You’re new to data analytics. How can you quickly get up to speed?

While data analytics may be very broad, it is so much important to consider areas that interest you. Marketing Analytics, Finance Analytics, or Web analytics are some of the interesting aspects. Focusing on a specific area allows you to delve deeper into relevant tools and techniques.

As someone who did a complete 180 in terms of their career, it was important for me to identify which part of data analytics not only piqued my interest but where I could potentially see myself. Once I had done that, it was about having a plan about what my path may look like. This included, learning what programming languages I'd need to learn, as well as what other skills I'd have to harness to really be good at it. And from there, it was just learning, learning and more learning. Learning from as many different sources and receiving learning in many different formats. It's important to being open to learning, making mistakes and growing from them.

Develop a plan centered around a problem you're passionate about solving. I'd recommend the following steps: - Identify an Impactful Problem: Choose a problem that has significant business implications. - Understand the Problem: Dive deep into the nuances and impact of the problem on the business. - Assess Available Data: Analyze the data you have and determine what is needed for a comprehensive analysis. - Explore Solutions: Outline potential solutions based on your understanding of the problem and data. - Acquire Skills and Tools: Once the problem and potential solutions are clear, identify and learn the necessary skills and tools to expedite the problem-solving process.

Many people face uncertainty about where to start when studying data analysis. Often, they end up diving into a multitude of topics and tools, only to realize that none of what they studied actually addresses the problem they had in mind. To avoid this trap, the first step is to clearly understand your needs and the problem you want to solve. Instead of getting lost in a flood of concepts and tools, focus on defining what you need and what objectives you want to achieve with data analysis. This way, you can choose the most appropriate resources and techniques for your specific case, making your learning process more efficient and targeted.

When delving into data analytics, it's beneficial to define your focus. Identify your goals—whether it's enhancing business performance, addressing a societal issue, or pursuing a personal interest. Consider the type of data available to you, the questions you aim to answer, and the problems you want to solve. Establishing a clear focus aids in setting specific learning objectives, selecting pertinent resources, and gauging your advancement in the field.

In addition to this article, look out for online resources with sample datasets. Practice basic queries, data exploration, and analysis techniques. Platforms like Kaggle offer great opportunities to work with real-world datasets.

I personally would suggest to follow below steps to speed up the journey in data analytics: 1. Master Basics: Dive into data types and statistical methods. 2. Hands-on Experience: Feel the thrill of analyzing real-world datasets. Python or R? Your choice. 3. Continuous Learning: Immerse yourself in online courses and vibrant communities. 4. Showcase Your Journey: Build a portfolio that reflects your unique perspective and projects. 5. Network: Connect with mentors and peers who share your passion. 6. Specialise: Explore different domains—be it business analytics, ML, or data visualization. Your expertise knows no bounds. 7. Curiosity: Embrace challenges with an insatiable curiosity. Remember, every setback is a stepping stone to success.

When you start data analytics, there would be pressure to get certified fast. While certification is a good step, it should not be the first thing you focus on as a beginner. Instead learn the basics. Focus on the 'WHY'. Why use a particular method and not the other, why is data validation important, and why shouldn't you skip data cleaning. By focusing on your WHY you would learn the basics of data analytics faster.

Before starting data analytics, grasp core concepts like data types, structures, sources, quality, cleaning, manipulation, visualization, and storytelling. Know the analytics lifecycle for managing projects. Learn through online courses, books, blogs, podcasts, or videos. Understanding principles and best practices is key, not memorization.

Familiarize yourself with fundamental concepts like data types, variables, and basic statistics (mean, median, mode, standard deviation). Understanding these helps in analyzing and interpreting data effectively. For example, knowing the mean income of a population gives an overall idea of wealth, while standard deviation shows how incomes vary around the average.

Learning the basics helps to understand what one needs to get started with in terms of leanring and using tools. Next step - Get Familiar with Tools: Excel: Master functions like pivot tables, VLOOKUP, and basic statistical functions. SQL: Learn to query databases for data manipulation and analysis. Python/R: Choose one for data manipulation (e.g., pandas, dplyr) and visualization (e.g., matplotlib/seaborn, ggplot2).

If you are not familiar with programming languages you can start with Excel for its simplicity and widespread use in data analysis. Online platforms like Kaggle and DataCamp offer interactive courses to practice your skills. Choose tools that align with your learning style and goals, and don't hesitate to explore various resources to enhance your data analysis proficiency.

Whenever feasible, prioritize open-source solutions. This approach not only reduces project costs but also provides access to a community where you can easily share and collaborate on your work.

Its also Important to continuously evaluate and adapt your toolset as your skills and project requirements evolve. With new tools and technologies emerging regularly. By staying informed about the latest advancements and industry trends, you can identify opportunities to focus on your workflows by allowing yourself new capabilities. Don't hesitate to try different tools and techniques to see what works best for you and your specific projects.

Consider this a complement to what others have written about tools here. You need to understand data life cycle to understand what jobs need to be done and the tools that are available for each job. Suppliers can bundle up different solutions inside the same box. For example, most people will say that "Power BI is for dashboards" and that you use "SQL/Python/R Clients or noteboos" to extract and transform data. But, you can actually do all this stuff inside Power BI, using data connectors and Power M/DAX to wrangle the data. Is that a good way to go? Depends. You won't get high performance so not good for final products. But if you're doing an exploratory analysis then you'll definitely get a benefit in doing it all inside one box.

Another thing that is very helpful is PRACTICE!!! The more you practice, the more it sticks. Practice it until you don’t have to think to do it, that way you probably don’t even need to waste so much time preparing for interviews. Like they say “practice makes you perfect” , even if its just an hour daily it will go a long way if you’re consistent..!!!???

Practice is the best way to keep up with the skills and keep the knowledge fresh. Practice with Real Data: Projects: Start with simple projects and progress to more complex ones. Kaggle: Participate in data science competitions for real-world data experience and learning from others. If want to learn basics to advance skills with directions - Datacamp is a really good platform.

One thing you can do is to practise on platforms like HackerRank. There are case studies and scenarios for you to apply your data analytics skills. Whenever you face difficulties, search on forums for help and tips.

Practice with real-world data. You can find datasets on Kaggle, participate in challenges, or work on personal projects to hone your skills. This step is crucial for gaining practical experience.

- If studying on your own: Most MOOC courses give access to some cool datasets. Creating your own portfolio with theses projects is always a good way to start (GitHub or personal portifolios are always major points when I'm head hunting for new talents). - If already employed in a company that has it's own data team: the "blanket is always smaller than the legs", in other words, there is always some smallers routine less complex challenges that the local data team could use some extra hands with. It's always interesting to reach out and let them know you are interested in a carrer shift. Be proactive and study any documentation prior. If someone can mentor you than finding the licensed tools and dataset will be much quicker.

Understanding that you don’t know everything and being willing to learn is key to success in any profession. Seeking feedback from those more senior in your field can be incredibly valuable. While seniority doesn’t guarantee complete knowledge, the essence of collaboration and seeking guidance will undoubtedly benefit you in the long run, especially when building a network of like-minded professionals to learn from.

collaboration is key. Actively seek feedback from fellow analysts, experts, mentors, or stakeholders—they provide valuable insights, guidance, and support. Be open to giving feedback to others in the learning process. Engage in online forums, groups, or events to connect with the data community and expand your network. By embracing feedback, you not only improve your skills but also broaden your exposure and connections.

Having access to mentor is a premium luxury due to feedback being it's element. People's response on your work help you think better. LinkedIn can also be a way to gather feedbacks on your demos.

Look for feedback regarding your ability to effectively communicate your findings. Figuring out how to engage your audience is about striking the right balance between technical and business lingo. It's not easy! Appreciate and nourish every feedback you get on this regard, you'll learn a lot.

Always seek feedback from mentors or professors on your analytics projects. Engaging stakeholders is crucial in real-world data analytics projects, so make it a practice to review your analysis with an audience and gather feedback.

Once you board the data analytics train successfully by mastering the most important topics and tools, it is time to assess your progress. Start by reflecting on achievements and failures and adjust your strategy accordingly. Data analytics evolves rapidly. So, you need to be curious, explore new tools, and grab opportunities to apply your skills.

The field of data analytics is constantly evolving, so stay curious, keep exploring new tools and techniques, and never stop learning. Experiment with different tools and approaches to find what works best for you.

The importance of learning tools and developing skills is undeniable, but it's equally crucial to keep them updated and continue learning. Personally, I believe that data and technology are progressing at an unprecedented pace, making it more important than ever to stay at the top of these changes. It's essential to know the industry and the progress, not just the tools.

The field of data analytics is ever-evolving. Stay updated with the latest trends and technologies, and continuously learn new tools, methods, and packages in R and Python to keep up with industry changes.

It is essential to develop complementary skill sets, such as data engineering and cloud computing. Acquiring these skills will provide a comprehensive toolkit, enabling you to manage projects from start to finish.

As a final note, 1. Data analytics is a rapidly evolving field. Stay updated by following industry blogs, attending webinars, or participating in online communities. This ensures you're familiar with the latest trends and tools. 2. Getting up to speed takes time and practice. Be patient, celebrate your progress, and don't be afraid to make mistakes. Embrace the continuous learning process that is data analytics.

As an Analyst you will very often interact with other non technical stakeholders, so it’s important to - understand business context. - identify roadblocks. - translate metrics to actionable insights.

Learn the subject in which you are applying your skills - take short courses or diplomas that enhance your understanding. The goal is not to switch to the subject matter but to understand the basics so that you can speak the same language with experts.

As you progress in your data analytics journey, remember the importance of adapting to new challenges and opportunities that arise within the field.

Writing and sharing your insights will not only enhance your communication skills, which is equally or even more important than the analytical work itself, but also contribute to the broader community.