Fundamentals of Data Science and Machine Learning

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About Course

Online Certificate course on Data Science and Machine Learning


In Collaboration with


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

 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

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
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What Will You Learn?

  • Learn about fundamentals of data science. Open for both, programmers and non-programmers, hands experience on real world projects, understand how organizations take decisions from the data.

Course Content


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

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