Become a Data Scientist: A Practical Guide
This program guides you through the essential steps to become a data scientist, focusing on practical application and building a portfolio, aligning with a job description for data scientists.
Program Modules
Start with Python
Begin your journey by learning the fundamentals of Python. This module focuses on basic syntax, data structures, and essential libraries. Aim to complete this module to gain the basic pre-requisite skill.
Python Fundamentals
WeeklyLearn variables, data types, operators, control flow, and functions in Python. Focus on code writing skills and problem-solving using Python.
Linear Algebra Essentials
Master the core concepts of linear algebra, essential for understanding machine learning algorithms. This module builds on the Python module; completing it will greatly enhance the next module.
Matrix Operations
WeeklyLearn matrix addition, subtraction, multiplication, and inversion. Practicing using Python libraries like NumPy and SciPy.
Machine Learning Algorithms
Learn and implement popular machine learning algorithms, including regression, classification, and clustering. Focus on understanding the underlying mathematical principles and practical applications.
Regression Algorithms
WeeklyUnderstand and implement linear and logistic regression. Analyze the performance of the algorithms.
Working with Big Data
Gain experience working with large datasets using tools like Spark and Hadoop. Focus on data processing, storage, and analysis.
Spark Fundamentals
WeeklyLearn the basics of Spark for distributed data processing. Focus on setting up a Spark environment and performing basic data transformations.
Career Development
Focus on getting ready for data science positions.
Update Your LinkedIn Profile
MonthlyShowcase your skills and projects on LinkedIn to attract potential employers. Tailor your profile to specific data science job descriptions.
What You'll Accomplish
- Learn the fundamentals of Python programming.
- Understand and apply linear algebra concepts in data science.
- Implement and evaluate common machine learning algorithms.
- Gain experience working with big datasets using relevant technologies.
- Build a professional LinkedIn profile to showcase skills and experience, making you appealing to recruiters.
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