Ronald Wayne, Steve Jobs, and Steve Wozniak co-founded Apple on April 1, 1976.

Comprehend Data Science to your Current role

Image depecting Data Engineering Architecture

If you are contemplating a transition to data engineering or a related field, it is imperative to possess a robust comprehension of data engineering, platforms, models, roles, pertinent tools, and technologies. Furthermore, acquiring knowledge of machine learning and deep learning is pivotal.

Our all-inclusive training program encompasses practical experience with real-time projects, in addition to the aforementioned proficiencies.

FAQs from working professionals

Can anyone make career career shift to IT f?

"IT is not core engineering field, anyone can opt for it. if they can be properly guided."

What about current job experience and role?

"It is important to thoroughly evaluate your current job experience and role, and determine if your skills align with the requirements of an IT job. Consider acquiring relevant skills that complement your profile. It is recommended to take a holistic approach to learning Data Engineering, focusing on both technical skills. ."

Is the data science stream easier than other IT fields?

"Programming might seem straightforward, but building applications in data science requires a diverse set of skills. Companies seek full-stack professionals."

Can I do self-learning?

"I don't recommend becoming an IT professional lightly, as the job involves both risks and rewards. Having an honest mentor is crucial, as they can impart valuable lessons from their own experiences of both failure and success. However, finding a mentor who is both capable and honest can be quite rare."

Data Engineering

"Professionals must grasp Data Engineering Architecture and its responsibilities to showcase.

With 20 years of experience, I am committed teaching these skills and enabling individuals to become full-stack resources."

Software Foundation

Programming and data handling skills are foundational for any software job, including data science.

The specific skills to focus on in depth and those to have as fundamental knowledge depend on various factors.

Data Science

Broad spectrum of Data Science (ML & DL) for Business Intelligence

You must master an in-depth understanding of descriptive, inferential, and prescriptive statistics. It's essential to learn various methodologies, models, and techniques to present projects in multiple domains.

These sessions are essential for a successful career transition, focusing on IT project management principles, methodologies, project phases, roles, documentation, and adapting to IT.

We will also share insights from my years in the industry.