Grokking Data Science

A streamlined, hands-on data science course that equips you with essential Python, statistics, and ML skills—and guides you to land your first data-scientist role.

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access	Lifetime
level	Beginner
certificate	Certificate of completion
language	English

#Educative

Description
Additional information

What will you learn in Grokking Data Science Course

Master Python libraries for data science—NumPy, Pandas, and Matplotlib—and apply them to real datasets
Grasp statistics fundamentals—probability distributions, significance testing, and Bayesian concepts—for robust analysis
Understand core machine learning algorithms, model evaluation metrics, and end-to-end project workflows

Execute a complete ML pipeline in a Kaggle-style challenge—from EDA and preprocessing to model tuning and deployment
Build career readiness skills: navigate imposter syndrome, craft a data-scientist resume, and interview with confidence

Program Overview

Module 1: Python Fundamentals for Data Science

⏳ 25 Lessons

Topics: Python basics, NumPy array operations, Pandas data manipulation, and foundational data visualization techniques
Hands-on: Complete NumPy and Pandas exercises; take the Data Visualization quiz

Module 2: The Fundamentals of Statistics

⏳ 12 Lessons

Topics: Statistical features, probability concepts, distributions (Uniform, Binomial, Normal, Poisson), and significance testing
Hands-on: Work through box-plot exercises and the Statistics quiz

Module 3: Machine Learning 101

⏳ 10 Lessons

Topics: Types of ML algorithms, supervised vs. unsupervised learning, model evaluation, and performance metrics
Hands-on: Complete quizzes on algorithm concepts and model evaluation

Module 4: End-to-End Machine Learning Project

⏳ 9 Lessons

Topics: Systematic ML workflow: exploratory data analysis, preprocessing, modeling, fine-tuning, and maintenance
Hands-on: Tackle a Kaggle-style challenge through guided assignments and quizzes

Module 5: The Real Talk

⏳ 3 Lessons

Topics: Career success strategies, overcoming imposter syndrome, continuous learning paths
Hands-on: Reflect with self-assessment quizzes and finalize your personal action plan

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Job Outlook

The average salary for a data scientist in the U.S. is $127,730 per year
U.S. employment of data scientists is projected to grow 36% from 2023 to 2033, much faster than average for all occupations
High demand spans tech, finance, healthcare, and e-commerce sectors for skills in data analysis and ML model deployment
Freelance and consulting roles abound for specialists in data visualization, statistical modeling, and end-to-end ML pipelines

9.5Expert Score

Highly Recommendedx

A highly interactive, no-fluff course that walks you from Python basics to real-world ML projects with career guidance.

Value

Price

9.2

Skills

9.4

Information

9.5

PROS

In-browser, text-based environment with zero setup and immediate feedback
Balanced focus on theory, hands-on quizzes, and a capstone ML project for portfolio building
Career-oriented “Real Talk” module addressing resume tips and imposter syndrome

CONS

Purely text-based format may challenge visual learners who prefer video content
Advanced topics like deep learning and big-data integration are beyond its introductory scope