#FutureOfDataAnalytics

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The Future of Data Analytics: AI and Machine Learning

The future of data analytics is poised for an unprecedented transformation with the integration of Artificial Intelligence (AI) and machine learning. As organizations increasingly rely on data-driven decisions, AI and machine learning are becoming pivotal in unlocking insights, predicting trends, and optimizing processes. Platforms like AI Swades are making advanced analytics accessible, empowering businesses to innovate and thrive in a competitive landscape.

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What is the Future Scope of Data Analytics in India?

Data Visualization has changed the way how we visualize data. The changing time, evolving digitization, and the Future scope of data analysts in India have caused the way complex data is handled. Visualization of complex data has brought about a revolution that surpasses the artificial intelligence intellect. The very reason that makes us humans better than algorithms, coding, and machines is the human intellect and how it evolves with exposure to make a future in Data Analytics.

Future Scope of Data Analysts in India:

India is a land of multiple opportunities and any firm trying to establish itself in the land would agree that the kind of trade in India has evolved over the past decade. Traders, businesses, and startups in India have become a lot more competitive and healthy competition is visible in the selection and hiring process and hiring process makes the Future scope of Data Analyst because it’s become need first then Demand.

But, to be able to access the insight hidden away in the mass accumulated data needs a careful analysis and representation in a simple format like a report or an interactive dashboard. So, the generated data can be queried and profitable insights might be generated to gain an edge over the vast competition in the market.

Data Analytics Skills for a Successful Career:

Data visualization is a skill learned by all but mastered only by a few. As a result, it has created a massive void in the industry that needs filling up. The void is the lack of skilled professionals in the field of data analytics. There is a rising need for more and more data analysts and that seems like good news for any fresher looking to make a career in the data analytics industry. The skill itself is not a challenging one to master, but getting a hang of being able to question the accumulated data is something of a challenge that even sometimes the most seasoned skilled professionals in this field of work tend to face.

Data Visualization:

In the context of visualization, every report should affirm directly the content. It’s got to get out of the way because it’s about the relationship with the viewer and how they reason with the content. Style and aesthetics cannot rescue failed content. If the words aren’t true, then even the most visually appealing content cannot transform false facts into truth. There are enormously beautiful visualizations, but it is proof of the truth and the authenticity of the information. The big steps in showing information began with cartography about 6,000 years ago, when the first map was scratched into a piece of stone and that is how we have wound up now with the most widely seen visualization in the world. Take the example of Google Maps, where people are using visualization to transform a flat surface called a map into a visualization.

The next big step was the development of real science. Galileo got his telescope going. He made stunning sketches of sunspots as he watched the sun for 40 days. After which he assembled the engravings of the sunspots and visualized what he had recorded and so the history of visualizing data is very substantially a history of science. Data visualization is not just some airy-fairy shenanigans but an extremely creative process, but it’s a very linear process of decision-making that you can do based on a few basic principles.

Three things that a user should keep in mind while designing a visual: –

As the designer what you have to say and what you want to communicate.

That reader is not you and they’re going to come with their context and their own biases and their assumptions and you need to account for that.

The data itself, what that has to say, and how that informs the truth.

There’s a lot of subconscious brain activity happening. We evolved for it to happen that way and to see things and make snap decisions. We have to be able to recognize patterns right away and make snap decisions on them to survive and that can be an advantage as a designer. The user may communicate a lot of information very quickly because we all have brains that are designed to recognize patterns this way. But also, there’s the emotional impact.

We as human beings tend to react to design and art and the aesthetics of a piece, just as much as we react to the information contained in it. So if the user wishes to change someone’s mind, if he/she intends to change someone’s behavior, sometimes presenting the information in a visual format is the fastest way to get them to engage with that information. Truth is one of those ambiguous things that you can’t really define and probably change and evolve is the enhanced understanding one has of the topic. Data itself is a result of research.

So, in simpler terms “data is just a clue to the end truth”. We believe that a successful infographic tells a story. It links massive and sometimes complicated data in a way that many people can understand.

The first step usually is always to dig deeply into the data yourself and find each key point and create a hierarchy and a narrative out of that story. When the user starts to merge different pieces of information and when they start to learn really what it’s all saying, the narrative becomes clear. The one key fact that everything can revolve around, is the hero of the piece which is the data visualization. There’s one single piece of data or insight that people respond to any kind of feature that encapsulates the whole vision and invites people in to see the nuances and all of the rest of the story around it. When you look at a piece, that has successfully translated data from something complicated to something simple.

The deepest curiosity lies on the edge between data and culture. There’s a revelation, which is to show us something that we’ve never seen before. Anybody can visualize data in Excel and display some bar charts. But with data visualization, it’s about showing them something in this kind of loose narrative frame that they can interpret. Part of it is leaving it open to interpretation, but part of it is also not knowing. Nobody has some miraculous masterful understanding of this system that you don’t. The user may have some ideas about how these systems might be changing and how they might be growing may be important for culture and society, to share some of those ideas with colleagues. And maybe the user can put together something that someone else wouldn’t have been able to. The general population is a lot smarter than we think. So, it’s not about knowing your audience but rather about respecting your audience and knowing the content.

Popular tools utilized for Data Visualization:

1. Tableau

The most popular visualization tool in use. Launched in 2003 and since then has been growing strong. The application has a knack for handling huge masses of data with ease.

2. Power BI

Launched by Microsoft in 2013, it has been going headstrong in the Gartner quadrant and is extremely popular with small to mid-sized businesses because of its low-cost subscription package.

3. QlikView

It is another popular visualization tool famous among analysts that helps them enhance data visualization processes. Similar to Tableau, QlikView is popular for handling big masses of data with ease. The only issue is that it is not available at a cheaper cost for more personal use.

Data Visualization salary in India: –

Salary starting at entry-level for a Data Visualization professional – INR 3.25 lakhs per year

Salary starting at mid-level for a Data Visualization professional – INR 6.35 lakhs per year

Salary starting at senior level for a Data Visualization professional – INR 8.5 lakhs per year

The grasp of the concepts for both the profiles may differ but a fresher with the relevant knowledge in the field and with sufficient years of experience and a little bit of help from Analytics Training Hub might help any seasoned professional data analyst ace an interview for a Data scientist in the future. The skills are trainable but it’s the attitude and aptitude to of adapting oneself to the knowledge and implications of data analytic tools like Tableau which may move to one’s advantage.

The brighter prospects of data analytics have already been confirmed by a business review done by Harvard University, claiming ‘Data Scientist as the sexiest job of the 21st century. Although we believe that Data Analyst and Data Scientist sound like two completely different job profiles, especially with the suffix words of both the job titles sounding or rather being the same. There’s no need to worry as the job profile of a ‘Data Analyst’ is a stepping stone or rather the first step to becoming a ‘Data Scientist. Also, the Future Scope of Data Analysts in India is in high demand. So if you are a fresher or just about to switch your profile in Data Analytics so do not worry Future Scope in Data Analyst is way more widespread than in other fields. Choose your path in Data Analytics because Salary Package is above your expectations as a fresher.

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