This data science bootcamp is an intensive training course that aims to equip participants with the skills and knowledge needed to pursue a career in the field of data science. These bootcamps typically span several weeks and cover a range of topics such as statistics, machine learning, data visualization, and programming languages such as Python and R. During the bootcamp, participants will engage in hands-on exercises, projects, and assignments to gain practical experience in working with real-world data sets. They also receive mentorship and guidance from experienced data science professionals and network with peers in the field.
Why Data Science Bootcamp is in Demand?
The demand for data science professionals is growing faster than the supply, making it a lucrative and attractive career option for individuals with a background in mathematics, statistics, computer science, or related fields..
As more and more tasks are being automated, businesses are looking to data science to help them optimize their processes and make more informed decisions..
Companies are recognizing the value of data science in gaining insights into customer behaviour, predicting trends, and making data-driven decisions to improve their bottom line.
Introduction to the course
This module will be a basic module that will help you in framing your analytical perspective.
Python for Data Science
In this module we are going to start with Python for Data Science where all the important needful concepts will be covered in detail
Statistics for Data Analytics
In this module, we are going to learn about different statistical concepts which are not important for Data Science and also from an interview point of view.
Data Wrangling
Once you have the core skill of programming covered – dip your feet in the nitty – gritties of working with data by learning how to wrangle and visualize them
RDBMS and SQL for Data Science
Data is stored in Databases and databases can be of various types. But when we talk about Databases what first comes in our mind is SQL (Structured Query Language). Even in most of the data analytics job description SQL is always mentioned.
Machine Learning
Machines have increased the ability to interpret large volumes of complex data. Combine aspects of Computer science with statistics to formulate algorithms that helps machines draw insights from structured and unstructured data.
Natural Language Processing
NLP helps resolve ambiguity in language and adds useful numeric structure to the data for many downstream applications, such as speech recognition or text analytics.
Deep Learning & Computer Vision
Go beyond superficial analysis of data by learning how to interpret them deeply. Use deep learning nets to uncover hidden structures in even unlabeled and unstructured data.
Deploying ML Models using Flask, Streamlit (ON PREM) & Cloud
Go beyond the model preparation by learning how to deploy a machine learning model using Flask, Streamlit and Cloud etc.
Spark and Data Engineering
Get prepared on the concepts of Data Engineering as an add-on feature.
Sunday
10 AM
Thursday
08:00 PM - 10:00 PM
I attended BA Program at Pragra. My experience has been incredible. Pragra not only provides quality training and good value but also provides the whole support system which keeps students motivated while searching for jobs. All trainers are very approachable and supporting. Veer is an excellent mentor and he helped me understand Salesforce concepts very easily. Even after completion of the training he guided me for Certification and Interviews.Vivek is always approachable for any advice...
Arshdeep Kaur
Business Analyst
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