You’re interested in learning data analytics. What’s the best way to start?

Specificity: Consider specifying what aspects of data analytics you want to focus on, such as statistical analysis, data visualization, machine learning, or data engineering. Relevance: Align your goals with your interests, career aspirations, or the demands of your current job or industry. Measurability: Establish metrics or milestones to track your progress. This could include completing specific courses, mastering certain skills, or working on real-world projects. Realism: Set goals that are achievable within a reasonable timeframe and with the resources available to you. Time-bound: Define a timeframe within which you aim to accomplish your goals.

In my experience, the best way to start learning data analytics is by gaining a solid foundation in statistics and data analysis concepts. You can begin by taking online courses or reading books on topics such as data visualization, exploratory data analysis, and statistical modeling. It's also helpful to practice your skills by working on real-world data sets or participating in data analytics competitions. Additionally, learning popular tools and programming languages used in data analytics, such as Python, R, SQL, and Excel, can enhance your proficiency in the field. Networking with professionals in the data analytics community and seeking mentorship can also provide valuable insights and guidance as you start your learning journey.

While training people and the skills needed for data analyst, I have realized that most of the time, the real skill needed to become a data analyst is understanding Data, then there are tools available which can be helpful to make data more insightful. For anyone who want to start Data analytics journey, I suggest start with Excel, get some same data, try to find some useful information about it. Then start with SQL technologies, learn how to get this Excel data in SQL database. Because SQL is the technology which is the base for many visualizations tool. So having fluency in SQL does helps a lot. While learning SQL, focus on understanding end-to-end picture of data Analysis.(Data Collection,cleaning,processing, modeling & Visualization)

I recommend that you start with a a specific question, and make sure you'll be able to give actionable insights at the end. That is, what will people start doing, stop doing, or continue doing (with greater confidence) as a result of your analysis. Perhaps you have something at your current job that you could focus on. If you're in school, pick something that you hope to work on in the future. You can also volunteer to do a small project for a local project; just make sure that you can conclude with actionable insights.

To get the most out of your learning experience, you need to have a clear goal in mind. Having a clear goal is important for a few reasons. First, it helps you stay focused and motivated. It's easy to get overwhelmed or distracted when you're learning something new, but having a specific goal to work towards will help you stay on track. Second, it helps you choose the right resources. There are a lot of different data analytics courses, books, and other resources out there. Knowing your goal will help you find the ones that are most relevant to your needs. Third, it helps you measure your progress. As you learn, you can track your progress towards your goal and see how far you've come.

You need surprisingly little to do solid data work. Although I do my professional work in R and I'm capable of doing sophisticated analyses, for my work with nonprofits I usually focus on spreadsheets. (I prefer Google Sheets.) Knowing how to enter data in a spreadsheet, sort and filter values, create a pivot table, and make bar charts and line charts can get you far. The technical work is not the hardest part of a data project; rather, it's (a) understanding the goals clearly; (b) being able to interpret the actionable insights in your analysis; and (c) communicating those actionable insights clearly to others.

Familiarize yourself with tools and programming languages used in data analytics. Excel is a great starting point for understanding data manipulation. As you progress, learning programming languages like Python or R.

Learning the basics is a crucial part of your learning journey. It helps you lay the foundation for more advanced topics and keeps you from getting overwhelmed. It also makes the whole process more efficient and effective. If you try to jump into complex topics without learning the basics first, you're likely to get confused and frustrated. Once you have a good grasp of the fundamentals, you'll be able to learn more quickly and efficiently.

In my experience, I noticed that when starting a journey into data analytics, it's crucial to understand the foundational elements that form the foundation of this domain. Start with the data analytics lifecycle, from data collection to actionable insights. Familiarize yourself with the diverse data types—quantitative and qualitative—and the analytical methods suited for each, from descriptive statistics to predictive analytics. Equally important is an awareness of ethical considerations and compliance in your analyses.

Begin your data analytics journey by mastering fundamental tools like Excel and SQL. Familiarize yourself with basic statistical concepts, and explore online courses or articles on the same. that being said, the best way to learn anything is by doing hands-on! Practice by working on small projects to apply your skills and gain practical experience.

Working on personal projects is the best way to grow your skills. And for data analytics, learning python, Excel, SQL, and then PowerBI or Tableau helps better. Along with that the python packages- Pandas, and pyspark too.

A valuable contribution project work has is that projects involve debugging errors and troubleshooting issues. This process teaches you how to identify problems, analyze root causes, and implement solutions—a crucial skill in data analytics where dealing with messy data and unexpected errors is common.

There are three ways to learn: 1. studying, 2. practicing and 3. teaching. For me, practice is the easiest one, because you can find many resources to study but some might be wrong, or you can try teaching but finding an audience might be difficult, but practice definitely has no limits. There are many free and paid datasets available in the Internet that you can use to practice the different stages of the Data Analysis process; from cleaning and transforming, to developing reports and dashboards.

Some online educational institutions hold data analysis challenges. You can participate in these challenges, place your project in your portfolio, learn analytical points from other participants’ projects, and even some of the challenges have prizes, for example, you can use free courses. One of the best resources for doing projects are sites like Kaggle and DataCamp. Also, don’t overlook AI tools for learning

One of the best ways to learn data analytics is by doing. Platforms like DataCamp and Kaggle offer a wealth of projects and competitions tailored to different skill levels and interests. These platforms not only provide structured learning paths but also allow you to explore datasets mimicking real-world scenarios. I've found that using datasets that genuinely interest you or align with your career goals is a good starting point. Whether it’s analyzing sports statistics, predicting housing prices, or understanding social media trends, working with datasets that resonate with you enhances learning and motivation. This approach not only builds technical proficiency but also hones analytical thinking and communication skills.

When you finish a project, it's a good idea to share it with somebody who (a) is not a technical person; and (b) doesn't know much about the topic. That way you can be sure to keep things clear enough that a broad audience can see how you reached your actionable insights and how they can put them into practice.

El análisis de datos también tiene su comunidad al igual que otras ocupaciones, muchos estarán dispuestos a ayudarte en el camino y soy un ejemplo de ello. Estoy siempre a disposición para preguntas u orientaciones.

As you delve into data analytics, remember you're not alone on this journey. Seek feedback and support from fellow learners, mentors, or experts. Engage with online forums, groups, or networks dedicated to data analytics. Attend webinars, workshops, or events for learning and networking. Embracing feedback and support will enhance your skills, tackle challenges, and grow your network in the data analytics community.

Seeking feedback and support in data analytics is essential for beginners. It validates understanding, identifies weaknesses, and fosters learning from mistakes. Feedback broadens perspectives, enhances problem-solving skills, and builds confidence. It also opens networking opportunities, collaboration opportunities, and facilitates continuous improvement, which are crucial for navigating the complexities of data analysis effectively.

The best way to start is just to ask someone that is in the industry for 30 minutes of their time. Asking someone who posts or is active on LinkedIn can be an easy way to identify a person that can help guide you. I have taken the time to advise individuals about what my experience has been like and what I find important. Not only has it been rewarding for me but has proven to be helpful to those that needed some direction. It’s also the best way to discover what path of analytics you are interested in. Just ask away!

In the dynamic realm of data analytics, continuous learning is key. Stay abreast of the latest trends, techniques, and best practices. Regularly reassess your goals and progress, tweaking your learning strategy as needed. Celebrate your wins and glean lessons from setbacks. By perpetually learning and refining your skills, you'll nurture your curiosity, bolster your confidence, and stay adept in the ever-evolving landscape of data analytics.

Fortunately, spreadsheets, bar charts, and line charts don't change dramatically over time. What does change, however, is the context that you and your clients are working in, as that will affect your interpretations and actionable insights. Think about the recommendations you would give a nonprofit organization 40 years ago before the popularization of the Internet, 20 years ago before the rise of social media, over even 2 years ago before the advent of ChatGPT. All of those affect your working environment and what can be done, even if the basic elements of your analysis remain the same.

always try to reach out to experts for feedback and try to study their learning path or career path as they would help you a lot in creating your learning path

In the era of data-driven decision-making, the demand for skilled data analysts continues to soar. The goal is to turn a spreadsheet into a story of powerful insights. While much energy is placed on what worked, I would say focus your energy on what the data can reveal about what didn't work. That's the best opportunity for businesses.

Data analytics is wide & dynamic field in which every day new tools/skills are required to learn to survive in this competitive field. Constantly, we've to learn the new technologies/tools. Hands on practice will boosts our confidence to learn new things as well as work efficiently.

Nobody knows how to do everything, so don't intimidate yourself. Simple work is usually the easiest to interpret, the easiest to troubleshoot, the easiest to apply, and the easiest to generalize to new situations. Focus on basic processes – again, spreadsheets, bar charts, and line charts – and devote your most creative work to interpreting and applying your insights.

Don't underestimate the basics! Math and statistics form a strong foundation. Take online courses. There are many great free resources available online (such as W3Schools). Practice! Courses are great, but knowledge is unlikely to stick unless applied in day-to-day tasks. Engage with communities. Share and learn from others. Stay updated. You'll never stop learning, so get used to always "starting" to learn data analytics.

I think that it's best to start by understanding the impact of data analytics on decision-making, business strategies, and daily life first because this way provides motivation and context. Once you grasp its significance, learning the technical skills becomes more meaningful. Exploring case studies and real-world applications can illustrate its value before diving into the technical aspects.

Embarking on your journey in data analytics is a meaningful endeavor that requires a combination of clear aspirations, fundamental knowledge, and hands-on experience. First and foremost, it's important to clearly define what you hope to accomplish with data analytics—whether it's for professional growth, a specific project, or simply to expand your skill set. Starting with the basics, take the time to grasp important concepts in statistics, data manipulation, and visualization, and put your learning into practice by engaging in real-world projects. Also, don't hesitate to seek feedback from mentors or communities to refine your skills, and remember to stay curious—continuously learning and improving as you progress.