Data Science Training
8 WEEKS BOOTCAMP
Data Science course covers the concepts and tools needed throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. At completion, students will have a portfolio demonstrating their mastery of the material.
Eligibility Criteria
BASIC KNOWLEDGE OF PROGRAMMING
BASICS OF DATA STRUCTURES
Data Science Training Outline
Week 1
Data Science Toolkit: R, RStudio, RMarkdown
Week 2
Introduction to github, Reshaping Data, Merging Data, Subsetting, Regular Expression
Week 3
Exploratory Data Analysis-1: Clustering, Dimension Reduction, Exploratory Graphs
Week 4
Exploratory Data Analysis-2: ggplot2, hierarchical and Kmeans Clustering approaches
Week 5
Exploratory Data Analysis-3: Plotting Lattice, Plotting Base, Principles of EDA
Week 6
Reproducible Research, RMarkDown, Reproducible Research Concepts Statistical Inferences – Concepts of probability, Hypothesis Testing
Week 7
Regression Models, Predictions, Relative Importance, Cross Validation, Data Slicing
Week 8
Summarized Data pipelines
Boot Camp Outcomes
INDEMAND SKILL
HANDS-ON EXPERIENCE
TRUSTED KNOWLEDGE
TRAINING CERTIFICATE
Course Instructor
MUHAMMAD USMAN JOYIA
Lecturer FAST-NUCES
Muhammad Usman is PhD scholar at FAST National University of Computer and Emerging Sciences Chiniot – Faisalabad Campus. His research interests are predictive analytics and machine learning applications for cross domains. Currently, he is working as Lecturer in Department of Computer Science at FAST National University of Computer and Emerging Sciences, Pakistan. He is passionate about deep learning applications for multi-domains problems. He has also trained more than five hundred students across Pakistan.
Education:
• PhD (CS) FAST – NUCES CFD Campus (2021 contd.)
• MS (CS) FAST – NUCES CFD Campus (2016-2019)
• BS (CS) FAST – NUCES CFD Campus (2012-2016)
OBJECTIVES & OUTCOMES
Data Science Objectives
Course objectives – The objective to learn the data sciences are as follows:
1. To learn and distinguish among the various types of data and it sources.
2. To explore, manipulate, visualize, and analyze various kinds of data from different sources.
3. To learn and apply data science approaches to derive the knowledge from data for decision support.
4. To better understand the optimization and analysis techniques of data for better decision-making in real-life scenarios
Data Science Outcomes
Learning outcomes – Upon successful completion of the course, students should be able to:
1. Program in R Language for Data Science pipelines
2. Obtaining Data from various sources (KDD process)
3. Perform Exploratory Data Analysis
4. Reproduceable Research
5. Apply Regression Models
6. Use Machine Learning Algorithms
7. Develop Data Products
Scope of Bootcamp
The scope of this course to familiarize the students with the data science pipeline in the recent age. The students will get to know about the recent approaches to knowledge discovery in databases. The course will enable students to find insightful patterns from real-life data with detailed visualizations. Upon completion of this course, students will be mastering analytics, visualizations, and the fascinating world of Machine Learning. This course will provide you with an opportunity to become the part of the productive environment that FAST offers to its students.