DEV Community: Rajasekar Nonburaj

𝑾𝒉𝒂𝒕 𝒅𝒐𝒆𝒔 𝑫𝒂𝒕𝒂 𝑨𝒏𝒂𝒍𝒚𝒔𝒕 𝒅𝒐 — 𝑺𝒌𝒊𝒍𝒍 𝑨𝒏𝒂𝒍𝒚𝒔𝒊𝒔 𝒐𝒇 𝒂 𝑫𝒂𝒕𝒂 𝑨𝒏𝒂𝒍𝒚𝒔𝒕 𝑹𝒐𝒍𝒆

Rajasekar Nonburaj — Tue, 14 Nov 2023 16:17:23 +0000

I have analyzed over 100 job postings on LinkedIn and identified the essential skills required to become a data analyst (video tutorial coming soon).

💻 𝑫𝒂𝒕𝒂 𝑨𝒏𝒂𝒍𝒚𝒔𝒕 (𝑰𝒏𝒕𝒆𝒓𝒏 — 2 𝒀𝒓𝒔) | 𝑺𝒓.𝑫𝒂𝒕𝒂 𝑨𝒏𝒂𝒍𝒚𝒔𝒕 (3–6𝒚𝒓𝒔)

📌 𝑷𝒓𝒊𝒎𝒂𝒓𝒚 𝑺𝒌𝒊𝒍𝒍𝒔
1️⃣ SQL Server (Oracle, MS-SQL, Postgres, MySQL)
2️⃣ MS Excel (Vlookup, Pivot, Advanced Excel functions & Formulas)
3️⃣ Data Viz Tools — Tableau, PowerBI
4️⃣ Programming language — Python or R programming
5️⃣ Strong understanding of Statistical and ML Concepts

📌 𝑺𝒆𝒄𝒐𝒏𝒅𝒂𝒓𝒚 𝑺𝒌𝒊𝒍𝒍𝒔
1️⃣ DBT & Snowflake
2️⃣ Databricks & Pyspark
3️⃣ Google Analytics, BigQuery & Looker — Mainly for Digital Marketing project
4️⃣ Basic understanding of A/B Testing
5️⃣ Understanding of Scrum & Agile Process
6️⃣ Basic and advanced level of understanding of any cloud platforms depending on the roles. (Azure, AWS & GCP Knowledge)

📌 𝑨𝒏𝒂𝒍𝒚𝒔𝒊𝒔
✍️ After reviewing numerous job postings, it is evident that for entry-level positions such as internships or data analyst roles, a basic understanding of Python programming language is frequently required.

This is especially relevant when conducting predictive analysis using customer data, as many modern BI tools offer functionality that can be effectively utilized with Python.

As a sr. analyst, it is often necessary to have an understanding of machine learning (ML) concepts and the ability to apply regression, classification algorithms, and other techniques. Additionally, some companies may expect candidates to possess advanced knowledge of cloud platforms such as Azure, AWS, or GCP.

For a sr. data analyst role, experience with tools like Snowflake and data transformation tools such as DBT may be required, as these positions often involve handling large data volumes and complex processing tasks.

I have observed that some data analysts transition to data scientists or machine learning (ML) engineers, depending on the role and the company they work for. This demonstrates the versatility of the skills acquired as a data analyst and the potential for career growth in various directions within the data field.

👉 If you like the post, please follow @rjinsights and share / like / comment on the post for wider reach.

dataanalytics #dataanalyst #dataanalystjobs #techcommunity #careerdevelopment #techcareers #chennaitech #helpinghands #rjinsights

Free LLM Course — Large Language Model Course

Rajasekar Nonburaj — Fri, 14 Jul 2023 04:30:54 +0000

Stepping back into the world of blogging after a long hiatus is an experience filled with a whirlwind of emotions. As I sit here, fingers poised over the keyboard, I can’t help but feel a mix of excitement, nervousness, and a tinge of self-doubt.

But amidst it all, there’s a flicker of something familiar — a deep-seated passion for sharing my thoughts and stories with the world. I am planning to write atleast one blog psot either weekly or fortnightly, lets start the week with LLMs course.

Large language models have emerged as powerful tools in natural language processing, transforming the way we interact with technology. Their ability to understand and generate human-like text opens up countless possibilities across industries, from customer support to content generation and data analysis.

As the field continues to evolve, it is crucial to strike a balance between harnessing the capabilities of large language models and addressing the ethical considerations to build a more inclusive and responsible AI-powered future.

Below are some of the sources where you can learn more about Large Language Models, these are some of the free courses offered by various companies, in the coming blogs I will write about universities and vendors on fee-based courses.

Free Large Language Model Course

Deeplearning.ai

cohere

Databricks

Google Cloud

Weights & Biases

llms #largelanguagemodels #nlp #machinelearning #ml

Role of a DevOps Consultant

Rajasekar Nonburaj — Tue, 25 Aug 2020 16:12:07 +0000

Recently, I have started to note there is a lot of opportunities piling up for DevOps consultant, so I thought of putting up this post just to understand what else is required for someone to set a career path in that area.
Before jumping into the topic, let us first understand a few basic things

What is DevOps?
DevOps is not a framework or a workflow. It’s a culture that is overtaking the business world. DevOps ensures collaboration and communication between software engineers (Dev) and IT operations (Ops). With DevOps, changes make it to production faster. Resources are easier to share. And large-scale systems are easier to manage and maintain.

In 2009, Patrick Debois from Belgium and Andrew “Clay” Shafer from the US met and started talking up (and coined the term) DevOps, and then Patrick held the first DevOpsDays event in Ghent that lit the fuse. The concept, now that it had a name, started to be talked up more in other venues (I found out about it at OpsCamp Austin) including Velocity and DevOpsDays here in the US and spread quickly (Source: https://theagileadmin.com/what-is-devops)

Wonderful explanation is given in this video, DevOps Foundations (James, Ernest) — a three hour set of videos designed to orient beginners into the whole scope of DevOps.

There is a lot of resources available for someone to start in DevOps, I find this website be useful https://devops.com,if you go through the website you will get a lot of ideas on DevOps.

Below is the mindmap (Draft) I have put together on the set of tools used in DevOps world, basically this is based on the analysis done on various Job Description posted on LinkedIn and other websites for the role of DevOps Consultant (https://whimsical.com/ThsH9ZpZJ1YSKtrUAoZ2hW)

Career Roadmap - How to Transition to Datascience

Rajasekar Nonburaj — Wed, 08 Jul 2020 08:56:05 +0000

This is a series of articles on career transitions, the first article is about the career transition to Datascience. I am going to list down how you can learn and do a career transition to either one of the tech (Datascience, Cloud, DevOps and Full-stack)

There are a lot of course on the internet where they teach Datascience, if you do a search on google you can find out and below is the result and out of which some of them are paid and free courses, it's up to to the individual which one they want to choose, even nowadays we can see some of the universities are offering courses on Datascience.

I have been in the IT industry for 16 plus years and most of my experience is in Business Intelligence, Datawarehousing, etc.. this was once a booming tech where we work on data analysis and creating some meaningful insights for clients.

As of now, you can see most of the people who once worked as a BI Developer, ETL Developer, Database Developer (Junior to senior level) had themself transition to various other Datascience roles and for some still, they find it difficult to take up the transition and this article is for those people, the below screenshot shows what you can choose if you are in the crossroads and don't know what to decide.

This article is about my personal experience in interaction with various people in the IT industry. The tech specified below is one of the top emerging job roles in 2020 according to LinkedIn, we can't be a master of all, so I choose some which will be useful for someone to do the transition quickly, the first article about how to start transitions to Datascience.

Datascience Paid Course:-
The courses specified here are paid courses, these are my personal recommendation and udemy has many more courses which are also good, you can search for a specific topic and take it up if this is not good for you. Please read the reviews and ratings before taking up any courses.

Course:-1
Before you start getting into Datascience, you need to have a basic understanding of Statistics, the below udemy course “Statistics for Business Analytics” is by “Kirill Eremenko”, he is one of the top-rated instructors in udemy for Datascience, ML and Deep Learning courses, he has more than 10 Million students registered for his courses. This course has a rating of 4.4 (5400) and so far 32,089 students has registered for the course.

https://www.udemy.com/course/data-statistics

Once you have done the statistics course, you need to learn any one of the statistical / programming languages (R/Python) in order to develop ML Models. The debate of whether to learn python or R is a never-ending one, you need to start first instead of debating over the language. R programming is considerably earlier one for someone like me who is not from the programming side, so I would suggest starting with R Programming.

Course:-2
The below udemy course “R Programming A-Z™: R For Data Science With Real Exercises!” is also by “Kirill Eremenko”, this course has a rating of 4.6 (26644) and so far 119,956 students have registered for the course.

https://www.udemy.com/course/r-programming/

Course:-3
After you completed R programming, it’s time for getting our hands into Datascience and Machine learning, I have found this course “Data Science and Machine Learning Bootcamp with R” by “Jose Portilla” useful, he is also the “Head of Datascience” for “Pierian Data Inc”, and also one of the top-rated udemy instructors on Datascience, ML Courses. This course has a rating of 4.6 (9402) and so far 50,413 students have registered for the course.

https://www.udemy.com/course/data-science-and-machine-learning-bootcamp-with-r

Course:-4
If you programming background and like to build ML Models using python, then this course is for you. This is a newly launched course from Andrei Neagoie and Daniel Bourke, the course “Complete Machine Learning and Data Science: Zero to Mastery” is launched on Feb 2020 and has a rating of 4.6 (859) and so far 8,826 students have registered for the course. This is one of the top rated courses now in Datascience in Udemy.

Andrei is a self-taught programmer and also the instructor of the highest-rated Development course in Udemy.

Daniel is also a self-taught Machine Learning Engineer, he has previously worked as a Machine learning engineer at maxkelsen.

https://www.udemy.com/course/complete-machine-learning-and-data-science-zero-to-mastery/

How to prepare for Microsoft Azure Fundamentals Certification (AZ-900)

Rajasekar Nonburaj — Mon, 29 Jun 2020 07:39:17 +0000

This post is about my experience in preparation for the AZ-900 Azure Fundamentals certification.

The new role-based Azure certification details are given below
AZ-900: Microsoft Azure Fundamentals exam is designed for candidates looking to demonstrate foundational-level knowledge of cloud services and how those services are provided with Microsoft Azure.

The exam is intended for candidates with non-technical backgrounds, such as those involved in selling or purchasing cloud-based solutions and services, or who have some involvement with cloud-based solutions and services, as well as those with a technical background who have a need to validate their foundational-level knowledge around cloud services.
Technical IT experience is not required, however, some general IT knowledge or experience would be beneficial.

This exam can be taken as an optional first step in learning about cloud services and how those concepts are exemplified by Microsoft Azure. It can be taken as a precursor to Microsoft Azure or Microsoft cloud services exams, while it would be a beneficial first step, validating foundational-level knowledge, taking this exam is not a prerequisite before taking any other Azure-based certifications.

Preparation:-
I have used the following materials to prepare for the certification, the Microsoft website has wonderful notes if you want to prepare for the exam https://docs.microsoft.com/en-us/learn/certifications/exams/az-900. Below are the various topics on which the questions will be asked

Skills measured:-
Understand cloud concepts (15–20%)
Understand core Azure services (30–35%)
Understand security, privacy, compliance, and trust (25–30%)
Understand Azure pricing and support (20–25%)

I have used the subscription I had from acloudguru for certification preparation, but you also have some training content available in Udemy as well if you want to prepare for the certification, it's up to the individual whether you want to opt for a paid training content or free course.

Practice Test:-
The practice test is very important, even though during the course of training you get some Q&A at each topic, but you can take up some practice test before appearing for the certification, I would recommend the practice test from whizlabs.

I have a kind of fear towards online exams right from my college even though I took a couple before still for me it kept shivering before I see the marks. Thankfully whizlabs help to prepare me to equipped with necessary knowledge towards the certification.

D-Day:-
You can register for the test through Microsoft portal, you can take the certification from Pearson Vue testing center, below is the certification I got after I finished the exam.