Data Science for Professionals

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Centers

Via Zoom

Duration

3 Months

Mode

Online

Fees

Full Scholarship

About The Course

Accelerate Your Career with Uki Data Science

Unlock the power of data with our Data Science for Professionals course! This comprehensive 3-month online program is designed to equip you with essential skills and knowledge to thrive in the fast-paced world of data science.

With hands-on experience and expert-led sessions, you’ll master programming, machine learning, and data analysis through practical, real-world applications.

Full Scholarship Opportunity: Gain industry-ready skills at zero cost.
Expert-Led Sessions: Learn from experienced data scientists and industry leaders.
Hands-On Learning: Work on real-life projects and case studies.
Flexible Online Format: Learn from anywhere, at your own pace.
Networking Opportunities: Connect with like-minded peers and industry professionals.

Take the first step towards your data-driven future! 🚀

Ready to dive in? Enroll today!

Course Content

Week 1: Introduction to Data Science & Python

What is Data Science?
Who is a Data Scientist?
Why Data Science?
Introduction to Python

Week 2: Python and libraries in Python

Understand the basic syntax of Python functions, classes, and regular expressions
Understand Python file handling
Create basic Python programs using the concepts

Week 3: Data and libraries with Scipy

Use Python Packages to manipulate the data and files
Use Python Pandas to manipulate the data and files

Module 4: Control Flow in Python

Use Matplotlib to create plots for given data.
Data Acquisition
ETL Extraction Transformation Loading

Week 5: Machine Learning

Understand data dependency, linear regression, and distances.
Be able to apply linear regression in Python.

Week 6: Supervised Machine Learning

k Nearest Neighbours,
Naive Bayes,
Decision Tree,
Random Forest algorithms
Be able to apply these supervised learning algorithms in Python.

Week 7: Unsupervised Learning Algorithms

K-Means,
Hierarchical Clustering
and Density-based Clustering algorithms

Week 8: Data Pre-processing and Dimensionality Reduction

Understand how feature selection and dimensionality reduction methods such as PCA work.
Be able to apply these methods in Python to select features and/or reduce dimensionality.

Week 9: Model Evaluation and Improvement