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Analyse data and communicate insights using Python and statistics.
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View detailsData Analytics with Python Internship
A self-paced virtual internship in practical data analytics with Python, covering NumPy, pandas, data cleaning, exploratory analysis and visualisation, ending with a portfolio-ready analysis project.
Course duration3 months – 6 months
Modules4 module exams
Learning hours60 hours
Certificate at70% average
CertificateQR-verifiable
Overview
The Data Analytics with Python Internship is a self-paced virtual internship for learners who already know basic Python and want to apply it to real-world data. Python, with libraries such as NumPy, pandas and Matplotlib, is one of the most widely used toolsets for analysing data, and this programme focuses on the everyday workflow of an analyst: loading data, cleaning it, exploring it and communicating what it shows.
The internship is organised into four modules. You begin with the Python and NumPy foundations needed for analysis, then learn to load, select and summarise data with pandas. The third module covers cleaning and transformation, including missing values, data types, grouping and merging. The final module covers exploratory data analysis and visualisation with Matplotlib and Seaborn. Each module includes detailed study notes with code examples, followed by a timed online multiple-choice exam.
You work at your own pace in Jupyter Notebook or Google Colab and finish with a capstone project in which you analyse a public dataset and present your findings in a well-documented notebook. After meeting the exam requirements you receive a QR-verifiable internship certificate that shows your internship period.
What this internship covers
- Python data structures and NumPy arrays for numerical work
- Loading, inspecting, selecting and filtering data with pandas
- Handling missing values, duplicates and incorrect data types
- Grouping, aggregating, merging and reshaping DataFrames
- Exploratory data analysis, correlation and outlier checks
- Charts with Matplotlib and Seaborn, and a capstone analysis notebook
Who is this for?
This is an intermediate-level internship. You should already be comfortable with basic Python syntax, and you need a computer with Jupyter Notebook installed or access to Google Colab.
- Students of computer science, statistics, engineering, commerce or management who want hands-on analytics skills
- Graduates preparing for data analyst, business analyst or MIS roles
- Excel or SQL users who want to automate and scale their analysis with Python
- Prerequisite: Python basics (variables, lists, loops, functions) and school-level statistics such as mean and median
Course duration: 3 months – 6 months · 60 hours of learning
What you’ll learn
Use NumPy arrays and vectorised operations for numerical calculations
Load, inspect, select and filter data with pandas DataFrames
Clean datasets by handling missing values, duplicates and wrong data types
Summarise and combine data using groupby, merge and pivot tables
Perform exploratory data analysis including distributions, correlation and outliers
Build clear visualisations with Matplotlib and Seaborn and document findings in a notebook
Benefits
- QR-verifiable internship certificate showing the internship period
- Portfolio-ready capstone analysis notebook on a public dataset
- Module-wise study material with runnable Python code examples
- Self-paced online learning that fits around college or work
- Instant exam results after each module exam
Course modules
Modules unlock one after another. Pass each module exam to open the next.
Refresh the Python features used most in analysis and learn how NumPy arrays make numerical work fast and concise.
- The Data Analytics Workflow and Jupyter Notebooks
- Python Data Structures and Comprehensions
- NumPy Arrays, Shape and Indexing
- Vectorised Operations, Broadcasting and Boolean Masks
Outcomes: Use lists, dictionaries and comprehensions for data tasks; Create, index and reshape NumPy arrays; Apply vectorised operations and boolean masks
Load data into pandas DataFrames, inspect its structure and select, filter and sort the rows and columns you need.
- Series and DataFrames
- Reading CSV and Excel Files
- Inspecting Data: head, info, describe and value_counts
- Selecting, Filtering and Sorting with loc and iloc
Outcomes: Create Series and DataFrames and read CSV and Excel files; Inspect data with head, info and describe; Select and filter data with loc, iloc and conditions
Fix common data quality problems and reshape data by grouping, merging and creating new columns.
- Missing Values: isna, dropna and fillna
- Duplicates, Data Types and Text Cleaning
- Creating Columns with apply and Conditions
- groupby, merge and pivot_table
Outcomes: Detect and handle missing values and duplicates; Convert data types and clean text columns; Group, merge and reshape DataFrames
Explore datasets with descriptive statistics and charts, communicate insights clearly and complete the capstone analysis.
- Descriptive Statistics and Distributions
- Correlation and Outlier Detection
- Charts with Matplotlib and Seaborn
- Communicating Insights and the Capstone Notebook
Outcomes: Describe distributions, correlation and outliers; Create suitable charts with Matplotlib and Seaborn; Present findings in a clear, reproducible notebook
How it works
Choose & enroll
Pick your course duration, register, verify your email and pay securely online.
Study each module
Your course dates are set automatically. Learn the topics and study material of each module.
Pass module exams
Take the module exams in order — pass one to unlock the next. 2 attempts per module.
Get your e-Certificate
Reach a 70% average across all modules to unlock your verifiable e-Certificate.
Assessment & certification rules
Four module exams of 10 multiple-choice questions each (15 minutes per module). Pass each module with 60% and reach a 70% overall average to earn your internship certificate.
| Module exams | 4, taken in order (each module unlocks after the previous one is passed) |
|---|---|
| Questions per module | 10 multiple-choice |
| Pass mark per module | 60% (unless stated on the module) |
| Needed for the e-Certificate | All modules passed and an average of 70% or more (best score of each module, weighted by marks) |
| Attempts | 2 free attempts per module; an extra attempt costs ₹199 |
| Completion deadline | All module exams must be completed within your course duration (extension of 30 days available for ₹299) |
| Certificate release | On the end date of your course, after all requirements are met |
| Grading | Automatic, immediately after submission |
Your e-Certificate
On completion you receive a QR-verifiable digital internship certificate showing your name, the program, your internship period and learning hours. Anyone can verify it online using the certificate number.
CERTIFICATE
OF ACHIEVEMENT
This is to certify that
Your Name
has successfully completed all module examinations of the professional programme
Data Analytics with Python Internship
3 months programme · 4 modules · 60 hours
COURSE PERIODStart date – End dateISSUE DATEDD Month YYYY
ISSUED BYMr. Ajay NautiyalManaging Director
DS-INDA-2026-000000
This is a digitally generated e-Certificate and does not require a physical signature.
View sample certificate (PDF)
Sample for illustration — your certificate carries your name, dates, a unique number and a QR code.
- Unique certificate number and verification ID
- QR code linking to the public verification page — employers can check it in seconds
- Shows your course period and learning hours
- Validity: Lifetime
Internship program
This is a self-paced virtual internship. You read the study material, run the code examples in Jupyter Notebook or Google Colab and take each module exam online when you are ready.
How the internship works
- Enrol and get access to all four modules in your dashboard
- Study each module's notes and run the example code yourself
- Take the timed multiple-choice exam for each module and see your result instantly
- Pass every module with at least 60% and keep an overall average of 70% or more
- Complete the capstone project brief given in Module 4 and download your certificate
Capstone project
In the final module you choose a public dataset, clean and explore it with pandas, create at least five meaningful charts and write up your key findings in a Jupyter notebook. The notebook can be shared on GitHub as part of your portfolio.
Internship certificate
Learners who meet the exam requirements receive a digital internship certificate that shows the internship period and carries a QR code for online verification. The programme does not include live mentoring or placement services.
Ratings & reviews
Frequently asked questions
The programme is designed for about 60 hours of study. Since it is self-paced, most learners finish in six to eight weeks, but you can adjust the pace to suit your schedule.
You should know basic Python, including variables, lists, dictionaries, loops and functions, along with simple statistics such as mean and median. Python itself is not taught from scratch.
Each module has a timed online multiple-choice exam. You get 2 free attempts per module, and extra attempts can be purchased if you need more.
When you pass every module with at least 60% and reach a 70% overall average, you receive a digital internship certificate that shows your internship period and can be verified through its QR code.
The capstone brief is in Module 4. You analyse a public dataset of your choice in a Jupyter notebook, covering cleaning, exploration, at least five charts and a written summary of findings.
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