Programme at a Glance

No. of Participants

Limited to 25

 

Course Fee

Rs. 35,000/=
08 Saturdays

(from 8.30 AM to 5.30 PM)

Training course on Data Science with Python

Data Science with Python training course teaches participants to learn Data Science and its implementations using Python. It explores both theoretical and practical aspects of Linear Algebra, Statistics, Data Wrangling, Data Visualization and Machine Learning from the very basic level to the advance concepts. The course is conducted physically at UCSC premises.

Target Audience

Some experience in Data Analytics and experience in Programming.

Learning Outcomes

Upon completion of this course, students will be able to do the
following:
LO1. Understand the theoretical concepts and practical implementations of Linear Algebra and Statistics for Data Science and
learn how to implement with Numpy library.
LO2. Understand the concepts of Data Wrangling (Data Manipulation and Data Cleaning) and implementation with Pandas library.
LO3 Understand the concepts of Data Visualization and implementation with Matplotlib, Pandas, Seaborn and Plotly libraries.
LO4 Understand the concepts of Machine Learning and implementation with Scikit Learn library.
LO5 Understand the concepts of Deep Learning and implementation with Scikit Learn library as well as Tensorflow and Keras libraries.
LO6 Learn how to build web-based applications embedded with Data Science.

Online Registration

Click Here

Contact

csc@ucsc.cmb.ac.lk

Course Overview

Day 01

  1. Numerical, Relational and Logical Operations in Python
  2. Primitive Data Types and Compound Data Structures in Python
  3. Flow Controls, Functions and Lambdas in Python
  4. Numerical Computations with Numpy Library
  5. Data Manipulation and Cleaning with Pandas Library

Day 02

  1. Data Visualization with Matplotlib Library
  2. Data Visualization with Pandas Library
  3. Data Visualization with Seaborn Library
  4. Data Visualization with Plotly Library

Day 03

  1. Supervised Learning, Unsupervised Learning, Reinforcement Learning Theory
  2. Regression and Classification Theory
  3. Linear Regression using Numpy, Scikit Learn Libraries
  4. Logistic Regression using Numpy, Scikit Learn Libraries

Day 04

  1. ANN Theory
  2. ANN using Scikit Learn Library
  3. ANN using TensorFlow and Keras

Day 05

  1. Dimensionality Reduction – Principal Component Analysis (PCA) Theory
  2. PCA using Numpy and Scikit Learn Libraries
  3. Decision Trees Theory
  4. Decision Trees using Scikit Learn and Keras Libraries
  5. Random Forest Theory
  6. Random Forest using Scikit Learn and Keras Libraries

Day 06

  1. SVM Theory
  2. SVM using Scikit Learn Library
  3. Clustering Theory
  4. K Means Clustering Theory
  5. K Means Clustering using Scikit Learn Library
  6. Hierarchical Clustering Theory
  7. Hierarchical Clustering using Scikit Learn Library

Day 07

  1. Convolutional Neural Networks (CNN) Theory
  2. CNN using Scikit Learn and Keras Libraries
  3. Recurrent Neural Networks (RNN) Theory
  4. RNN using Scikit Learn and Keras Libraries

Day 08

  1. Introduction to Streamlit
  2. Streamlit Layout System, Interactive Widgets, Dynamic Visualizations
  3. Working with data in Streamlit
  4. Dashboard Creation with Streamlit

Resource Persons

Lecturer in-charge: Ms. Gayani Rupasinghe