You’re a data visualization specialist looking to transition to data analysis. How do you make the move?

Assess your current skills by identifying areas of strength in data visualization, such as proficiency with visualization tools and design principles. Identify gaps in analytical skills and statistical knowledge. Take online courses or certifications in data analysis, practice using tools like Python or R, and work on analytical projects to build a portfolio. Seek feedback from mentors or professionals in the data analysis field to gauge your progress and ensure a smooth transition.

Data Analytics and Data Visualization are intricately wound together. On closer inspection, I believe what sets them apart is - Data Visualization is about arriving at a pictorial representation of the trends in the data, while Data Analysis is more about Interpretation and Insights. It is always recommended to do a quick visualization of the data before we proceed with the analysis, wherein we can picture the trend first before diving deeper into deriving insights. While Tableau, PowerBI etc. help in visualizing, on a broader scale, SQL, R Modelling techniques, Domain knowledge etc. help in analyzing. Knowing the difference between the two steps is probably step 1 to identify the missing pieces of the puzzle to become an Analyst.

Skill up: Acquire proficiency in data manipulation languages such as Excel, SQL, and Python, coupled with a solid understanding of fundamental statistical concepts. Leverage existing skills: Utilize your data visualization skills for exploratory data analysis (EDA). Build interactive dashboards that unveil the patterns. Network: Connect with data analysts! Attend industry events, join online communities, and showcase your visualization skills - but highlight your interest in analysis. Continuously learn: Stay updated on data analysis trends, tools, and techniques. Read blogs, follow experts, and participate in online discussions.

If you're done with IBM cognos or Power BI for data visualization, one good path could be learning to use IBM SPSS Modeler, where you can do predictive analytics in a very hassle-free manner and explore the world of predictive analytics too??

Durante alguns aprendizados que tive durante minha Pós Gradua??o, a principal lacuna que entendi que deveria ser preenchida para uma transi??o em Análise de Dados é ter um conhecimento mais profundo em conceitos estatísticos práticos a aplicá-los em análises exploratórias. Também é importante conhecer as regras do negócio a tal os dados foram coletados ou ser?o utilizados, pois as regras de negócio podem definir as melhores abordagens estatísticas. Conhecer linguagens de análise e manipula??o de dados também se torna muito importante, pois com a integra??o junto às ferramentas de visualiza??o, podem dar maior autonomia na constru??o de visuais ainda mais maduros e objetivos.

Transitioning from Data Visualization to Data Analyst begins with learning new skills and tools. Start by gaining proficiency in SQL to retrieve, update, and manipulate the data from various sources. The next step is to learn R or Python programming to perform Statistical Analysis, clean the data and Data Analysis. After acquiring skills, it is important to work on projects and datasets that are available on Kaggle and GitHub to have real-time experience.

Analysis is a wide canvas, you cannot develop analytical skill just by going through some books. You need a combination of training specially on analytical skill, data representation and visualization. Along with this you have to play with data around you and start telling stories from data. Like in a story you first introduce characters and then elaborate about them similarly in data also you have to identify key features (Characters) show them first, then elaborate them using central tendencies. Post this show interlinkages between features(Characters) and then conclude your findings. The above approach will help in gaining proficiency in analysis.

One of the best ways to learn data analysis is to play around with a dataset that is interesting to you. For example, if you are into football, you can download a dataset from one of the major football leagues like the Premier League or La Liga, and then start playing around with it. Ask yourself interesting questions and try to unearth the results through tinkering with the data. For example, which team scored the most goals in injury time? Or a more difficult one, is there a correlation between distance run and match result? Using this approach is a great starting point and will invariably lead you to start looking into deeper analysis techniques and statistical concepts.

To transition from data visualization to data analysis, start by building your analytical skills through online courses or certifications. Practice using data analysis tools such as Python or R, and familiarize yourself with statistical concepts. Showcase your analytical abilities by incorporating data analysis projects into your portfolio. Network with data analysts, attend relevant events, and seek mentorship for guidance. Highlight your data visualization experience on your resume and emphasize how it complements your analytical skills.

Data Analysts are predominantly well versed in statistical analysis mostly with Excel and SQL. These tools can be used to summarize and organize data.Familiarize yourself with programming languages like R and Python. A grasp of the combination of the above tools can help you conduct hypothesis tests, regression analysis, and probability distribution.

Demonstrate your analytical skills on your resume. If you have relevant skills, certification or volunteer experience, that would be a great addition. Also here is a tip: Make sure you quantify your achievements under each role.

1. Showcase your analysis skills: Highlight projects where you went beyond visualization and delved into data exploration, hypothesis testing, or drawing actionable insights. 2. Quantify your achievements: Don't just say "created stunning visuals"; explain how your visualizations impacted business decisions or improved user engagement. Always back it up with a meaningful example. 3. Tailor your resume: Adapt your resume for each data analysis job you apply for, emphasizing relevant skills and experiences.

It is important to demonstrate your data analysis skills on your resume, having an e - portfolio of your projects demonstrating your expertise in the data tools is an added bonus. E - portfolios make your skillset more tangible. Try to focus on your data analysis achievements, quantify your resume wherever possible.

Updating portfolio and resume is an important step to start your journey towards job search. While framing resume the most important thing as per my experience is to not only highlight your projects , achievements , certifications but to lay stress on certain terminologies related to data analysis that will make your resume stand out of the crowd and there will be a better chance to get selected by recruiter on resume scan.

In today's job market, standing out is crucial. Here's how to shine: Build a Professional Portfolio: Highlight your skills, projects, and certifications. Use Online Platforms: Optimize your LinkedIn, GitHub, or personal blog profiles. Highlight Achievements: Tailor your resume to emphasize relevant skills and certifications. Provide Evidence: Include links to projects and examples of your work. Network Effectively: Engage with industry peers and leaders to establish credibility. By showcasing your skills and certifications effectively, you can make a strong impression and land your dream job.

Document your journey and network with professionals in your field. Join data events, data challenge and put the news out there that you are one of us now. Also apply for jobs but don’t apply aimlessly, have a strategy for applying. You can apply to 10 jobs a day so you can have the time to read each job description and tailor your cv and cover letter to the role. Space yourself out, don’t try to overwhelm yourself.

Data Analysis is a lucrative as well an interdisciplinary endaevor It can be applied anywhere and everywhere 1. Find your niche 2. Do you like all industries and Data in general then you can become a consultant 3. If you love a specific industry that fans your flames, learn specifically about how data contributes in that space 4. HIT APPLY

Networking is the key. Try to attend conferences / events related to data and analysis. If you aren't able to make it to a conference physically try joining in virtual events and make sure to network with like minded people. Make the most of platforms like LinkedIn and twitter to increase your digital presence. Also whenever possible try taking part in hackathons, I have found hackathons to be super useful for networking.

Grow your professional network by engaging with individuals in the data analysis field. Attend industry events, join online communities, and seek advice through informational interviews. Apply for data analysis positions, emphasizing transferable skills from your data visualization background in tailored job applications. Proactive networking and applications enhance your chances of securing opportunities in data analysis.

Networking is essential in applying for data analysis jobs as it provides access to hidden opportunities, industry insights, referrals, professional development, and support. Building relationships with peers and industry experts increases chances of hearing about job openings, gaining valuable knowledge, receiving referrals, and accessing mentorship. Networking enhances job search effectiveness and career growth in the data analysis field

Things to note: 1. The journey is long and you won’t master it all at once. 2. You will forget some things you learn, it’s okay to refer to your notes. 3. Getting that job interview or that job role might take some time, don’t give up. 4. Embrace AI, it is not the enemy. I wish you luck!

Note: 1. Focus on a specific niche: Specializing in a particular industry or data analysis domain can make you more appealing to employers. 2. Keep learning: Data analysis is a constantly evolving field, so continuous learning is crucial. Stay updated on trends, tools, and techniques. 3. Be patient and persistent: It may take time and effort to make the transition, but your passion and dedication will pay off.

Consider staying informed about industry trends and emerging technologies in data analysis. Keep up with relevant certifications, seek feedback from mentors or peers, and remain adaptable to changes in the field. Continuous learning and flexibility are crucial for a successful transition.

1. Keep updating your resume after every few days if there is no luck with the earlier updates ones. 2. Always learn from your interview experience as each interview learning will make you stronger and one step closer to master your interview. 3. Always do research for the role you are interviewing for and be straightforward on asking questions related to jobs and what role you will be hired for. 4. Don’t give up as each failure (if any) is a learning step for your next success

For me, it always goes back to programming, mostly in Python - mastering that gives you the flexibility to jump wherever you want in the data space, from analytics to AI, engineering to visualisation.