You're interested in data analysis. How do you develop the most important statistics skills for your career?

To ensure your statistical analysis is focused and impactful, start by clearly defining your objective, whether it's solving a problem, answering a research question, or improving a process. Understand the context of your data, including industry trends and external factors. Identify the necessary data and choose appropriate metrics or key performance indicators (KPIs) to measure success. Select statistical tools and methods that align with your objective and data characteristics. Plan your analysis steps carefully, execute while being open to iteration, and evaluate your results against the predefined metrics. Act on the insights gained, communicate findings clearly to stakeholders, and reflect on the process to learn.

To build up essential statistics skills to prepare for a data analysis career, begin with the identification of the areas of focus. Cover the important terms of statistics like probability, distributions, hypothesis tests etc. Select tools and programming languages that will help you reach your goals and these include R, Python, or MS Excel. Get hands-on experience by working on projects, internships or events to obtain practical experience. Never stop learning as the advances made in statistical methods and tools must be followed. Moreover, one can consider strengthening such areas as data visualization, data communication skills, as well as domain knowledge as valuable directions to broaden one’s expertise.

Identify goal First, and foremost thing to do is to understand the fundamentals. Whether the problem you analyse is completely measurable. If not first find a way to measure it or find a close proxy for it. Based on the problem in hand define goals keeping the immeasurable components in mind.

To learn the basics of statistics, start with understanding descriptive statistics, including measures of central tendency (mean, median, mode) and variability (range, variance, standard deviation), along with basic data visualization techniques. Then, progress to inferential statistics, focusing on probability, hypothesis testing, and interpreting confidence intervals and p-values. Utilize online courses, textbooks, and tutorials for structured learning, and practice applying concepts on real or simulated data sets using statistical software or programming languages like R or Python. Join online forums or study groups for community support, and work on projects to apply what you've learned.

As a start, you should learn the basic concepts of statistics and what does it mean in the world of math and statistics such as descriptive statistics like mean, median, variance, covariance, standard deviation, and range, how to visualize data by creating graphs, charts and how to apply the statistical tests, you can learn from books, or online courses or university class

As habilidades essenciais que devem ser desenvolvidas são: medidas de posição e dispersão (pratique muita estatística descritiva utilizando essas medidas). Depois que os conceitos iniciais estiverem claros, avance para inferência, testes de hipótese e intervalo de confiança. (Existe muito material gratuito sobre isso e dominar esses conceitos o tornará um profissional com muito mais capacidade de explorar os dados e fazer questionamentos). Se não tiver familiaridade com lógica de programação (Python ou R), sugiro utilizar o Excel, que é uma ferramenta mais amigável e tem recursos suficientes para aprender os conceitos de estatística que mencionei acima.

Learn data cleaning. Work with the messiest data you can find. You will be at a severe disadvantage if you jump straight to analysis and never learn to clean and manipulate your data. In all likelihood, more than 80% of your time will be spent on cleaning.

In this stage it is important to identify the appropriate analysis plan before venturing into statistics. The plan that is based on the mentioned goal will guide the type of statistical analysis you perform. If you are studying association then select appropriate techniques such as Chi Square. If it is about difference in two groups in terms of a specific outcome may go for Mean difference and so on

Are you interested in harnessing the power of R, but coding intimidates you? Jamovi is an open source statistical package based on R, that can be used for advanced analysis, while having a user friendly interface. Jamovi supports summary statistics, regression modeling and hypothesis testing. Furthermore, Jamovi includes syntax mode, where the underlying R code is displayed, hence making it a powerful tool for statistical analysis!

In the realm of statistical data analysis, selecting the right software is critical. R is a statistical powerhouse, crafted specifically for statistical analysis. It offers an extensive library of statistical tests, making it ideal for specialized analytical tasks. Python, while a general-purpose language, brings robust libraries such as pandas and scikit-learn to the table, which are indispensable for data manipulation and machine learning tasks. Both languages have strong community support and are open-source. The decision between R and Python should align with the complexity of your data, the nature of the analysis, and your proficiency in programming.

Choosing the right data analysis software involves assessing your specific needs, such as the complexity of the analysis and the size of data sets, along with your own skill level and familiarity with coding. Consider the software's ease of use, functionality, and extensibility to ensure it supports the statistical tests and data visualization capabilities you require. Budget is also a key factor, with options ranging from free tools like R and Python to more expensive solutions like SAS and SPSS. Collaboration features, compatibility with team members' tools, and the ability to export data in various formats are important for teamwork and sharing results. The availability of strong community support and comprehensive documentation.

Choosing the statistical software often depends on your specific needs, preferences and the type of analysis you're conducting, some are open-source, and free to use like R or Python while others like STATA or SPSS are expensive, but all will provide the statistical methods, and data visualization you need to provide

After deciding the statistics, select appropriate software. Many basic analysis are possible with spread sheets. Some need sophisticated software. Then think of identifying a software depending on scale and affordability. One may self learn software online.

Application is the best way to learn and improve understanding of anything! You can get freely available government data, and freely available analysis and see if you can replicate the results. If that is too far-fetched for your current skill level, you can always try out websites like Kaggle and Git Hub for data and apply your analysis. You can also find peer solutions and is a great way to find like-minded people with similar goals.

It is important to increase your skills in statistics by engaging in projects and real data and datasets, it can be downloaded from websites, also apply the data on statistical programs based on research goals so you will have various knowledge of different uses of software

The ultimate thing is interpretation of the analysis results. This in a context of a theory. One has to be clear before engage oneself in mechanically interpreting results. There comes the importance of domain knowledge. A health or social scientist will best interpret the data on their respective domain.

To continuously learn and stay updated in the field of statistics and data analysis, engage with a variety of educational materials like books, scholarly articles, blogs, and podcasts that cover new developments and practical tips. Participate in online courses and workshops offered by platforms such as Coursera, edX, or Udacity to delve into advanced topics. Leverage online forums, GitHub, and Kaggle to engage with real-world projects and challenges. Attend conferences and seminars for insights into the latest research and to network with professionals. Seek mentorship for personalized guidance and regularly apply your skills through personal or collaborative projects to reinforce learning. Stay curious and review.

In my experience, if someone wants to evolve in the field of statistics and machine learning should be a life-long learner. Do not be afraid to learn new things and always be curious. Personally, I am more of a book guy rather than taking online courses, but these two are complementaty. So, my tier list for learning is: 1. Books (highly detailed information, but time-consuming). 2. Research reviews (helpful for someone who wants to have an overview of the recent advances of a field). 3. Research papers (provide details for a specific part of the field). 4. Courses. 5. Blogs.

even if you hold degrees in statistics, not learning and working on projects is useless, so continuing to learn statistics involves a combination of theoretical understanding, practical application, and continuous practice like taking online courses and workshops, reading books will help also to keep learning and the most important is to practice with real-data or engaging in practical statistical projects that will include analyzing surveys, experiments or observational studies using statistical software. Also taking advanced courses like multivariate analysis or machine learning and teaching others like students the statistical concepts.

Learning Statistics with Jamovi is an excellent book that will help you get acquainted with various topics, including descriptive statistics, data visualization, regression modelling, hypothesis testing and more. The book is based on Jamovi, a statistical package utilizing the R language, that can be used for advanced analysis, while having a user friendly interface. You can freely download the book as a PDF document, with a website version also being available.

In terms of Statistics knowledge (putting aside other DS must-have abilities) the most useful is descriptive statistics followed by data visualization techniques. But the real game changer is inference. In order to fully understand it, you need (among other things): calculus (the very notion of expected value depends on integration), linear algebra (ML algorithms are essentially matrix multiplications), combinatorics (embedded in many probability calculations) and set theory / measure theory (the general concept of probability relies on sets relations and measure definitions in given spaces). You can think of it as a hard study load not worth the effort or a lifelong journey of learning about how to manage randomness.

In the professional world, statistics are a tool, not an end in themselves. It's crucial to recognize the plethora of statistical approaches and wisely choose the one suited to your field. Understanding specific industry needs ensures optimal tool utilization. Collaboration with domain experts helps tailor statistical analyses for informed decision-making, maximizing their impact.