What do you do if you want to master statistical analysis as a data scientist?

Las estadísticas descriptivas son importantes pero siempre hay que empezar con el ejercicio de como estas se alinean con un problema real. Descomponer el problema en sus partes, generar las preguntas de investigación (que en la práctica del científico de datos muchas veces tienen que estar alineadas con los objetivos del negocio). Y ya con estos primeros pasos empezar a estudiar las estadísticas descriptivas. Es importante siempre verlas dentro del contexto de problemas reales para mejorar su entendimiento.

To master statistical analysis as a data scientist, you would want to take a comprehensive structured approach that balances theoretical understanding as well as practical application. Practical experience plays a critical role in mastering statistical analysis. Engage in projects that allow you to apply statistical techniques on real-world datasets, starting with simple tasks and gradually moving to more complex challenges. And each time you faces with new projects you can go and learn more advance topics.

First thing to remember is that learning statistical analysis is a process which doesn't have any end. 1. Learning basics: Starting from the central tendency, dispersion to estimation and testing - all come under basics. One needs to learn these concepts first with sufficient examples to proceed further. 2. Try to apply these concepts to as many real world datasets as possible using any programming language. Common data sources are UCI ML Repo, Kaggle etc. 3. Learn visualisation tools like Seaborn or Plotly to visualize your analyses - this will make things clearer. 4. Study advanced statistical concepts like multivariate analysis, time series, categorical data analysis etc. 5. Statistics come with several paradoxes - learn them

Mastering statistical analysis as a data scientist involves a combination of theoretical understanding, practical application, and ongoing learning. Here are some steps you can take to achieve mastery in this field: 1. Build a strong foundation 2. Practice with real-world datasets 3. Learn statistical programming 4. Explore advanced topics 5. Stay updated 6. Work on projects 7. Seek feedback and mentorship 8. Teach and share knowledge By following these steps and dedicating time and effort to continuous learning and practice, you can become proficient in statistical analysis as a data scientist and eventually achieve mastery in the field.

This is your foundation! Ensure you have a solid grasp of descriptive statistics (measures of central tendency, dispersion, etc.) and inferential statistics (hypothesis testing, confidence intervals, etc.). Many online resources and textbooks can help you build this foundation.

Más que entender como codificar, es más importante entender en qué tipo problemas, que requerimiento deben tener los datos y lo supuesos de las pruebas estadísticas. La complejidad de programar o comprender una prueba no implica nada sobre si es la prueba es más óptima para determinado problema.

Statistics and coding go hand in hand! Learn a programming language commonly used for data science, like Python or R. This allows you to manipulate data, perform statistical analyses, and visualize your findings. There are many online tutorials and coding exercises available to get you started.

To excel in statistical analysis, it's crucial to develop proficiency in programming languages like R or Python, as statistical analysis often involves coding. Practice coding by writing scripts to perform statistical tests—begin with simpler tests like t-tests and chi-squared tests, then advance to more complex analyses such as ANOVA or regression models. Utilize libraries like pandas for data manipulation and seaborn for data visualization to bolster your analysis. Regular coding practice will deepen your understanding of statistical concepts and enhance your problem-solving abilities.

To truly master statistical analysis as a data scientist, it's essential to develop a profound understanding of the underlying mathematics. Delve into subjects such as linear algebra, calculus, and mathematical statistics. This knowledge empowers you to grasp the inner workings of algorithms and tailor them to address specific problems effectively. For example, familiarity with matrices is invaluable when handling large datasets. While you don't need to become a mathematician, a strong grasp of these areas will greatly enhance your analytical prowess.

While the focus is on applied statistics, a basic understanding of the underlying mathematical concepts can be helpful. This can involve brushing up on your probability theory, linear algebra, and calculus. Many online courses and textbooks can explain these concepts in a data science context.

If you want to master statistical analysis as a data scientist, "3. Understand Math" is a crucial step. Understanding the underlying mathematical principles is essential for proficiency in statistical analysis. ??

To truly master statistical analysis as a data scientist, theory alone isn't enough—you must apply your knowledge to real-world datasets. Participate in projects or competitions where you can practice cleaning, exploring, and interpreting data. This hands-on experience will teach you how to deal with imperfections in data and extract actionable insights. It will also expose you to various data types and statistical challenges, preparing you for the diverse scenarios you'll encounter in your career. By working with real data, you'll solidify your understanding and become a more effective data scientist.

Don't just learn in theory! Put your statistical skills to the test with real-world data. Participate in online data science challenges (Kaggle is a great platform) or find datasets relevant to your interests and analyze them. This hands-on experience is crucial for solidifying your understanding.

Enroll in advanced classes or further your education in statistics, data science, or similar subjects to better your statistical analysis. Investigate subjects including multivariate analysis, time series analysis, Bayesian statistics, and experimental design. Follow blogs, podcasts, and academic publications to learn about new approaches, algorithms, and applications. Develop critical thinking abilities so you can evaluate statistical approaches, understand data, and form meaningful conclusions. Seek mentoring and collaboration from experienced statisticians or data scientists.

At DBS Bank, I faced a challenge when required to use the R programming language, which I was previously unfamiliar with at that time. I had primarily worked with Python and Java, so learning R quickly became essential to contribute effectively to our data pipeline projects. I embarked on a self-directed learning journey, leveraging online resources, tutorials, and practice to acquire the necessary skills rapidly. This effort allowed me to integrate smoothly into the team, contribute to our projects, and underscored the importance of adaptability and continuous skill development in a fast-paced professional environment. Despite not knowing the language earlier, I quickly learned R and contributed significantly to my team.

To master statistical analysis as a data scientist, focus on advanced courses in methods like hypothesis testing and regression. Enhance programming skills in R or Python, work on practical projects with real data, and use software like SPSS or SAS. Dive into machine learning techniques, stay updated through research, collaborate with experts, and practice communicating results effectively.

Seeking feedback is a crucial aspect of mastering statistical analysis as a data scientist. Share your analyses with colleagues, other fellow data scientists, or mentors to gain fresh perspective and catch possible errors or oversights. If you're using software such as Python, participate in code reviews to improve your coding skills and ensure analysis robustness. Stay open to learning from others and be receptive to feedback, use this as an opportunity to learn and improve your statistical analysis skills.