Post Graduate Program in Data Science

Data science: Why?

One of the fields in technology with the highest growth is data science. According to IDC, data will have increased significantly over the last ten years and will total 175 zettabytes by 2025. Big Data investment is expected to increase from $41 billion in 2019 to $116 billion in 2027, according to Fortune Business Insights.

Businesses large and small throughout the world, including Amazon, Microsoft, LinkedIn, and Facebook, are searching for experts who can manage their data effectively and derive business insights. The average yearly increase in hiring for data scientists over the next five years will be 37%. By 2029, there will be a 31% increase in the number of Data Scientist positions, resulting in an anticipated 3 million new positions for Data Science and Analytical roles.

Data scientists get high salaries as a result of this high demand. They average $123,000 and range from $86,000 in the lowest quintile to $157,000 in the highest. Data analysts get average pay of $75,200 while AI/ML engineers make an average salary of $149,800. Data science jobs need a variety of skill sets.

The benefits of this program

This program offers a thorough curriculum with a learning path that is matched with industry needs. It includes integrated laboratories and practical projects so that you may get experience as you study. Through its online live virtual classrooms with peer and teacher interaction, it goes well beyond self-paced learning. While remaining adaptable to your lifestyle and schedule, it may be finished in a year with five to ten hours a week of work.

This data science course consists of a culminating Capstone Project, 25+ hands-on Projects, and Integrated Labs across three domains. The student receives industry-recognized IBM credentials after successfully completing the courses.

Why This Program Is Successful

The whole data science training course is provided using the Online Bootcamp methodology, giving you the ability to quickly pick up the skills you need for the best Data Science positions available today. This comprises:

  • Actual Sessions Delivered by Renowned Specialists
  • Training for Employment Using Skill-Based Learning
  • Through interactive labs and projects, applied to learn
  • Learning in Cohorts and Peer Collaboration
  • 24/7 Support for Learning

What Makes This Program Unique

The data science certification contains a thorough learning route that covers all necessary data science abilities. Courses consist of:

  • Writing R code, using R data structures, and developing your own functions for data science
  • R programming and data science: applicability in the actual world and practical experience
  • For data science, use Python a programming language created by IBM
  • Python with Data Science: Python-based data science and analytics approaches
  • Computer learning: supervised, unsupervised, and practical modeling
  • Building visuals, managing data, and creating dashboards using Tableau
  • Natural Language Processing: Natural Language Toolkit for Python: Basics of Natural Language Processing (NLTK)
  • Data Science Capstone: a practical, industry-relevant project that integrates all of the courses into a capstone that is suitable for a portfolio
  • Electives: Purdue University's Academic Master Class with Industry a Master's Class 

You will study the following data science tools, libraries, and languages in the program:

  • Python and its computational libraries NumPy and SciPy
  • Pandas 
  • Pandas 
  • R

Leading data scientists who have practical industry expertise are the lecturers for all of this.

How to Register

For the Post Graduate Program in Data Science, you must meet the following requirements:

A fundamental grasp of mathematics and programming principles

A bachelor's degree with a minimum grade of 50% (GPA of 2.0 or higher)

It would be ideal to have two or more years of job experience, but it is not necessary.

So come and join this data scientist certification.

What is Correlation - Data Science Terminologies


What is Cross Entropy - Data Science Terminologies



Comments

Post a Comment

Popular posts from this blog

Statistics for Data Science

In the dynamic field of data science, statistics serves as the cornerstone, providing the essential tools and techniques for analyzing, interpreting, and drawing meaningful insights from data. Whether you're a seasoned data professional or an aspiring data scientist, a solid understanding of statistics is crucial for navigating the complexities of data analysis and modeling. In this comprehensive guide, we delve into the fundamental concepts of statistics for data science, exploring its relevance, key principles, and practical applications. Understanding the Relevance of Statistics in Data Science Statistics plays a pivotal role in data science course by providing the necessary tools and methodologies for extracting insights from data. From descriptive statistics that summarize and visualize data distributions to inferential statistics that make predictions and draw conclusions from samples, statistical techniques form the backbone of data analysis and modeling in data science. Ke
Read more

Programming Languages for Data Scientists

Image
In the realm of data science, programming languages serve as the backbone for data manipulation, analysis, and visualization. Data scientists rely on a diverse array of programming languages to extract insights from complex datasets. In this guide, we'll explore the top programming languages favored by data scientists, shedding light on their features, applications, and relevance in the field. Additionally, we'll discuss the significance of pursuing a data science course , training, and certification to enhance proficiency in these languages. Python: The Swiss Army Knife of Data Science Python reigns supreme as the preferred programming language for data scientists course . Known for its simplicity, readability, and versatility, Python offers a rich ecosystem of libraries and frameworks tailored for data analysis and machine learning. Packages like NumPy, pandas, and scikit-learn empower data scientists to perform tasks ranging from data manipulation to predictive modeling with
Read more

Empowering Your Data Science Learning Journey

Image
In today's technology-driven world, staying relevant and continuously expanding one's skill set is crucial. However, with the ever-evolving landscape of technology, the question of which technology to learn can be overwhelming. In this article, we provide guidance on data science course selecting the right technology to learn, empowering you to make informed decisions that align with your goals and aspirations. 1. Define Your Objectives: Begin by clarifying your objectives and understanding why you want to learn a new technology. Are you seeking to enhance your career prospects, stay updated with industry trends, or pursue a personal passion? Defining your objectives will help narrow down the options and focus your learning efforts effectively. 2. Research Industry Trends: Research current industry trends to identify the technologies in high demand. Analyze job postings, professional forums, and technology reports to gain insights into emerging technologies, programming languag
Read more