---
title: 'Data Analyst Roadmap 2026: How I''d Learn Data Analytics'
source: 'https://youtube.com/watch?v=d8U2G2_7dnM'
video_id: 'd8U2G2_7dnM'
date: 2026-07-31
duration_sec: 3935
---

# Data Analyst Roadmap 2026: How I'd Learn Data Analytics

> Source: [Data Analyst Roadmap 2026: How I'd Learn Data Analytics](https://youtube.com/watch?v=d8U2G2_7dnM)

## Summary

This video presents a practical, data-backed roadmap for becoming a data analyst in 2026. The creators analyzed 1,000+ real job postings, consulted hiring managers and industry experts, and combined it with 10+ years of experience to deliver a 16-week study plan using mostly free resources.

### Key Points

- **Job market analysis** [00:46] — Analyzed 1,000+ latest data analyst job postings from prominent job portals to identify the most in-demand skills, combined with 10+ years of industry experience.
- **Roadmap commitment** [01:42] — The roadmap requires 4 hours of study per day for the next 4 to 6 months. The creators explicitly state there are no shortcuts and warn viewers to leave if they expect one.
- **Salary ranges** [02:11] — In India, most data analyst salaries fall between 3 to 15 lakh, with a few exceeding 1 crore (mostly director/head roles). US salary ranges also shown. Use levels.fyi, Glassdoor, Naukri for current data.
- **Job availability** [02:54] — Searching 'data analyst' on Naukri shows ~30,000 jobs, but other titles like Power BI developer show ~46,000 jobs. The roadmap applies to all related titles.
- **Two skill categories** [04:42] — Tech skills (Excel, Power BI, Tableau, SQL) and core skills (communication, problem-solving, collaboration). Both are essential for data analysts.
- **Four data analyst categories** [05:51] — BI reporting analyst (MIS), tool-specific analyst (Power BI/Tableau developer), domain/function-specific analyst, and full-stack analyst. The first two are fresher-friendly; the latter two require more depth.
- **Expert insights on soft skills** [14:35] — Interviews with hiring managers, VPs, and directors highlight that communication, problem-solving, curiosity, and the right mindset often outweigh technical skills.
- **AI changes the analyst role** [16:57] — Technical skills alone won't get you a job. There is no future for analysts who only pull information and create reports—AI can do that now. Analysts must add business value, collaborate, and think critically.
- **Analysts becoming data engineers** [18:45] — Real project example: data analysts now build ETL pipelines using Fabric notebooks, use APIs, and apply automations—showing the merging of analytics and engineering roles.
- **16-week roadmap PDF** [26:38] — The video provides a week-by-week study plan with free resources, a complete checklist, and practical tips for all 16 weeks.
- **Excel fundamentals** [33:13] — Learn basic formulas (SUM, AVERAGE, PRODUCT) and advanced ones (VLOOKUP, MATCH, INDEX, pivot tables) using free resources like CodeBasics videos and Khan Academy.
- **Choose Power BI if undecided** [39:41] — Power BI is the leader in Gartner Magic Quadrant for 2025, so it's the safest choice for beginners. Transfer learning to Tableau or Qlik is easy afterwards.
- **SQL essentials** [48:34] — Learn relational databases, basic queries, joins, subqueries, and window functions. Stored procedures are good-to-know, not must-learn at this stage.
- **Python and AI generalist** [55:00] — Learn Python basics (variables, lists, dicts, loops), then AI fundamentals like deep learning and AI agents. Use tools like ChatGPT and Claude, and explore projects like Quadratic for supply chain.
- **Final weeks: Databricks and Fabric** [01:01:01] — Learn emerging tools like Databricks and Microsoft Fabric to stand out. Optionally get PL-300 certification to be in the top 5-2% of candidates.
- **Practice, don't just consume** [01:03:16] — Consuming content is easy but digesting requires practice. Convert information into skill by doing projects—avoid 'information fat'.

### Conclusion

The video delivers a realistic, research-backed roadmap for becoming a data analyst in 2026. Success requires discipline, a mix of technical and soft skills, continuous project practice, and an understanding that AI is shifting the role toward higher-value business contributions.

## Transcript

gateway to enter data and AI field. However, the role is constantly emerging, trends are changing, and you may have this confusion. Is it a good role to pursue in your 2026? And if it is, what are the skills that we need?
Now, if you go to YouTube, you will find so many videos. Some of these videos are created by people who themselves don't have any real industry experience, and they are unnecessarily hyping things up. When we started making this video, we
wanted it to be based on absolute reality. So, we did interesting experiment. We analyzed 1,000 plus latest data analyst jobs from prominent latest data analyst jobs from prominent job portals and created a list of the
skills which are in top demand. Then, we combined that with our own industry experience. Both myself and Hamon and Vadivelu, who is the co-creator of this video, have 10 plus years of data industry experience. Hamon and
industry experience. Hamon and especially was working as a data analyst in Europe for few years. Then, he become a data analytics lead, where he was managing a team of data analyst, and he worked on more than 30 enterprise
projects. And then, we also consulted several data analysts who are working in the industry. We talked to data analysts, their managers, VPs, and directors and took their opinion. On top of that, we combined our own experience
of working on AI and data projects in my data and AI consulting company, Atli Technologies. As a result, we have prepared this practical roadmap with week-by-week study plan using free learning resources, checklist, and
anything unrealistic to increase the views of this video. This will require 4 hours of study for next 4 to 6 months. And as you can see, there is a lot of hard work that you want to put in. And if you're looking for any shortcut,
please leave this video right now. On the screen here, I'm showing you some salary range for data analyst role. So, I searched for data analyst, and here you can see the salary range. We also created this histogram so that you get
an understanding. As you can see that most of the salaries are in range 3 to 15 lakh with some salaries going in the range of 1 crore as well. There are, I think, 14 jobs which had more than 1 crore salary, and these are the
directors and, you know, head of data, etc. In the US, this is the range. Folks, you can go to these websites levels.fyi, levels.fyi, Glassdoor, naukri.com, etc. at any given
point to get an idea on the salary range. And if you want to know how many jobs are available, it is simple. You go to any job portal. Let's say I go to Naukri and search for this data analyst job. I found total close to 30,000 jobs.
job. I found total close to 30,000 jobs. Now, see, they post these jobs using different titles. So, data analyst is one title, but there are different titles as well, such as Power BI developer, for example. For that, we
found some 46,000 jobs. There can be operational analyst, finance analyst, and so on. So, this roadmap is not valid just for general data analyst. It is also valid for function and domain specific and tool specific roles as
well. And we are going to look into the categories, different types of data analyst little later. Before proceeding further, if you have doubt if data analyst is a good career to pursue in year 2026 or not, then please watch this
video. We are going to provide you a link, and after watching that video, you can resume this roadmap video. In that video, we analyzed reports, we spoke with experts, and we provided a detailed recommendation on how to proceed. And
one important thing you need to figure out is if this career is suiting your natural skills and interest or not. For that, we have created a free survey questions, and it will tell you if this career role is good for you or not. Now,
folks, here is a tool we created to analyze the hot skills in the job market. It's a free code on GitHub. You can download this code and run it can download this code and run it anytime. And this Python notebook
shows you the hot skills for data analyst role. So, here you can see the skills such as SQL, communication. See, communication is a very common skill, Python, and so on. And then, there are different
role titles, right? Like marketing analyst, for example. For them, these are the skills. Finance analyst, for them, these are the skills. So, we are going to provide you a link of this code repository, so you can take a look. And
in the future, if you want to run this report, you can do it on your own. So, here are the tech skills and core skills. As a data analyst, you need to build both of these skills, and we are going to provide you a week-by-week
study plan using free learning resources, where you can study both the category of the skills. Uh in tech skills, you find some of the obvious things such as Excel, Power BI, Tableau, SQL, etc. But we want to highlight this
point about data engineering basics. Since data analyst and data engineering roles are kind of merging, it would be good to have the basic understanding of data engineering fundamentals, some of the cloud platforms such as Azure, AWS,
Databricks, etc. if you want to become a full-stack data analyst. And then, there are good to have skills as well, right? So, if you know of Microsoft Fabric, Databricks, Alteryx, etc. on top of the required skills, then that can give you
unfair advantage. All right, now let me hand it over to Hamon and to talk about the data analyst job categories. So, we looked into the hundreds of job descriptions available there in the job portal right now and divided the data
analyst role into four major categories. So, these categories are BI reporting tool specific analyst, domain function specific analyst, and a full-stack analyst. Now, I will explain each of these
categories with an example. Let's go into the first one. So, here you would see the job roles would directly say that you are into reporting. It will reporting or it will say something like MIS executive. So, when you see this
word MIS, just understand it's about reporting. MIS means management information systems, where you would be collecting a bunch of information and convert them to reports. So, when I say reports, these are most likely to be
standard reports. You're not going to create a new form of report. There will and you will be creating them. And you can also typically see in these job roles, they would mention multiple BI tools, like Looker Studio, Power BI.
All these are BI tools. Why do they mention multiple tools? Now, you need to understand this. Because most likely, these roles are a central IT team, right? A central IT team means there is a
typical team which is sitting as a function and IT team would support the marketing team, sales team, finance team, or supply chain team, those kind of things. Or it could be a service company which
is supporting multiple clients. That's why they would want you to understand uh multiple tools, use multiple tools. So, here the thing is, you won't create something significantly new, right? So, that is why this particular role, this
BI reporting analyst role is more fresher friendly. So, if you are a fresher with minimum experience, if you have a portfolio or something like that, most likely you will get a job as a BI reporting analyst
or an MIS executive. Now, let's move on to the second category, which is the As you could have already understood, tool specific analyst means it points to Power BI developer, Tableau developer, or a Looker Studio developer.
So, it will clearly say that you are going to work on this tool. All your work will be based on this tool. Here again, there will be standard reports, but you'll be working only on one particular set of tool, and the the
depth of work will be much higher here. Within Power BI, you would be going into In the previous role, in the BI reporting analyst role, you would be using multiple tools, but you will be
creating most likely some basic to mid-level reports, not advanced reports. But in this role, you might be creating advanced reports in Power BI, Tableau, the use cases will be predefined. You won't be creating new reports here. And
this role is also fresher friendly. If we talk about freshers, most likely as a fresher, you would get a job in these two As a junior Power BI developer, a junior Tableau developer. So, when you're
looking for jobs in Naukri or any job portals, look for these two particular saying you know, other people who are experienced won't get a job here, but this area is more friendly for freshers.
And so, you can see an example here, They would ask you to create Power BI dashboards, semantic data models, SQL views, stored procedures, ETL processes. This is something a fresher can do
pretty much easily. And whenever you see the experience mentioned as 1 to 5 years, when they say experience 1 year, as a fresher, you can apply, but always apply with a portfolio. Show the proof of your work, right? Which is something
we will discuss in the later part of this roadmap. And then comes the third category, which is domain function specific analyst, right? It could be a retail analyst or marketing analyst. So, if you don't understand clearly what
function, please check this post. I've made a post on this, and I'm going to give a link to this post as well, right? So, you need to know what are the domains, right? Domains are nothing but like domains like consumer goods,
healthcare, automotive, gaming, energy, banking, finance, securities and investments. These are industries, right? What are the functions? Functions are like sales function, finance, marketing, supply chain, and uh human
resources, customer experience. These are functions. So, these functions will exist within all the domains. So, you need to understand the domains and these functions and each of these domains would require an analyst to help
them in developing insights so that they can make a business decision, okay? So, the first step for you is to understand the domains and functions very clearly. you can go and watch it later. So, you can see an example here.
It clearly says they want a retail analyst, which means they expect you to have some domain knowledge in the retail industry and functional knowledge in the marketing. So, they're looking for a marketing analyst in the retail domain.
So, if if you understand the basic KPIs of marketing like customer acquisition cost and customer lifetime value and you also understand the retail retail business or FMCG business, if you understand how this business is run,
here. So, don't think that uh you are a be able to have that kind of a knowledge. There are ways to build fresher, which will be discussed in the road map later.
which is the full stack analyst. This is the category which is emerging these days. It has been there, you know, since last 5 years, but off late, in the last 1 year, I would say, it's emerging. It's becoming more evident that
data analytics and data engineering, they are both combining together. So, as a data analyst, if you have understanding about cloud technologies, Azure, [clears throat] if you understand how things work before it comes as a
data in a table, right? So, mostly as a data analyst, you will connect the Power BI dashboard or Tableau to a database table, but let's say that you understand then you are going to have a massive advantage and these are the people
looking for people who have familiarity with the cloud platforms. Like you can see here Azure, GCP, Snowflake. So, with these kind of things, what you will become is an end-to-end analytics engineer. Of course, you won't become a
beginning. You might understand few things. You will still need a support of a data engineer, but if you're someone who can understand these things at least at a basic level, you will be able to work better with data engineers. And
these kind of people are in demand. So, if you're a fresher or if you're an career transition, along with data analytics, having basic idea of cloud technologies and working with things
like Databricks Fabric will put you in advantage. So, just to summarize, these two categories are more fresher friendly and uh these two categories are less fresher friendly. That doesn't mean like
uh you know, you can't apply as a fresher to this category. All these categories have depth, you know, you can become a senior reporting analyst, you can become a senior Power BI developer, or you can
become the head of the analytics department where the department is working only with Power BI or only with Tableau. I've seen all these kind of roles here. And domain function specific analyst, this is something a product
company would prefer very much and if you are someone who has a lot of chain, try to become a domain specific or function specific analyst. That will give you a lot of advantage because
with AI taking over a lot of you know, manual task, having that domain knowledge, having that functional knowledge gives human beings a lot of advantage. So, if you think that you have the domain knowledge
or functional knowledge and try to become an analyst, try to learn these tools, which is Power BI, Excel, SQL, Python, try to learn these tools and become a domain or function specific analyst, that's going to help you a lot.
And for full stack analyst, I would also recommend freshers to try this. Freshers now that you understand all these tools from the data analytics side, if you try to understand the data engineering side of things as
well, it will increase the chances of you landing a job. guarantee you a job, but this is definitely going to increase your chances of you landing the job. So, what we did is uh instead of we just saying
who are actually working as data analysts, who are actually hiring data analysts, the head of departments, the VPs and directors. We spoke to them. We asked them what competencies they look for analysts in 2026 so that
they can hire them in their team. And we spoke to many people. I'm going to play a few clips to you so that you can directly hear from them. It's not the technical skills that are going to matter more. It's your problem-solving
ability and the communication skills. I like people that are curious, that are Um [snorts] that are constantly uh trying to not be comfortable, trying
where you actually grow and develop yourself with an open mindset. So, I >> [snorts] >> people that are probably maybe fresher on the market, but they have the right mindset uh rather than bring someone
with a lot of experience, but uh that doesn't have the right um attitude. Many numbers, but struggle to communicate the insights effectively. Data analysis, in my mind, is not just about numbers. It's about telling a story that can uh drive
business decisions. Domain knowledge is almost as equally as important as uh technical knowledge in many capacities in the world of analytics. Without domain knowledge, there's so much, you know, I'd say waste or inefficiency uh
because you often spend a lot of time trying to absorb knowledge from business people who don't really have the time to teach the analyst about their job or what they're trying to accomplish. So, I I do think that business uh and domain
expertise is critically important in many cases um as it enables you to be faster, better, and be able to speak and tell stories with the data and that what you're actually looking at. And if the domain knowledge is lagging, the next
best thing that I kind of tell people is that natural curiosity is the next best best trait for an individual in analytics. It's something I look for and something that usually can't be taught, but if you don't have the business
context or the domain expertise, I usually go for someone who is uh naturally curious because the curiosity aspect is what that allows them to dive deeper into that domain uh and be able to speak more intelligently about it.
So, after listening to all these people, one thing that is absolutely clear is technical skills alone won't fetch you a job. If I have to be definitive, I can say that there is no future for those kind of data analysts who simply used to
grab a bunch of information and create reports. That part is gone because those things AI can do now. Pretty much AI can do now. So, as a data analyst, the business people are expecting you to add more business value in terms of
strategic collaboration, in terms of critical thinking, you know, you you business so that they gain some competitive advantage or as a data analyst, you talk to stakeholders and make their
understanding easier. You know, a lot of people make this mistake uh you know, they talk very technical to the non-technical people. But as a data analyst, if you know how to switch gears, that you switch the
technical gear with technical people and a non-technical gear with non-technical people, that is a massive advantage because there the collaborative spirit of people in the team. And for all these things, you need to stay very curious
to continuously learn things every day and add value to the business. What these people said, I can see that it's also reflecting in the job postings these days, right? You can see the words
in finance, which is which is very important and you can see these words like cross-functional collaboration and you can see about like engage stakeholders and meet expectations by value and actionable insights. All these
Collaborate with cross-functional teams. So, these things are very important if you're planning to become a data analyst in 2026, not just the technical skills. Again, technical skills are important, but that alone is not sufficient. I can
explain this to you with a real experience of a project we did in earlier, what used to happen is like when we started 3 years back, our website did not collect a lot of data. So, what we used to do is collect the
data source directly to Power BI and we used to build the visuals. So, we had data analysts in our team who did this, right? So, they were pretty much like connecting the source and building the visuals.
But now, what they're doing? Now, the data has grown big, right? So, the same data analysts in my team, they kind of understood how to connect to the raw data source, how to connect the raw data source and how to connect with
multiple other sources using APIs. And they created this ETL pipeline using Fabric notebook. So, this is where the data analysts in my team started becoming like data engineers, right? So, they started building this
data engineering skill as well. And then they pushed it to data warehouse. Again, it wasn't easy for them. They have to learn few things in order to do that and We allowed them to make mistakes and learn from it. So, they were able to
work in an environment like a sandbox environment. They were allowed to make production. And then what happened is while making this push to Power BI desktop. By this time our team has grown
big. We have finance team, marketing team, we have learner experience team. All this team is now using this dashboard. So, for the data analyst in with these people. So, this cross collaboration, project management, and
you know, stakeholder management all the skills were exhibited by the all the skills were exhibited by the data analyst in my team to build this dashboard, right? And they even applied business logic. They even went to the
extent where they can save cost and enhance the user experience of people is like when the whenever the table is huge, they applied incremental refresh so that the business or the dashboard which the end users are using it's
getting loaded very fast. Otherwise, when they click some button, you know, implementing all these things they were able to make a much faster working dashboard. And I can clearly say the future is moving towards the direction
learning more things. So, my the data analysts in my team, they are able to make automations using Power Automate, n8n. And now they are learning also data engineering. And don't think that they are spending
more time at work. No, that's not the case because lot of things they were doing earlier like fetching the data manually, like fetching the data via email. They are now using APIs. They are now using automations. They are doing
that faster. Whatever time they are saving with the help of AI, they are reinvesting back to learn new things and do more for the company. So, this is how they are expanding as a data analyst. So, if you are a fresher
or an experienced data analyst watching this video, just know this that don't think that okay, why should I do more for the company? Why should I do more stuff? It's not about that. It's about like how you are
like the best product for the market, right? It's a product market fit, right? So, treat yourself like a product. The product has so many features. Now, the market cannot resist you. They have to have you in the team. So, you have to
build yourself like that. With that mindset, whatever job role you are trying in in the data and AI field, you'll definitely find success. So, in short I would say you can be jack of all trades and master of one. But if you
multiple trades is better. But in jack of all trades and master of one. And you know, all these tools are lying Power BI, Python, Databricks. There can be many tools, you know. As a problem
solver, you should not be worried about what tool you should use. For example, I learned Power BI when I have to solve a problem. I did not know Power BI is going to boom later. I I started learning Power BI in 2017. I was
And I just picked Power BI as a part of my solution and it became big later. So, right now Databricks or Fabric could be such tool, right? Just learn tools as a part of solving a problem and gradually
arena. Which will always make you relevant in need to aim to become a problem solver. This is the number one thing, right? number one thing. And build strong technical foundations.
It's important, right? It's It's It's a foundation. business acumen, which means on a daily basis you should learn how business Just think there are only three KPIs, revenue, cost, and profit are goals.
Within these three KPIs, what are the sub KPIs you can provide to improve the business? To give a competitive edge to the business. That is all about data analytics. So, if you understand that, your business acumen will grow on a
daily basis. And the most important of all, you should have that ability to communicate clearly to the stakeholders. When I say communicate clearly, do not misunderstand that with your English speaking skills or with your speaking
very fluent or it doesn't matter if you don't use a lot of vocabulary. That does not matter. What really matters is how clearly you are able to communicate, right? I've seen a lot of genius people in the tech industry struggling to
communicate with top-level stakeholders, right? The sit in some of the board meetings, they get confused and they just think, oh, I want to implement it. That's where they need a person who can
value. They won't say like okay, implement Azure, implement Fabric, this is that. They won't talk about the the endpoint security. They won't talk about like okay, fine. If you implement this within 6 months, our revenue can grow
this, and that. This is how you switch gears and communicate to stakeholders. And this is a very important skill. So, if you look data analyst skill matrix for different kind of data roles. As you
can see for some of the roles, you know, you you need a particular skill, which is SQL is kind of needed for all the roles. It's It's very high. But you know, for some of the
important, right? Maybe for a domain-specific analyst they don't work much with Python, right? It's It's kind of low. This is purely based on the job descriptions available out there on the internet. Sometimes
things together. So, ignore that. I'm just taking the general average and given this matrix to you. And like let's let's say basic statistics and understand for all the tools to a medium level.
And you can see that business metrics and domain knowledge is very high if you are a domain-specific analyst. For the roles, it's It's medium. And data storytelling is very important for almost all the roles. And data
engineering basics this is a skill that, you know, as a You need to have the skills at a very top level. And in terms of stakeholder management, as a domain-specific analyst this is the
number one skill you would have because you would constantly work with people who talk business. They don't They don't talk data. They talk business. You convert the Python code or SQL whatever you write to KPIs. So, that's the
speak. You convert that to KPIs. You talk to them using that language. So, that's why this skill is very important there. And insatiable curiosity is like common for all. I would say this is a very high skill you would have for all
the roles irrespective of whichever part of data analyst role you are taking. Same goes with communication. And being AI generalist, this is very important now. But when I say AI generalist, it means how you can
use tools like Claude, how you can use tools like ChatGPT projects. And there are a lot of things within this tool, right? Like just don't think that you answer. You know, you can go deep into these tools. And this is a skill that
you should have as a data analyst in in general. And critical thinking like I always say it's very important for all kinds of data analyst. So, friends, this is the data analyst roadmap PDF for 2026. In this PDF you will get the
week-by-week plan with all the free resources attached, right? For all the 16 weeks. And you will also get a proper checklist, a complete checklist for a data analyst role. And you would also
get all the practical tips and guidance. So, let's begin with the first week. But before that, let's see what is week zero. I want you to be very clear about this. You are getting into this preparation and the foundation of your
preparation and the foundation of your preparation should be based on research. It should not be simply based on what CodeBasics says or what I say, what your friend says. No. It should be based on your own research.
Trust me, whatever effort you are going to make in the preparation if it has to get the actual outcome that it deserves, it must be based on your own research. That's why I want to build this research mindset with you. So, we
the research. These are some of the research questions checklist that you market, are people getting job roles, blah blah understand first. And of course you need to refer some standard reports, right?
The World Economic Forum report which I'm going to show you now. So, this is go through. You can study especially the page 19. It gives you some idea about scientist roles can grow. You can understand that. And
I've also written a LinkedIn post about this which is about what is the reality of data analyst, right? Let me let me share that with you. So, here I clearly explain like who can land a job or who cannot land a job, right? So, if you are
a course, and taking somebody's advice, and preparing, you are never going to land a job, right? Do not waste your efforts at least. Please know what is required to land a job. This is a very competitive field.
You need to understand that and you have to do that extra thing to land a job. these research things, right? This is again only to help you. So, there is a video we made which is about is data analyst a good career in 2026.
can watch this video and you can check the comments of people, right? How they are finding it useful. You know, what people are telling about this video. You can watch this because this video is not based on what we think or
spoken to the industry people, people to them. We have gathered those insights. And on top of that, I've been Davel has been in the industry for more than 12 years. We have put all those
than 12 years. We have put all those insights together. And given this piece make your own decision. In this video we have not said something like oh, you must become a data analyst. It is going to give you a a big growth or something
don't become a data analyst. Everything will be automated by AI. We did not take any extremes. We just understood what is happening in the industry and clarified Okay? So, please watch this video if you have not before starting to prepare for
the data analyst role, because it will actually give you the outcome for the understanding all these things, if you are just learning stuff, you will be wasting time. I really don't want you to do that. So, let's begin with the week
one. And also know that you have to prepare your mind and body for this because it requires a certain focus and concentration. Do not do this, you know, or you cannot concentrate, do not do this because
spend like a week or two and after that you will feel like, "Oh, no, it's not reasons not to do that. Just prepare your mind first. Just accept that you're entering a very competitive field. This is not a field where like how people
saying like, "Oh, just you can learn to build this dashboard in 5 minutes and you can earn 17 LPA, 18 LPA." All those are BS. Do not do not trust them, you know, they just do that to get money. They just don't want to uh you know,
real information in the industry. They just using the marketing gimmicks, information new, you should also take this uh scam awareness course, right, that we have created in the past. Uh we created this 1 year back.
So, this is a scam awareness course where we have clearly listed all the different kind of scams happening in the data industry and you need to go through are someone who are watching this video for the first time without having any
idea about the scams happening in the data industry, please watch this video. data industry, please watch this video. It will open your eyes. Okay? And uh body, you need to use internet like internet.
AI and everything. You need to use that as your second brain. And you know, the job market is not very great across the world, not just in India. have the victim mindset saying that, "Oh, you know, we won't get this. Those
people are not helping me. The companies are like this. Companies are evil." Disengage from those kind of people because they always find an external reason to complain and not move forward in the life, right? Disengage from them
and find people who have the hero mindset. When I say hero mindset people, so you might have seen in the movies, right? The the hero will have lot of Uh amidst all those difficulties, the hero will emerge, right? That's when we
clap for them. So, find those kind of people. I totally understand, right? The world is not great. Uh it is not very fair. Uh it is always not uh very uh lot of effort. The universe is not giving you back sometimes. But that
about external factors and then and demotivation. Don't do that. Just understand that you are required to make something you can't really define because there are a lot of external
factors. For you, what is important is that you make the effort and leave the nature. One day you will find that all the efforts that you have made, once you have reached a certain spot, you will feel like, "Oh, I did learn a lot
through that hard phase." So, that is very important. So, find the people who have that hero mindset and engage with them only. And uh eating properly, sleeping properly is very important. If you want to uh read any spiritual book,
will align with your soul and your efforts will become very conducive to you can start with excellent business math and statistics. Uh all you have to understand is some basic formulas like sum, average, product, mean, uh all
understand. And uh you need to understand the advanced formulas like VLOOKUP, match, index, pivot tables, all those things, right? So, we have created this video. So, I'm just clicking this link and showing you. So, in this in
this video you can see we have used a simple food data simple movie data set and made you understand all those Excel basics. This is good enough for you to attached other resources for you to explore. If you're looking for something
very structured, right? If you don't have time to go into multiple resources and if you're looking for structure, then only you can go for this paid course from CodeBasics. It is it is up to you. I mean, you can achieve the same
outcome with the free courses as well, but the paid courses will help you in a way it is more structured and uh you will find a lot of exercises, business uh scenarios and those kind of things. Okay? And uh in terms of business math
and statistics, very important thing you need to understand what is arithmetic maths, right? You need to understand what are percentages. Uh for example, right? When Apple is uh selling a phone, the revenue is growing from 50 to 60
growth? So, you should be immediately companies talk in percentages. Nobody will say like, "Oh, we got like 10 talk like that. Everybody will say, "Okay, how much percentage we have
grown?" So, you really have to convert all your conversations in percentages. If you learn how to do that, this will naturally help you to succeed in the data analytics field. Okay? And uh again, we have attached all
the free learning resources here, you know, there is one from Khan Academy. Uh it will teach you the basic maths and statistics. Everything is attached here. You can you can find all the links here. And uh then let's come to something very
important. This roadmap is structured in a way where you are building your skills, but alongside, you need to know how to show those skills. Because in today's world, if you're only building skills but not knowing how to
you will not succeed. You will not knock that gate of a job. You will not do that. So, you need to know how to show those skills and LinkedIn is one of the platform to do that. So, you can use this LinkedIn checklist to polish or
created one, this is a free checklist and you can just click this link and download it. So, you can see it contains everything, right? Like how you have to keep your profile visibility, how you have to fix your profile picture, your
uh banner image, your headlines. Every single thing is given here. All you have to do is just go through it, right? So, now you might wonder why LinkedIn is important. Why why to create a LinkedIn profile? Let me just help you with an
something, right? In the right side you can see the image where you're selling it in a in a place where it is not uh marketplace, right? So, only few people "Okay, uh the food is good, great." and all, but only few people can come. But
in a busy market where a lot of people come. Then what happens? Your business skyrockets, right? This is exactly what happens with LinkedIn. You have a skill and you are putting that in the marketplace for professionals. This is
the biggest marketplace on planet Earth for professionals. You're putting your skills there and you're constantly doing something so that professionals will notice your profile and then your profile gets highlighted among them. So,
that's the reason you need to have a very strong LinkedIn profile. And uh we have also included some section for your motivation where, you know, you can watch people who have achieved something extraordinary. I would take the story of
Khushboo Rani who was a BSC fresher and she transformed to a data analyst. So, I've shared a LinkedIn post on her on her story. I've just attached the link to the same. So, you you can see this, right? Like from where she has started
and uh she said how she did not have money to buy a laptop and then how she take these stories to get some inspiration. But the problem with this motivation is one thing. Uh don't rely on it, right? Just use motivation as
this extra pump to to gain some momentum, but always trust on discipline because that is what will stay with you. So, discipline is such a beautiful thing because it will make you do something even on the days when you're not
motivated. If [snorts] you get to the habit of discipline, then you have won everything, right? Like there is nothing that can stop you. There are days you're feeling well, you know, you will still do it.
That's unbelievable superpower. If you have got that, if you are building to to worry about anything, right? That's that's something you should focus on while working on this roadmap. Okay? So, then there are some assignments which
assignments, if you're not practicing, it's like watching swimming lessons. You're watching how to swim. right? You need to get into the water. For those things we are giving you this
assignment, so please make sure you are practicing them. You can also put it in LinkedIn and tag us and uh we are happy to engage uh on your post so that your profile gets more visibility, right? So, we are
happy to do that for you. And uh so, the one insight I want to give you is that let's say 100 people have watched this video and picked this roadmap. Uh only 60 people would complete this stage, right? So, if you are completing
this stage, just know that you are among the top 60. If you're completing this particular part, right? So, so all the best. And uh so, then comes the second leg which is week three, four, and five. So, here you can pick any of the BI
tools, right? It can be Power BI, Tableau, Qlik. Do not worry about Let's say you are already working and your company is using Qlik, then pick Qlik. If they are using Looker, pick Looker. If they're using Tableau, pick
Tableau. But you are a fresher, you don't know which tool you will be using later, then pick Power BI because Power BI is the one, you know, leading in the don't know what is a Gartner Magic Quadrant, let me tell you. Let me show
agency. They position the technology players within a specific market every year. And this is something what people look into, right? This is something what companies look
into. They somehow want to get into the top of the Gartner Magic Quadrant. They really respect and revere the Gartner Magic Quadrant. So, just follow Gartner Magic Quadrant, what they are saying. So, for 2025, they They said Power BI is
Quadrant, right? So, that's why I said if you don't have a necessity to pick one tool, like let's say you don't have a necessity to only pick Tableau, then pick Power BI because most of the companies are switching to
Power BI now. They are using Power BI. But, also don't worry if you pick Power BI and later you have to learn Tableau, it's not going to take a lot of effort because the fundamentals are the same. It's like you are learning to drive a
right? It's not going to be very different. are not going to take a lot of time. You can do transfer learning, okay? you have to research on the trends, what is unified analytics, what is Microsoft
Fabric, what is Databricks. First of all, what is unified analytics? Data together, that is a unified analytics, that's a trend. You need to understand what is it. And what are the tools which are built on base of this trends, which
Microsoft Fabric and Databricks. You need to understand them as well, okay? what is the edge for data analyst in the age of AI? All those things do a beginning this week. And we have also created a data analyst FAQ navigator,
which you can access from here. So, basically this is comprising of all the questions that we get and the corresponding answers. So, let me share it with you. So, if you are a fresher, you can simply click
fresher here. You can You can select the question and you will find an answer, right? You'll find an answer along with the key resources. This is done to make sure that you are not lost during the road map, right? During the road map,
course, you have to search the internet as well. This is just a consolidation of these questions and our answers, but you need to check outside as well. Like I said, doing a thorough research is very, very important, okay? I would also
this stage. There is There is an Indian Data Club. You can join this club because they do a lot of lot of activities. So, recently they do a lot activities as well. So, they started something called Founders Walk where
they are taking one founder from an industry and they are making them walk along with the aspirants and you can ask them questions during a walk. This is an amazing idea. And so, they recently they also conducted something called 21-day
challenge where people from 22 countries participated, more than 29,000 participants. That's an amazing stuff. And they made 37 lakhs of lines of code written by these people. So, why participating in this kind of challenges
and this kind of community is important? Because you find people who have the hero mindset, right? It's very easy for you to work along with people who have wants to challenge themselves, and move forward in the career. That is number
one. And number two is this is the stage for you to build networking, right? So, let's say you start building the network from now on, right? You would meet some fellow aspirants. It's very easy for you to ask for referral in the later stages
or you can refer some people also, right? This way what happens is that you get an extra edge where there are a lot of applicants for of the community, you already have some connection or some network with with
edge. That's the intention of creating this offline community, all right? And need to understand is like how to connect to different data sources, how Query, creating a DAX metrics, data modeling,
visuals, dashboarding. Using Power BI service is very important. So, these are the major things you have to understand and I've attached some free resources here. And I've also attached some YouTube channels to follow. You can
follow them. And like I said, there is a track B with an affordable fee. There is that, you can explore that as well, all right? And I've already answered this question whether you have to learn Power BI, Tableau, or any BI tool. So, like I
said, if you don't know which tool to pick, pick Power BI. You can transfer your learning to other tools later. It's It's going to be very easy. given list of some people who are regularly posting genuine stuff about
data analytics. You can You can follow them. And one way to increase your profile visibility is you have created your LinkedIn profile, right? Now Now go to these people's posts and comment.
Don't say great post, appreciate your insights. Don't comment generic stuff. your thoughts. So, this way what will happen is like your profile gets appeared in their followers and their followers might be some professionals,
right? So, this way your profile becomes more familiar and you can connect with some people might like your comment or this way what happens, right? You start building a connection, you start
straight away to the person's inbox and say, "Hey, thanks for uh you know, responding to my comment. I think we both agree on this point." It's a good starting point to have a meaningful discussion, right? So,
all this thing might look like a lot of effort, but my friend, trust me, this is what the world needs right now. You have to make that effort to get into this is a stage you must understand the business fundamentals and domain
the difference between domains and functions, right? I've given a link to >> [snorts] >> Then, try to understand some business School has some really good documentaries. You can go through that
And [snorts] the most important thing for you to understand is a profit and loss statement. You can simply go to, you know, Google and try a profit and a P&L statement, right? You would get something like this, right? You can just
Amazon's profit and loss statement. So, I'm giving the link to this document. Try to understand this. What is the net revenue and what is the operating income, what are the cost involved, and what is the total profit, right?
Revenue, cost, profit. These are the three major things. You will be working on those things irrespective of whatever job you do because at the end, the bigger picture is based on these things. And this is why I'm stressing that you
need to understand the profit and loss statement. So, I'm attaching the link for the same. All right? And then a core skill is like outreach, how you need to reach out to people. So, I've given a couple of links
here. The first link is like saying like how you can get help, right? Because a nobody helps me in office and things like that. This post will open that mindset for you that you don't need somebody all the time, right? Because if
you go to companies, what really matters is like how much you are self-reliant, how much you are able to do stuff on your own. That really matters. And this post will help you with that mindset. And I'm giving another link which is
talking to you about how to reach out to people. Let's say you are reaching out You can follow this format because this is one of the formats that I really liked from one of our CodeBasics
analyst, he's working in Dubai. And he reached out to me like this and I So, you can follow this format. I'm attaching the link uh here as well. And for motivation, there is an interview with a mom,
Rohini. She was a mom and she had a career break and then she became a data analyst. I have gathered all her insights, what she did during that phase manage with kids. So, that is really inspiring uh in terms of time
management, in terms of uh you know, achieving that goal irrespective of the video. And for this particular week, the assignment would be you have to do one unguided project from this list. When I
say this list, what I mean is our resume project challenges. A lot of you would project challenges. A lot of you would know this and there are about 17 to 18 There are about 18 resume project challenges now and these are from
different domains, different functions. So, just do one of this because by this time you have learned Excel, you have learned Power BI or Tableau and you must be able to do this project, okay? So, do this project and what are you going to
because again, showing your work is very important. You can put that and like I said, make one meaningful comment every day, right? If you're not able to make one every day, at least make three in a week, right? And congrats if you have
reached this stage, only 50 people would have reached this stage. Others Others would have not reached this stage. So, it means what what the fundamental idea you should get here is like it's it's a game of probability, right?
You are increasing your probability by making more engineered efforts, not just efforts, okay? You know that you have to learn some skill and show some skill. You are doing both parallelly. At the same time, you have to
you know, improve your technical skills, core skills, all of that. This is a coordinated engineering effort you are making week by week, okay? So, now going to week 6, 7, 8, you'll be working on
SQL. You'll be learning SQL. I've attached all the free resources. And then the ATS resume, you'll be working on ATS resume and then building your project portfolio, right? Again, this week starts with the research.
There are set of research questions which you will go through. And with SQL, >> it's just that you have to understand basics of relational databases and basic queries like select, where, like, distinct, between, group, order by,
important, subqueries and window functions are very important. Joins, you need to understand all different kinds of joins. And stored procedures are this stage, you don't need to learn
database creation, indexes, or triggers because those will be rarely used, but it's it's a good to know section. Just know it's a good to know thing. And for resources, I'm attaching some resources here. So, you can you can watch them.
So, this SQL tutorial for beginners is 1 hour 30 minutes videos and you'd pretty much get everything from here, right? Whatever you need to learn as an SQL And if you need a structured course, like I said, there is an affordable
course as well. And one core skill I would love you to is presentation skills. Trust me, I've been working in industry for, I don't know, more than 10 years now. I've seen even some senior
a lot of lot of text in their presentation. They they make the presentation really hard to follow. And even I did that mistake until I watched this video. So, there's this amazing video I would recommend thousand times
that watch this video, Death by Presentation. This will open up a lot of you, right? Like this is a very good video, TED Talks, and by David JP
Phillips. And he clearly explains, right? How your eyes read the visuals and how to keep the presentation in a clearly understandable manner. So, watch this video and this will help you
At whatever stage you are in your career, if you're watching this video, it is going to help you, okay? So, now that you're building skills, now you're building your core skills, what is more important? It's building the resume. So,
I've given this ATS resume checklist here. So, you can just click this link and you will get this checklist. So, this checklist again contains every single thing that you would need to build your ATS resume, okay? So, you
just have to follow this checklist and I also told you how to improve your ATS Everything is included there. So, just follow that, okay? So, then another thing is like okay, now you have done everything. You need a place to put all
your skills in one place in a very attractive way. It's like the front door home. So, for that purpose, you would need a And there are many ways to build a portfolio. I've listed those ways. So,
I'll show one of those through our CodeBasics website. So, created those profiles. So, I'm taking example from Rachna here.
show your work to a potential hiring manager, right? You can show your LinkedIn engagement. You can show your project explanation. You can put it on a YouTube. So, this way the person will be able to access all your skills, your
skills, the way you are able to concisely convert the analysis to can access, right? And they can also access your work, your Power BI work. Everything can be done in one place. And if you are looking for a completely free
Linktree is a good place for that. It's nothing but, you know, you can put all your links in one place. And this is very easy to create. You can just go and follow the steps there. And in terms of assignment here,
you can participate in an SQL resume project challenge. I've given the link here. And again, show your work. So, if you're reaching this stage, just know that you are going to be top 40 among the 100, right? So, after
this stage, you're pretty solid, right? Uh you should be applying for jobs. That's the reason why I've asked you to create the ATS resume. So, you have learned Excel, Power BI, SQL, and you have two
portfolio website. And you've created a portfolio website using any of the Linktree or any of the sources that I have provided. And you have started where you can expand your probability horizon by learning some data
engineering basics. And so, that's the reason I have given some topics that you need to learn. And you have given some free resources as well. You just go through this free resources and build your data
that, you need to understand what is stakeholder management communication. I've given everything here, right? Because stakeholder management It's about how you manage the expectation of your stakeholders, which
means your manager, your manager's manager, and their manager without making them follow up, without making them, you know, confused, or without being unpredictable, right? You have to be always predictable among them and
they find they should find very easy to work with you. If senior stakeholders your chances of getting promoted, growing up in the career is very very management communication is very important. And I've given some
learning resources here. You can go through them. And uh So, as an assignment, you finish all the exercises, projects from the resources provided, and pick any domain, right? Like finance, banking, or
health care. And write five to seven clear questions you would ask to a requirement. So, for this purpose, I would recommend one video. If you can go to YouTube and type CodeBasics revenue insights, you would get this project.
And in this project, just go through the stakeholder, you know, This project is one of the best work that we have done. And I mean, you can see the comments, how how people have benefited from this. Because here it is
not about the technical thing. It's about how to manage the stakeholder. In the requirements from the stakeholder, right? You can see like this this gathering the requirement from the stakeholder. And in the later portion,
like review, how the stakeholder will review the dashboard, what kind of questions you should ask to a stakeholder as a data analyst. All those by watching this video, right? Just go through this video as a part of your
we are going to week 10, 11, and 12, where we are going to learn Python and Pandas, and some of the AI generalist tools, and the initial research for this leg, you have to understand different career
course, we have seen that before. Here right? You have to go like okay, what is a finance analyst, what is a supply Try to learn more domain knowledge related to these roles. Because by this
stage, you should be starting to apply for jobs. And you're just increasing your chances of getting the job. So, that's why I'm asking you to look deeper there is a resource I would recommend to follow here. You can you can click this
link and go through that. So, for Python again, you go through the variables, list, dictionaries, tuples, if condition, for loops, all those basic things you you go through the Python thing. Again, we have a free playlist
for Python. You can you can just follow this link. And we also have that in through that. And then after Python, you have to become an AI generalist by understanding some of the basic topics of AI. And we have amazing resources
here. So, you have we have the AI basics for for beginners. And this this particular video will help you beginners' point of view, right? You don't have to understand thousand
what is RNN, CNN. You don't have to understand all of that. All you have to what is deep learning, how it is applied in the industry. Those kind of things you need to understand, okay? That is number one. And number two
is like you need to understand what is the AI agent. How as a data analyst you AI agents. So, there is one project video we have done in that. So, we have used a tool called Quadratic. And we have solved the problem in the supply
chain industry, right? So, this is the project for you. Again, here you will those kind of things. So, please go through that video. It will certainly help you. And one more thing to learn as a data analyst that don't think that you
will always stay in the back end. Right now, with the help of AI, a lot of data means you should be doing more things, like I So, project management is one of those things. So, you have to understand what
is Scrum, what is Kanban. You have to know more about the tools like Jira and Notion. All these stuff are free, right? I've given the links. Just go through help you in the interview process. Even if you're an experienced person with one
fresher, it's going to certainly help you, okay? This is the blog that I've suggested to immensely useful. So, I have the latest links updated here. Just go through
example, you know, how Walmart is using Power BI. This is you would get. If you just spend some clearly understand okay, what kind of architecture they they had before, what
kind of architecture they have now, how it is improving Walmart's you know, here. Because if you are understanding how a solve a problem, then you're actually in the path of becoming a problem solver.
problem solver. You are a person who be able to solve a problem using that. So, that's why all this problem-solving mindset is more important, for which you have to understand how companies are
using these tools to solve a problem, okay? So, all these things are very important from that point of view. And the assignment for this week, make a presentation. I'm asking you to do a video presentation. It's very important
because you need to remove that camera shyness, right? If you are able to talk in front of a camera, doesn't matter how many retakes you do, but if you are able to do that, I'm pretty sure you are going to do a lot better in an
confident now. You have gone through that embarrassment phase. And now you're able to face the people without thinking what you're going to say. This is super video presentation and put it on LinkedIn.
to say, what people are going to think. There is always a starting point, right? This is a starting point for you. And then, right? You can already see that, right? You are you are among the top 20 now, the top 20 person now by doing all
these things, okay? And week 13 and 14, spend some time in learning some automations. So, when I say automation, right? Understand the difference between no code and the low code tool. And Power Automate, Zapier, n8n, make.com, these
are very important free ones. And uh analysts are using in my team. They are using KNIME, they are using Power Automate, and there are a lot of free resources here, again, right? So, you can just manage with free resources.
spending money. All you have to do is like spend your time and keep it information about strategic collaboration, critical thinking. Go through that, and uh we have also seen from the experts who have spoken in this
video, they have emphasized these skills as some of the highest they will look for people, right? At least when they ask you in the interview, you'll be able to answer about them. Okay, these are the things that I will do, okay? And
please please go through them. These are really good books, okay? So, by this stage, you must be already appearing for interviews, and you must be refining are some tools here, there are some useful channels. Uh Data Lemur is one of
so for Power BI, I really like Shashank's uh channel, which is Learn scenarios, which is which is not can go through his channel for them, all right? And uh there are some of the
projects which hiring managers rate highly. I have I've included those projects here. You can do these practice projects. And for job applications, if you want to understand how to uh do cold emailing, there is a resource
here. And so, the assignment is participate in more resume project challenges, keep posting your work, and start writing posts on LinkedIn at this stage, okay? So, I have attached some examples for that as well. And uh
16 week is going to make your preparation for data analytics role very, very solid, my friend. And top of that, I would recommend you to learn Databricks. Uh Databricks is an emerging
tool. It is It is emerging as like how Power BI emerged some 5 years, 6 years back. So, I really feel Databricks is going to go big. So, learn Databricks because it is covering end to end. Now, it is covering the
your uh AI engineer, and everything in one project, which which is what Fabric is also doing. So, I would recommend you to understand both Fabric and Databricks. Go through these projects. If you're
definitely going to add more weight to resume, okay? And uh then if you want to uh separate yourself, uh you know, if you want to be in the top five or top two, you can also do this PL-300
certification. That is again going to give you more credibility. And uh I would say is, right? From all these projects, when you're making your dashboard, don't just simply follow what's shown in that video. A lot
of people do that. Don't do that. I always told you that, make some unique changes. Make your dashboard appear unique. Because if everybody's doing the right? Then you're not applying your own skills. So, whenever you are doing a
project, a free project, or a paid project, whatever it is, just simply don't copy that project. Apply something on top of that, so that your work looks unique, okay? That's very important. And uh
there are some uh you know, resources on books about storytelling you can follow here. And if you want all these things, right? Like all these things in a called the 66 data analytics boot camp. You can explore that. But uh not just
this boot camp, do not go for any paid uh resources without exploring the free the free resources itself, you would know whether this is something good for you or not, right? If you don't explore the free resources and directly go for
if this is good for me or not. So, first explore the free resources, and only if you feel comfortable, then go for paid resources for any paid resources, right? Not just for CodeBasics, anything. Always make this as a thumb rule, okay?
So, always I use this analogy that uh you can eat uh you know, pizzas or sweets, you know, it's it's high calorie content, but you can eat them in like 5 minutes. You can consume 2,000 calories. But to burn those 2,000 calories, you
have to run like 30 minutes. So, just understand that consuming something is very easy, but digesting the same is not, right? That's what happens with learning as well. You can consume a lot of videos, and but if you
don't digest that, which means if you don't practice, it will accumulate as a information fat, right? That will stay as a bunch of information, as and but there is no skill. You have to convert that information to
skill by practicing, practicing a lot and lot and lot, right? And uh that is one uh tip that I will say. And uh you can also do something about group learning. So, there is a Discord uh channel that we
channel, you would find something called a partner finder group. So, there you learn, right? You can check with them if your uh pathways align, you can learn people finding partners, learning buddies, and
together. So, this this way you can keep each other accountable. But this is not people want to learn in groups. You can choose You can choose your style. But helps. So, it's up to you. And this is the final checklist, my friend. So, you
can print this out and put it on your desk somewhere, and try to make more ticks. The more you tick, the closer you are to your job. Okay? And uh I've also attached few other resources that will help you. This
from preparing your body and mind, acquiring skills, showing the proof of work, everything. So, I would say all the best from my end, and uh great that you have watched the video until this point, because uh a
lot of people would have not done that. So, just know that one important thing, don't look for motivation every single day. You won't get that. Look for discipline. If you are following this road map with discipline, I'm pretty
sure you'll achieve success. All the best. That's it, folks. If you have any question, there is comment box below. Thank you very much for watching, and we Thank you very much for watching, and we wish you all the best.