You’re new to the data analytics industry. How can you make a name for yourself?

1) As a new comer you bring fresh perspective to things and ability to ask questions (curiosity) this is essential for all jobs including data science and analytics. TBH most of skills simply emanate from thereon (hard or soft) 2) Specifically, as a new comer you lack domain expertise and that's always learnt on the job in midst of your seniors and mentors. There is no replacement of this experience ever, don't miss out on it , be proactive to expedite this tacit learning. 3) Lastly, give yourself time and visualize what success looks like and challenge yourself to achieve it. Rest will follow.

First thing first…understand what are the different models and tools available today and what they can do and can’t do. Secondly, understand where the pain lies…what your company needs that nobody else is solving right now.

Basics form the foundation of my data career. I begin my learning journey with one tool/language, solidify my fundamentals, and then move on to another. Watching online tutorials, practicing, reading articles, and engaging in conversations with data professionals boosted my confidence in my skills. Sharing my learning through LinkedIn posts enables me to understand how data professionals worldwide approach similar topics. That's fantastic!

Becoming a data analyst can be an exciting journey, I will suggest my way of learning for fresh data aspirants. 1.Start by understanding the fundamentals of data analysis, we can get all from resources like online courses, tutorials can help build this foundation. 2. Familiarize yourself with popular tools and technologies used in data analysis, such as Excel, SQL, Python, R, and data visualization libraries like Matplotlib etc., 3.Apply your knowledge by working on real-world projects or datasets. (Kaggle,github...) 4.Keep upskilling yourself.

1.Programming- Focus on either Python or R. Most of the companies work with Python. I would recommend you to go ahead with it. Practice from Hackerearth and other online platforms as per your preference. 2.Database query language- Focus on SQL(MySQL) and work on projects which would involve the use of different queries analyse them. 3.Data Visualization- In industry there are multiple BI tools, choose either Tableau or Power BI. Create multiple projects. Create at least one project in 3 industries like logistics, health care and finance. Additional knowledge of Statistics, data modeling, data cleaning and wrangling would be better. Also work on your communication skills, problem solving skills and confidently analyse hypothetical scenario

As a newcomer in data analytics, showcasing skills is vital. Start by building a portfolio of projects demonstrating your capabilities in data analysis, visualization, and problem-solving. Share these projects on platforms like GitHub or create a personal website. Additionally, contribute to online forums and communities, sharing insights and solutions to demonstrate expertise. Collaborate on industry-relevant projects to gain practical experience and expand your network. Consistently showcasing your skills and knowledge will help establish credibility and make a name for yourself in the data analytics industry.

While showcasing your skills is certainly important, I have to say that data analytics starts with ensuring the data is cleaned. I can't even begin to speak to the number of times I've been incorrect metrics by someone who didn't clean the data first. AFTER ensuring your data is accurate & cleaned, then skills can start to shine. Show what the key points are, and cater it to your audience (VP, Director, Project Manager). Learn what each role feels is important for their part in running the business, and then cater your analysis to them. Different roles requires different versions, because different roles care about different things.

To make an impact in the data analytics sector as a novice, develop a solid portfolio of projects that demonstrate your ability to analyze data, generate insights, and effectively convey findings. Share your work on platforms such as GitHub, Kaggle, or personal blogs to demonstrate your competence to potential employers or colleagues. In workplace show the essential points and tailor it to your top management. Participate in data analysis competitions and contribute to open-source projects to increase your visibility and recognition in the community.

You need to understand data and showcase your skills. First, get a dataset from Kaggle or other online data sources, then start to understand the data and the most important KPIs in this field. For example in sales you google most important sales KPIs to have a better understanding of which KPIs you need to get from this dataset. Then you proceed by creating the logical data models and how you create the models that will serve delivering those KPIs into an insightful dashboard. You showcase all this on your LinkedIn Profile to get better exposure.

To make a name in data analytics as a fresher: 1) Learn Key Skills: Master tools like SQL, Python, R, and visualization software. 2) Get Certified: Complete courses from reputable institutions. 3) Build a Portfolio: Showcase your projects and analyses. 4) Network: Connect with professionals on platforms like LinkedIn. 5) Apply for Internships: Gain practical experience. 6) Stay Updated: Follow industry trends and news. 7) Contribute to Forums: Share insights on platforms like GitHub or Kaggle. 8) Craft a Strong Resume: Highlight your skills and experiences.

Continuous Learning - Stay updated with the latest tools, techniques, and trends in data analytics Networking - Build relationships with professionals in the industry by attending events, joining online communities, and connecting on platforms like LinkedIn. Personal Projects - Undertake data analytics projects to showcase your skills and creativity. Contribute to Open Source - Contribute to open-source projects related to data analytics. This not only helps you learn but also increases your visibility within the community. Blogging and Content Creation - Share your knowledge and insights through blog posts, articles, or tutorials Online Presence - Maintain a strong online presence by regularly sharing relevant content.

.. Building a network is indispensable in the realm of data analytics. Beyond the exchange of experiences and insights, networking in this field involves active participation in online communities, forums, and events dedicated to data analytics. Engaging in webinars, workshops, and conferences not only connects you with experts and mentors but also presents opportunities for professional growth. Contributing to open source projects, collaborating on data initiatives, or volunteering for data-related causes enhances your visibility and opens doors to diverse opportunities in the dynamic landscape of data analytics.

There were so many eye-opening insights which were already being shared and I would like to second to the majority of the ideas and express the fact that nowadays it has become a mere race especially while transitioning to data roles to show-off your skills in that pursuit folks end up copying the works of others, using a namesake dataset from Kaggle (very ubiquitous!). The same is unethical as there is a process that needs to be followed that of defining the problem, the end goal and the KPIs that you'd use in order to strenthen your case and I guess that's when you learn to fit into the real world scenarios!

Networking with peers is the most crucial part. You need to be connected with people in the same industry and learn from the best. You also need to spread the word and showcase your competence. You need to show that you have what it takes to succeed in this area.

O que é análise de dados? Análise de dados é o processo de aplica??o de técnicas estatísticas e lógicas para avaliar informa??es obtidas a partir de determinados processos. O principal objetivo da prática é extrair informa??es úteis a partir dos dados.

For someone who is new, knowing the latest trend is a good-to-have but the most important thing is getting the basics right. You'll be known by your work so better make sure you do it right! Software vendors keep improving their offerings often so keep in touch with other knowledgeable people to understand best practices and know when to use which tool.

Acompanhar as tendências é essencial no dinamico campo da análise de dados, que exige aprendizado e adapta??o contínuos. é importante estar a par das últimas novidades, inova??es e desenvolvimentos da indústria, incluindo novas ferramentas, técnicas, aplica??es ou desafios. Seguir blogs influentes, ouvir podcasts, assinar newsletters ou revistas especializadas em análise de dados s?o ótimas maneiras de se manter atualizado. Também vale a pena se inscrever para receber relatórios da indústria, trabalhos de pesquisa ou estudos de caso que apresentem as melhores práticas, histórias de sucesso ou li??es aprendidas. Manter-se informado sobre as tendências ajuda a permanecer relevante, bem-informado e inspirado na indústria de análise de dados.

To make a mark in the data analytics industry, staying updated with trends is crucial. Follow blogs, podcasts, or newsletters that discuss the latest innovations and tools in the field. Subscribe to industry reports and research papers to learn about best practices and success stories. Keeping abreast of trends ensures you remain relevant and inspired in the dynamic world of data analytics.

A análise de dados é um campo dinamico e em constante evolu??o, que exige aprendizado contínuo e adapta??o. Para acompanhar as últimas tendências, inova??es e desenvolvimentos da indústria, como novas ferramentas, técnicas, aplica??es ou desafios, siga blogs influentes, podcasts, newsletters ou revistas que falem sobre temas de análise de dados. Também vale a pena se inscrever em relatórios do setor, artigos de pesquisa ou estudos de caso que mostrem melhores práticas, histórias de sucesso ou li??es aprendidas. Manter-se atualizado vai te ajudar a continuar relevante, bem-informado e inspirado na indústria de análise de dados.

I believe that constant upskilling plays a strong role in the field of Data Analytics. With Technology constantly changing around us and AI, it is pivotal to be able to solve real-world problems with the latest tools and technology.

Yes, you should be curious. If we are talking about data analysis from the perspective of reporting then you should be curious to know about your data sources and how are business concepts and operations represented in your data sources. Assume you are required to build a dataset to get the transactions of some customers and you are provided with data source so you should ask yourself what does each record represent i.e. is the single transaction represented with one line or it can span more than one line so we should then know what each line represents. So get a dataset and ask yourself one question and try to answer it.

Entenda o processo! Saber as ferramentas (SQL, Python, Dataviz, cloud's) é apenas um dos passos para entregar valor. Quando se conhece o processo e a dor do negócio, a entrega de valor irá te demandar muito menos esfor?o e até mesmo menos domínio de ferramentas complexas.

To make name in Data Analytics for someone who is new in this industry should know these things very well, he or she must be creative and curious to do something new every time to adapt anything on current time or run-time because Data Analytics is basically like a story telling it depends on the person who tells or describes it well through making dashboards, running sql queries and more. So try a Large and complex data, clean it give a problem to yourself and try to solve it!!

Il est primordial de bien comprendre les données avant toute analyse. échanger avec les experts des différents secteurs, producteurs de données permet d'avoir une meilleur vision des données. Un data analyst se doit d'aider à la décision mais également de faire parler les chiffres.

Creativity and curiosity are inherent traits that vary among individuals. Aspiring data analytics professionals should introspect and assess their own inclinations towards these traits. Personal and professional experiences significantly shape one's level of creativity and curiosity. Recognizing one's natural inclination can guide them towards leveraging these qualities to excel in data analytics, ultimately driving their career to new heights.

To make a name for myself in the data analytics industry, I would start by focusing on building my skills and knowledge. For example, Excel is the most important skill that anyone should focus on. Most of the stakeholders prefer things to be done in Excel than on any tools(funny but true ;). I would actively seek out opportunities to learn from experienced professionals, take on challenging projects, and stay updated with the latest trends and technologies in the field. Additionally, I would look for ways to showcase my work, such as through creating impressive data visualizations and publishing on social media platforms.

To carve a niche in data analytics, focus on perfecting your analytical skills and familiarizing yourself with the latest tools. Keep abreast of evolving trends, pursue ongoing education, and build connections within the industry. Enhance your visibility by contributing to open-source initiatives and authoring thought-provoking articles. Find your specialty, work collaboratively, actively solicit critiques, and exercise patience. With persistent, high-caliber efforts, you’ll gain acknowledgment. Your distinctive way of tackling problems will make you stand out.

The most important factor a fresher should consider is 1. Doing as many projects as one could 2. Doing every project in a different style possible 3. Implementing at least a few new concepts in each project 4. Have notes of what is implemented and it's behaviour As every data analytics project consists of data modelling, data engineering and visualization focusing on all these areas will help to learn all areas As a fresher one can shine with these factors having in mind with in less span of time by mastering the skill. Rather than theoretical study, practical implementation is the quickest way to learn

Try to always understand the story that data is signalling out. Make sense of the data, create a story of the data and learn how to tell it in a Simple form for even a kid to understand.

Keep your ears open… find a mentor.. learn from others, especially where you’re working, what didn’t work- and why it didn’t work - as well as what has worked. Find someone at your work that has an important business problem that can be solved by a quick BI win - a spreadsheet that has a thousand pivots and calculations and create a whiz-bang BI solution with visualizations (a map is always a winner for me) and then let that user do ALL talking, especially to leadership.