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Dr. Akanksha Bhardwaj

Program Coordinator

Course Overview

The course enables you to learn fundamental concepts of statistics along with its practicality using Python. The course has been specifically designed to fulfil the basic needs concerning elementary concepts of statistics and getting started with Python. You will master the technique of how Python is deployed for Data Science.

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Course Key Features
  1. Concepts of Statistics, major focus on Descriptive Statistics and Exploratory Data Analysis using Python.
  2. Opportunity to learn-relearn the concepts as per current industry demand.
  3. Opportunity to learn new and booming technology, Python.
  4. Hands on experience on various Datasets.

Skills Covered

  1. Concepts of Statistics
  2. Working with Python and Python Libraries NumPy, Pandas, Matplotlib and Seaborne
  3. In-depth understanding of Data structure and Data Manipulation
  4. Detailed coverage of Descriptive statistics and Exploratory Data Analysis
  5. Learn to analyse data graphically
  6. Gain expertise in implementing Statistical concepts using Python.


Data Science is all about science behind data. The Statistical and research methodology concepts help in understanding the science behind the data. Additionally, the requirements of Data Scientists are taking boom therefore right knowledge acquisition at right time becomes a principal key behind learning this course

Course Curriculum

Eligibility: Undergraduate and Post graduate students of RDIAS can enrol for the course on volunteer basis.

Pre-Requisites: No specific prior knowledge for course. Though, a basic knowledge of programming and research methodology concepts can help.

Course Content: Sessions has been divided into three modules:

Module 1: Statistical Concepts and their Applications


Duration (10 Hours)

1.1  Outline- Descriptive Statistics

1 Hour

1.2  Data and Histogram, Central Tendency and 3Ms

2 Hours

1.3  Measures of Dispersion, Range, IQR

1 Hour

1.4  Standard Deviation

1 Hour

1.5  Coefficient of variation, the Empirical Rule and Chebyshev Rule

1 Hour

1.6  Five number summary, Boxplot, outliers and other plots

2 Hours

1.7  Correlation Analysis

2 Hours



Module 2: Data Science using Python


Duration (14 Hours)

2.1  Introduction to Python, NumPy and Pandas

2 Hours

2.2  Joining NumPy arrays

2 Hours

2.3  NumPy Array Mathematics, saving and loading NumPy Arrays

2 Hours

2.4  Pandas Dataframe & functions

2 Hours

2.5  Descriptive Statistics using Python

2 Hours

2.6  Hands on different Datasets

4 Hours



Module 3: Data Visualization with Python


Duration (6 Hours)

1.1    Introduction to Data Visualization

1 Hour

3.2 Data Visualization using Matplotlib and Seaborn

3 Hours

3.3 Hands on with Dataset

2 Hours



After completion of each Module online Quiz using Google forms will be conducted and qualifier certificates will be allotted to the students who attain minimum 70% marks. Students who are not able to qualify the minimum attainment percentage with respect to a particular module will not proceed for the next Module. Each student will be given 2 chances to attempt and qualify a particular module. After completion of Module 3, a comprehensive learning assessment will be taken and the student will be awarded with the certificate with the average percentage mentioned over it.

** Top three performers will awarded with “Performance Excellence” Certificate.

Course Advisor

Dr. Akanksha Bhardwaj

Associate Professor
Rukmini devi Institute of Advanced Studies
Rohini, Delhi

Dr. Akanksha Upadhyaya working as Associate Professor and Head AI Cell in Rukmini Devi Institute of Advanced Studies. She has been associated with RDIAS from more than  8 years with overall experience of more than 12 years, as an Academician. She has teaching experience in the field of Information Technology, Computer Science and Management Studies. She is Ph.D. in the Area of Data Authentication and fraud detection from Amity University, Noida. She has been awarded with Outstanding Thesis Award 2020 by GRF. She has been a beneficent speaker and Resource Person in the field of Data Science,Research Methodology and Data Authentication in number of Research workshops and faculty development programs of National level. Under her vast experience, she has published more than 25 research papers that are included in Scopus Indexed conference proceedings  and Scopus Indexed Journals such as IEEE, Springer, Inderscience, Elsevier, IGI Global to name a few. She has been Session chair in number of International and National conferences. She is also Reviewer and Editor of various International book publications and journals including Inderscience, Lambert, CRC press (Taylor & Fransic), Springer Nature, Peer Reviewed Journals etc.

Programme Fees

Programme Fees (Mention the mount and provision of refund if students meet the attendance/performance criteria): 

Fees: A refundable fees of INR 700/- can be charged but purely on refundable basis (T&C) applied.

Fee Refund T&C

  • On successful completion of the course or,
  • If not able to qualify any module but having 80% attendance.
  • The Refund is not allowed to drop out students having attendance less than 80%.
Register Now !

Register Now!

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