You're a Business Intelligence Analyst who wants to transition into data science. How can you get started?

Switching from BI to Data Science means checking my skills. Good at SQL for data, and can visualize with Power BI or other tools. Statistics are okay, need to brush up. Know a bit of Python or R. I'm a problem solver and know my industries.

As mentioned above, the transition has some prerequisites involving tools and expanding your knowledge base. Different tools like R, Python, advanced SQL etc are a given but what most people fail to consider is the principles behind data science. Statistical knowledge is a must before you run off learning new skills. Fundamentally, data science requires manipulation of raw data based of off these principles. Secondly, it is important to learn data base structures, the principles behind querying data and understanding why data cleaning is so important along with how to go about it. If your understanding of the basic principles is sound, the rest is just practice.

First step is to understand that transitioning from BI to Data science is not like turning ON a switch, but more like tuning a control knob. Build upon the foundations of SQL, Visualizations, and descriptive statistics by adding measures of dispersions, distributions, probability and combinatorics, and basic python to your toolkit. Practice practice practice. Then venture into inferential statistics which involves hypothesis testing and experimentation. With this skillset, one can make a career as product data scientist, or experimentation data scientist. If you want to venture further then get into advanced python, predictive analytics/modeling by mastering basic ML algorithms, and if feeling adventurous then deep learning algos.

L'auto-évaluation est très importante car elle permet de savoir où commencer l'apprentissage. Le point de départ peut être un renforcement en notions mathématiques ou informatiques notamment en algorithmique.

- Transitioning to data science leverages BI expertise and requires upskilling in machine learning and data engineering. - Proficiency in data analysis and BI tools like Excel, R, Power BI, and Tableau forms a strong foundation. - Identify gaps by comparing current skill set to job requirements, particularly in advanced statistical methods and Python programming. - Tailored learning plan should focus on addressing these gaps while building on strengths in data storytelling and business analysis. - Smooth transition into data science roles is ensured by balancing skill development in required areas and leveraging existing expertise.

There are special search options for datasets and many free public datasets can be found there. Participating in hackathons is another good way to improve skills and learn from others.

In my opinion, transitioning from BI to data science requires a strategic approach. Here are some steps to get started: - **Master programming languages:** Learn Python or R, as they are widely used in data science for tasks like data manipulation, analysis, and visualization. - **Take online courses:** Enroll in online courses or bootcamps focused on data science fundamentals, machine learning, and statistical analysis. - **Build a portfolio:** Work on personal projects or participate in Kaggle competitions to showcase your skills and practical knowledge. - **Network:** Connect with data science professionals, join online communities, and attend meetups or conferences to learn from others and stay updated on industry trends.

To transition from being a Business Intelligence (BI) analyst to a Data Scientist, one needs to start by building proficiency in programming languages like Python or R, which are fundamental in data science. Next, delve into mastering statistical analysis, machine learning algorithms, and data visualization techniques. Enrolling in courses, attending workshops, and undertaking practical projects will help solidify your understanding and gain hands-on experience. Practice is the key - only acquiring knowledge does not help.

Both R and Python have their own pros and cons. But, if you are starting now, I would highly recommend Python over R. The reason being, data volume is growing everyday and in case of parallel processing of data we can use pyspark which is an added advantage. Also, Python is a programming language where R is more of a statistical programming language than a pure programming language.

BI analysts can leverage their data experience to become data scientists. Focus on learning Python, R, statistics, and machine learning. Utilize online courses, books, and tutorials. Your existing data wrangling and communication skills are valuable assets. Build a portfolio with personal projects and open-source contributions. Network with data scientists for guidance and industry exposure.

Understanding the data science process is essential for effective work with data. This involves five key steps: identify the problem, collect and clean data, explore and analyze it, build and assess a model, and communicate the results.

- Mastering the data science process is essential for transitioning from BI to data science. - Skills in problem definition and result communication from BI are transferable to data science. - Deepen understanding of data collection, cleaning, exploration, and model building stages. - Frameworks like CRISP-DM offer structured approaches to these tasks. - Apply processes to real-world projects to enhance portfolio and showcase ability to derive actionable insights for data-driven organizations.

-Start by learning the fundamentals of data science, including statistics, machine learning, programming languages like Python and R, and data visualization techniques. -Practice Coding: Practice coding in Python and R to analyze data, build models, and create visualizations. Hands-on experience is crucial in mastering data science techniques. -Work on Projects: Start working on data science projects to apply your knowledge and skills. -Engage with data science communities, forums, and meetups to network with professionals in the field, share knowledge. -Find a mentor who is experienced in data science to guide you through the learning process, provide feedback on your work, and offer career advice.

Les projets en analyse de données suivent un processus bien établi. Le conna?tre et le ma?triser est très important dans la résolution des problèmes posés.

Le portofolio est un outil très puissant. La structuration des projets en analyse de données se base sur l'expérience acquise dans la conception de ce document.

La science des données est domaine très vaste où l'apprentissage continue est important. La constante évolution des outils et celui du marché du travail placent le réseautage à une place importante dans la construction d'une carrière

Three things I came to realize to effectively perform the work of data analytics Spend time understanding the data. First, gain a complete understanding and reach out to people who know about the data to clear all doubts. Reach out to them as many times as necessary until you have a clear understanding. Be clear about the value you are going to provide and the client's expectations. Once you understand this, 80% of our analyst problems are solved. Understand the project lifecycle you are working on. In any project, our team will be working on multiple processes and tools. So, first, understand the complete workflow, and then understand all the tools and their use cases in the project. This will avoid most of the unnecessary confusion.

In this internet era, we are lucky enough to have great mentors online for free. Let me explain how I utilized them in my career: I have completed a postgraduate program in data science, but honestly, I've learned more from these social media influencers than from my postgraduate program, simply by going through their blogs, and LinkedIn posts. Don't waste these valuable free resources, especially linked is a great resource to learn any skill, make use of it to grow. At the end of the day, how we use the internet matters.

I would say stick to the basics. Understand what data science is! First, strengthen your foundation in statistics and mathematics—essential for analyzing and interpreting data scientifically. Grasp concepts like probability, regression, and statistical testing. These skills will help you understand algorithms and perform more complex data analysis, which are central to data science. Familiarize yourself with programming languages like Python or R, frequently used in data science. Finally, practice by working on data-driven projects to apply these new skills in real-world scenarios.