Job Titles and Career Opportunities with Average Salaries

1. Data Scientist – Average Salary: $100,000 per year
2. Data Analyst – Average Salary: $65,000 per year
3. Machine Learning Engineer – Average Salary: $112,000 per year
4. Business Intelligence Analyst – Average Salary: $85,000 per year
5. Data Engineer – Average Salary: $105,000 per year
6. Research Scientist – Average Salary: $95,000 per year
7. Quantitative Analyst – Average Salary: $120,000 per year
8. Data Architect – Average Salary: $110,000 per year
9. Statistician – Average Salary: $80,000 per year
10. Data and Analytics Manager – Average Salary: $115,000 per year