Fundamentals of Data Science and Machine Learning
About Course
Online Certificate course on Data Science and Machine Learning
DEPARTMENT OF COMPUTER SCIENCE, APPLICATIONS AND ANIMATION
In Collaboration with
SIONA SOLUTIONS
Teaching methodology: Online Sessions, Doubt Solving Sessions and Hands on Training on real Projects
Online Class Timings |
Only on Sundays 11AM – 1PM |
| Duration: | 6 Months, 60 HRS |
| Fees: | 10,000.00 |
| Instructor |
Mr Avinash · Data Scientist in Siona Solutions · Certified from Global Certification on Data Science from INSAID – International School for Artificial Intelligence and Data Science. |
Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Brief Syllabus
| Sl No | Content | Theory/ Practical | No Of Hours |
| 1 | Basics of Statistics
– Importance of Statistics – Data, its Types, Data quality issues, Population & Sample – Fundamentals of Statistics – Popular Statistical plots – Probability – Random Variables – Normal Distribution – Central Limit Theorem – Hypothesis testing – Workout in MS. Excel |
Both | 4 |
| 2 | Basics of Python Programming Language
– Anaconda Installation – Google Colab – as Alternative – Building blocks of python – Variable and data types – Input/Output formatting – Operators and control flow – Data Types and functions – File Handling, Exception Handling |
4 | |
| 3 | Data Analysis with Python
– DS Fundamentals, – Data Operation with Numpy, – Data Manipulation with Pandas |
Practical | 6 |
| 3 | Data Visualization techniques
– Introduction to data visualization, – basic python data visualization – modules Matplotlib and Seaborn |
Practical | 6 |
| 4 | EDA and Storytelling
– Introduction to EDA – EDA Framework, – Case Studies |
Practical | 8 |
| 5 | EDA Hands on Project | Practical | – |
| 6 | Machine Learning -1
– Introduction to Machine Learning – Introduction, Linear Regression – Logistic Regression, – Model Evaluation Techniques – Case Studies – Hands on Project |
Practical | 12 |
| Term end project | – | ||
| Total Hours | 40 | ||
Hands on Projects to work:
| Sl No | Projects | No Of Hours |
| 1 | Exploratory Data Analysis (EDA) | 10 |
| 2 | Machine Learning -1 | 10 |
| Total Hours | 20 | |
Evaluation Method:
| Sl No | Evaluation Method – project submission | Marks |
| 1 | Project Video Demonstration | 20 |
| 2 | Jupyter Notebook Evaluation | 30 |
| 3 | Online Examination | 50 |
| Total Marks | 100 | |
Course Content
Introduction
-
Brief Introduction to Data Science
11:09 -
01:30:51
-
Anaconda Installation Steps
17:05 -
Data Science Class Session
01:44:01
Statistics Starter Kit
Basics of Python
Numpy and Pandas
Data Visualization in Python
Exploratory Data Analysis (EDA) in Python
Data Cleaning Session
EDA Project Guidelines
Machine Learning – 1
Model Evaluation Techniques
Student Ratings & Reviews
