Accelerated Data Scientist Certification Program

Accelerated Data Scientist Certification Program Presented by the ISBF Institute Website: https://Isbfinstitute.com Duration: 15 Weeks | 2 Sessions per Week | 4 Hours per Session | 120 Total Contact Hours Delivery Format: Live via Zoom or Onsite at Partner Institutions Languages Offered: English, French, Spanish, and other languages as available Instructor: Certified Data Scientist Instructor (Assigned by ISBF) Program Tuition: $1,750 (includes books, software, datasets, practice exams, and certificate of completion)

Why You Need a Data Scientist Certification

In today’s data-driven world, data science is powering innovation in healthcare, finance, retail, energy, and nearly every other industry. Businesses, governments, and organizations depend on certified data scientists to analyze trends, extract insights, and make intelligent decisions that increase efficiency, profitability, and impact.

The global demand for data scientists has grown by over 650% since 2012, with an average annual salary exceeding $120,000 in the U.S. and remote opportunities available globally. However, the field is highly competitive and requires not just technical ability but also sharp critical thinking, ethical responsibility, and a strong grasp of communication and business acumen.

A certification from organizations such as Google, IBM, or Microsoft validates your skills and opens doors to roles such as data analyst, machine learning engineer, AI specialist, and data engineer. This course provides rigorous training to earn these credentials and thrive in the field.

Course Overview

The Accelerated Data Scientist Certification Program is a 15-week comprehensive training initiative that equips learners with technical, analytical, and practical business skills to launch or grow a career in data science. The curriculum combines certification exam preparation with hands-on coding, data modeling, and machine learning labs using Python, R, SQL, and leading industry software.

The course covers data collection, preprocessing, visualization, algorithm design, supervised and unsupervised learning, model evaluation, cloud integration, and more. A strong emphasis is placed on ethical data use, communication of insights, and cross-functional collaboration.

Program Benefits

Participants will:

• Master Python, R, SQL, and Tableau for data analysis and visualization

• Learn data wrangling, statistical analysis, and machine learning fundamentals

• Prepare for global certifications (Google Data Analytics, IBM Data Science Professional, Microsoft Certified Data Scientist Associate)

• Create and present real-world capstone projects with live data

• Build a job-ready GitHub portfolio and personal data science website

• Receive career coaching and job placement assistance

Target Audience

This program is for:

• College graduates and professionals transitioning into data science

• IT or analytics staff upgrading their technical toolkit

• Career switchers looking for flexible, high-income roles

• Entrepreneurs and startup founders needing data skills

• International students seeking U.S.-recognized certification

Learning Outcomes

Graduates of the program will:

• Understand key data science processes and techniques

• Develop algorithms and models using supervised/unsupervised methods

• Use Python libraries (Pandas, NumPy, Scikit-learn, TensorFlow) for analysis

• Visualize findings and automate reports

• Apply data ethics and interpret findings for decision-makers

• Pass a leading industry certification exam

Prerequisites

No prior coding experience is required. Participants should:

• Be comfortable with basic math and Excel

• Be curious, analytical, and committed to completing the program

• Have a working computer with internet access

Participant Guide & Tools

Each participant receives:

• Data Science Fundamentals Workbook

• Python, R, SQL, and Jupyter Notebook Setup Guide

• Free access to datasets and visualization tools (Kaggle, Tableau Public)

• Sample certification questions and timed mock exams

• Resume template and GitHub portfolio starter

• Certificate of Completion from ISBF

Certification Preparation & Registration

Participants will be guided to register for a globally recognized certification exam such as:

• Google Data Analytics Certificate

• IBM Data Science Professional Certificate

• Microsoft Certified Data Scientist Associate

The ISBF team will provide support for registration, fee payment, timeline planning, and study strategy.

Job Placement Assistance

ISBF’s Career Success Center supports graduates with:

• Resume & LinkedIn profile optimization

• Technical interview coaching & mock challenges

• Access to job boards and partner employer networks

• Job readiness assessments and career navigation tools

• Professional references and recommendations

Tuition & Registration

Tuition: $1,750 (includes full training and exam prep resources) Register now at: https://Isbfinstitute.com Email: info@finanpower.com | Phone: (678) 921-2121 Payment plans and limited scholarships available

15-Week Curriculum Schedule

Each week features two 4-hour sessions (Day 1 and Day 2). Projects and assignments are required weekly.

Week 1: Introduction to Data Science & Industry Tools

• Day 1: Careers, Roles, Ethics & Data-Driven Decision Making

• Day 2: Introduction to Python, Jupyter, GitHub, and Data Sources

Week 2: Data Wrangling & Cleaning

• Day 1: Data Types, Importing & Exporting, Missing Data

• Day 2: Pandas, NumPy, and DataFrame Manipulation

Week 3: Exploratory Data Analysis (EDA)

• Day 1: Descriptive Statistics, Outliers, Visualization with Matplotlib

• Day 2: Correlation, Histograms, Heatmaps, Storytelling with Data

Week 4: SQL for Data Science

• Day 1: SQL Basics, Queries, Joins, Subqueries

• Day 2: Using SQL with Pandas & Practice Projects

Week 5: Statistical Thinking & Inference

• Day 1: Probability, Hypothesis Testing, Distributions

• Day 2: A/B Testing and Confidence Intervals

Week 6: Intro to Machine Learning

• Day 1: Supervised Learning: Regression & Classification

• Day 2: Scikit-learn, Model Evaluation Metrics

Week 7: Unsupervised Learning & Clustering

• Day 1: K-Means, Hierarchical Clustering

• Day 2: Dimensionality Reduction, PCA

Week 8: Advanced Machine Learning & NLP

• Day 1: Decision Trees, Random Forests, XGBoost

• Day 2: Natural Language Processing, Sentiment Analysis

Week 9: Data Visualization & Dashboards

• Day 1: Tableau, Power BI, Interactive Dashboards

• Day 2: Project: Build a Public Portfolio Dashboard

Week 10: Time Series & Forecasting

• Day 1: Time Series Analysis, Trends, Seasonality

• Day 2: ARIMA Models & Forecasting Projects

Week 11: Cloud Integration & Big Data

• Day 1: Google Cloud, AWS, Azure Data Services

• Day 2: Spark, Hadoop, and Distributed Data Tools

Week 12: Capstone Project Development

• Day 1: Team Project Planning, Dataset Selection

• Day 2: Milestone Reviews, Feedback Sessions

Week 13: Certification Exam Prep

• Day 1: Review Certification Domains & Practice Questions

• Day 2: Full-Length Mock Exam & Study Plan

Week 14: Career Launch Lab

• Day 1: Resume, LinkedIn, Portfolio Website Reviews

• Day 2: Behavioral & Technical Interview Preparation

Week 15: Final Capstone & Graduation

• Day 1: Capstone Project Presentations

• Day 2: Final Review + Exam Registration + Graduation Ceremony