Online
Acquiring data
Duration
10 hours
Level
NSQF Level 5
Delivery
Self-paced online
Assessment
FutureSkills Prime & Accenture
Certificate
FutureSkills Prime & Accenture
Overview
This course, developed by Accenture and delivered through FutureSkills Prime, introduces the basic concepts of data required for data science. You work through the categories of data and what each is good for, the terminology and frameworks around big data, metadata and why it matters, and the practical business of validating what you have collected. The second half moves into Pandas, the Python library that most data work in India runs on, and specifically the DataFrame, which is the structure you will spend most of your working life inside.
It is part of the AI Ascend program and is deliberately positioned as a building block. The concepts here are prerequisites for everything more advanced in data science, which is why it is worth doing properly rather than skipping to the modelling.
The course is delivered through video lessons explaining the concepts, knowledge checks to test retention, and hands-on exercise documents that put the learning into practice.
What you will learn
- The various types of data and the methods used to acquire and store them
- Big data terminology and the frameworks used to work with it
- Metadata, what it is and why it matters in a data pipeline
- Categories of data and their practical applications
- Data validation concepts and techniques
- DataFrames in the Python Pandas library, and how to manipulate them
Prerequisites
The course is designed for learners entering the data field and for those already working with data who want to strengthen their understanding of data processing procedures and technologies.
You will need access to Jupyter Notebook with Python 3.6 or above to complete the hands-on exercises. Basic familiarity with Python is helpful, though the course starts from foundations.
Skills and tools
Python, Pandas, Jupyter Notebook, DataFrames, big data frameworks, metadata handling and data validation.
Certificate
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Frequently Asked Questions
Find answers to the most common questions about the Acquiring Data course.
The Acquiring Data course introduces learners to the fundamentals of acquiring, storing, validating and preparing data for data science applications. It covers data types, metadata, big data concepts, data validation and practical data handling using Python and Pandas.
This course is suitable for students, aspiring data analysts, data science beginners and working professionals who want to strengthen their understanding of data acquisition, processing and validation.
You will learn about different types of data, methods used to acquire and store data, big data terminology, metadata, data validation techniques and practical data manipulation using Python, Pandas and DataFrames.
Basic familiarity with Python can be helpful, although the course begins with foundational concepts. Learners will use Python and Jupyter Notebook while completing practical exercises.
The course has a duration of approximately 10 hours and is delivered in a self-paced online format, allowing learners to progress according to their own schedule.
Yes. The course is delivered online in a self-paced format, giving learners flexibility to study according to their availability.
The course introduces Python, Pandas, Jupyter Notebook, DataFrames, big data frameworks, metadata handling and important data validation concepts used in data processing.
Learners receive a co-branded certificate from FutureSkills Prime and Accenture on successful completion. Clearing the SSC NASSCOM assessment can also earn the relevant industry-recognised NASSCOM certification.
Yes. The course is designed for learners entering the data field as well as professionals who want to strengthen their understanding of data processing and data acquisition procedures.
Data acquisition is an important early stage of the data science workflow. Understanding how data is collected, structured, validated and prepared helps learners work with datasets more reliably before moving to analysis and modelling.