You're looking to break into data analytics. How can you turn your current skills into a career?

Skill Assessment: Identify transferable skills. Upskilling: Learn essential analytics tools. Networking: Connect with industry professionals. Portfolio Development: Showcase data projects.

In my experience, if you’re aiming to break into data analytics, it’s essential to leverage your existing skills and build upon them. Understand the role, develop technical skills, master data analysis techniques and practice real-world projects. Continuous learning is key together with connecting with professionals in the field.

Research about the relevant skills for a data analyst currently. Because of the dynamic nature of this industry, what was relevant 3-4 years ago, might not be the best today. So, network with the right professionals and research the skills required and from where to learn these skills as well.

?? Evaluate your skills. ??? Learn Python, SQL, Excel. ?? Showcase problem-solving through projects. ?? Pursue online courses or certifications. ?? Network and find a mentor. ?? Master SQL for databases. ?? Learn to use Tableau or Power BI!

When I had to shut down my startup a question arose - What Next? One of the transferable skills I learned in my startup was Data analysis. I was dealing with a quite huge amount of data in excel. I learned Python and tried to do the same with it. God it was easy. I landed my first job in Analytics as a trainer. Soon I became a Data Scientist for a leading Automobile company from then one thing led to another. Now I am leading a team for a Pharma major in the space of Patient Insights (Oncology) The crux of the story is " You need to find the pivot and from then on things will look better " Start small All the best!!

To learn new skills for data analysis, explore online platforms like DataCamp, Kaggle, EdX, and Medium. These resources offer interactive courses, competitions, free courses from universities, and insightful articles. Choose resources that match your learning style and budget, and consider joining online communities to connect with other learners and professionals.

Learn new skills to bridge gaps for a career in data analytics. Explore online platforms like DataCamp, Kaggle, EdX, and Medium for courses, projects, competitions, and articles. Choose resources aligning with your learning preferences and connect with communities for support and networking. Continuous learning is key to stay updated in the dynamic field of data analytics.

As a seasoned data analyst with a focus on financial analysis and MIS reporting, I've seen the transformative power of mastering new tools and languages. Learning SQL and Python can significantly enhance your data manipulation capabilities, while proficiency in Power BI or similar dashboarding tools is essential for presenting insights effectively. As AI continues to shape the industry, understanding its applications in data analysis can give you a competitive edge. Remember, the key to becoming a successful data analyst is not just learning new skills but also applying them to extract meaningful insights from clean, well-organized data.

Explore programming languages like Python and R, for data manipulation, statistical analysis, and machine learning tasks. Python's libraries such as Pandas, NumPy, and Scikit-learn offer support for data analysis and modeling. R provides a nice ecosystem of packages like dplyr, ggplot2, and caret, tailored for statistical computing and visualization. Learn/explore database query languages like SQL or NoSQL to efficiently extract, manipulate, and manage data stored in databases. Visualization tools such as Tableau, Power BI, or matplotlib/seaborn libraries in Python allow the presentation of data. Expertise in data wrangling tools like Excel, OpenRefine, or pandas in Python is invaluable for cleaning, transforming, and preparing data.

To become a data analyst, focus on learning and enhancing your skills. Explore online platforms like DataCamp, Kaggle, EdX, and Medium, offering interactive courses, competitions, tutorials, and articles on data analysis. Tailor your learning to fit your preferences, budget, and schedule. Engage with online communities to connect with peers, mentors, and professionals in the field, creating a supportive learning environment.

I'm just going to share how I did it in the Internal Audit world: I took an analytics class focused on a specific tool. In the last 20 minutes of the class, the teacher said, "By the way - you can learn to code and automate everything we did the last 3 days." I was hooked. I figured out how to code, and I applied analytics to EVERY project I touched. I found millions in cost savings (paid my dept's salaries!), automated testing that used to take weeks down to seconds, found miscalculated sales tax at some cash registers throughout the US, proved our External Auditors were doing things wrong... That was all on top of my "actual" job, but that success led to my offer to build Mastercard's Audit Analytics program!

Build a compelling portfolio to enhance your prospects as a data analyst. Choose a topic of interest or relevance, select a dataset, and define a specific question for analysis. Follow the data analysis process, creating clear, concise, and visually appealing deliverables. Utilize platforms like GitHub, Tableau Public, or Medium to showcase your portfolio online, and integrate it into your resume, cover letter, and interviews to demonstrate your skills and accomplishments.

Building a robust portfolio is essential for aspiring data analysts. By selecting topics that are aligned with your interests or career aspirations, you can demonstrate your expertise effectively. Utilize accessible datasets to address specific questions or problems, employing the data analysis process meticulously. Craft your deliverable with clarity and conciseness, integrating compelling visualizations and storytelling techniques to captivate your audience. Leverage platforms like GitHub for online sharing, and incorporate your portfolio into your professional documents and interviews. This amplifies your credibility and sets you apart in the competitive landscape of data analytics.

Work on real-time projects using Kaggle projects. Connect with folks on LinkedIn and learn about projects' implementations. Look for GitHub submissions and research projects to get more insights

Breaking into data analytics requires showcasing your skills effectively through a portfolio. Stand out by displaying your projects on platforms like LinkedIn and GitHub. Ensure your portfolio is easily accessible to recruiters. Utilize datasets from sources like Kaggle to hone your skills while tackling real-world business challenges. Consider volunteering with firms in the industry; for instance, my experience with Resagratia, a data consulting firm, proved invaluable. My work with Tobi, the CEO, not only enhanced my skills but also paved the way for seamless projects when I joined Deloitte. By actively building your portfolio and seeking hands-on experience, you can turn your current skills into a successful career in data analytics.

Breaking into data analytics requires a blend of technical prowess and critical thinking. You need to strike a balance between hard and soft skills to do that. Soft skills could include- 1. Communication skills to understand the problem and convey the solution and approach 2. Critical thinking and problem solving skills to analyse data and find innovative solution 3. Attention to details to ensure accuracy and precision Technical skills could include - 1. Starting point is excel followed by SQL. 2. Programming language like Python and R 3. Data visualization to turn data into story Once you have excelled in this then develop skills around AI\ML but have your basics clear and strong. "Data analytics is all about story telling"

You start by understanding your domain's data. Pick any dataset that is from your domain then learn any analytical tool to derive insights/trends. The barrier for entry is lowest for Microsoft Excel. Start by cleaning how to clean data, how to preprocess data and then pivot tables/visualisations for the Analytical part.

apart from the learning new skills and creating a portfolio, Networking is also an important factor to consider for beginners. Connect with professionals in the field through networking events, online communities, and social media platforms like LinkedIn. Networking can lead to job opportunities and mentorship. Another tips is to Stay updated with industry trends, new technologies, and advancements in data analytics through continuous learning and professional development. Courses, webinars, and certifications can enhance your skills and marketability.

In my experience, I’ve appreciated candidates who know about the subject domain and can apply analytics to it, as opposed to a candidate who may only have analytics expertise and have to learn the subject matter. In the former, the candidate already knows the types of business questions that are being asked and can then select the appropriate tool to answer them adding instant value. For interviewing, I’d recommend always having a showcase of work ready to demo. Seeing the work is powerful in knowing the person is qualified. Keep the subject matter light. Use current day topics where necessary to gain interest from the interview panel. Something complicated may leave too many unanswered questions and confuse the panel.

Believe it or not, there are varying types of data analysts. While transitioning from another field, it’s imperative to ascertain to which field your skills best align, this will ensure that you’re building on baseline data analytics knowledge to upskill in areas and broaden your knowledge in fields that align with your new career path.