You’re a statistician who wants to be the best. What are the tools you need to master?

As a statistician, I would say below are the key points to be master. 1. Basic understanding of fundamentals/techniques used in field of stats. 2. Statistical Software: Excellent hands-on statistical coding/programming softwares like SAS, R, Python, PASS, nQuery, Minitab, etc. 3. Hands on data visualization tools. 4. You should know Microsoft tools like Excel, Word etc. Its help a lot. Believe me. 5. Use of AI, machine learning techniques, algorithms.

1. Master EDA Skills in R/Python. 2. Develop a solid understanding of Probability Theory, Statistical Inference (Estimation & Testing of Hypothesis), Design of Experiments. 3. Develop a strong understanding of Linear Algebra, Calculus and Optimization Theory.

Let's clarify a few things before talking about technical things. First, the overall question here is: 'You’re a statistician who wants to be the best. What are the tools you need to master?" Unfortunately, that's an ambiguous question. What, exactly, is it that you want to be best at? Also, this prompt says: "Statistics is a computational discipline." It would be more accurate to say that "Statistics is an interpretive discipline that uses computing." My point is that while tools may be an important part of statistics (and I prefer R), the field is much, much broader than that. Think of tools as necessary but not sufficient conditions for good data work. You need them, but they're nowhere near enough. That said, R is good & Python is good.

Of course, the “Fab Four” of R, Python, SAS, and MATLAB are the obvious ones. But R and Python are both written in C, with some of the most important libraries in Fortran. So, if you need to do anything that’s not readily available in an existing library, and especially if you’re working with a large enough data set where performance matters … and also if you’re looking to contribute to a high-performance public library … you’ll at least need to know C. Note that there are multiple flavors of C! The “original” is just called, C. The more modern version is C++; it adds many object-oriented programming (OOP) features which are syntactically nice … but C++ tends to be slower than straight C. Microsoft also has C#, and …

I am not the statistician however I would like to add a my cent here.. As a statistician, you are carrying great responsibility to design the hypothesis of any research activities. It should be robust enough to prove it right or wrong instead of twisting the design to prove it right for commercial benefits. It applies to all industries but in my clinical research industry, it is impacts millions of life. Statistician should follow ethical principles. They're are doing great job and backbone of every published research, data and invention.. they are underrated in any industry and receive hardly any credit.

The statistical software landscape extends beyond familiar names like SPSS, Stata, Minitab, and Excel, to include powerful tools like R and Python, known for their extensive statistical libraries and open-source availability. SAS offers advanced analytics capabilities, while MATLAB shines in numerical computations. JMP integrates dynamic visualization with analytics, and EViews specializes in econometrics. For visual storytelling, Tableau provides interactive dashboards, whereas Alteryx excels in data blending and analytics. Each tool caters to different aspects of statistical analysis, making selection dependent on the specific needs, expertise level, and data complexity.

Statisticians must excel in SPSS for its simplicity, R and Python for their comprehensive statistical packages, and SQL for robust database management, enabling effective data analysis and strategic decision-making in complex research scenarios. Proficiency in these tools is essential for modern statistical practice.

My general advice is to start with the simplest tools and only move to more advanced/complicated ones as needed: 1. Start with spreadsheets, unless and until it is no longer easy to do your work there. (I use Google Sheets every single day.) 2. If necessary, move to a statistical app like jamovi (my favorite) or SPSS, unless and until it is no longer easy to do your work there. (I use jamovi for things that Google Sheets can't do.) 3. If necessary, move to a programming language like R or Python. I do my professional work, or anything else that will need to be repeated, in R. Everything else is in either Google Sheets (95% of my work) or jamovi (3% of my work), while the remaining 2% is in R.

Statistical software offers user-friendly interfaces, pre-built functions, specialized design, and support services. Drawbacks include costly licensing, limited flexibility, opaque processes, and complex automation. Programming languages provide versatility, automation capability, reproducibility, cost-effectiveness, and supportive communities. Weaknesses include a steep learning curve, time consumption, standardization challenges, and limited interactivity. The best tool depends on the specific needs of the user - statistical software is ideal for standard analyses while programming languages excel in customization and integration. Combining both tools is common practice among data scientists for quick explorations and intricate workflows.

Absolutely! Access to diverse and quality data is essential for statisticians. Utilize various sources such as public databases, academic journals, and online repositories to gather data for analysis. Websites like Statista offer extensive statistics and datasets across various fields.

To excel in statistics, one must master various data sources, including diversity, evaluation, cleaning, and data integration. Embracing alternative sources like social media, sensor data, and web scraping can uncover unique insights. Mastering data quality, identifying biases, and cleaning messy datasets is crucial. Developing skills in data integration allows for comprehensive analysis. Proficiency in programming languages like Python and R, SQL, and data visualization tools like Tableau and Power BI is essential. Familiarity with big data tools like Hadoop or Spark is also crucial. Understanding machine learning and AI can significantly improve analysis.

Here's a list of valuable data sources for statistical analysis, categorized by sector: 1. Government and Public Institutions: Data.gov, Census Bureau, World Bank Open Data, UN Data, Eurostat. 2. Research and Scientific Data: Pew Research Center, ICPSR, UCI Machine Learning Repository, Kaggle. 3. Sector-Specific Repositories: Healthdata.gov, NOAA, FRED, WHO. 4. Finding More: Google Dataset Search, Data Portals, Academic Journals. 5. Important Considerations: Quality (reputation and biases), Format (CSV, Excel, JSON), Documentation (variables and methods), Brand Voice (academic and authoritative).

Data visualization tools are indispensable for statisticians, helping them present complex data in an understandable and visually engaging manner. Tools like ggplot2 and matplotlib are excellent for creating detailed and customizable plots in programming environments. On the other hand, Tableau and Power BI offer user-friendly interfaces with powerful capabilities for interactive dashboards and business intelligence reporting.

Believe it or not, my favorite visualization tool is Google Sheets. This is because (a) it's easy to fire up, as it's just a web page; (b) it's easy to use, as it's designed for a general audience; and (c) it forces you to keep things simple. I use it for bar charts, line charts, and scatterplots, which makes up 95% of my visualization needs. Also, Google Sheets has a very clean, clear style that I like (and I have a background in design). For more complicated work, I'll use either jamovi (e.g., for boxplots and density plots) or, if I'm prepared to spend a lot of time, ggplot2 in R. (R is especially good for work that need to be replicable.)

Predicting the top data visualization tools for statisticians in 2024 poses challenges due to the dynamic landscape: Notable contenders: Tableau, Python libraries (Matplotlib, Seaborn, Plotly), R's ggplot2, and Microsoft Power BI. Up-and-comers: Flourish and Observable, alongside emerging JavaScript and Python libraries. Key considerations for 2024 tools: augmented analytics, interactivity, collaboration, and integration. To select the right tool, assess statistical rigor, user-friendliness, shareability, environment (Python or R), and specific data needs. Experimentation is vital to identify the optimal tool for workflow and analytical demands. The content maintains an academic tone focused on scholarly discourse and factual accuracy.

This is a good one. No, don't use complicated ML because it is cool. Let's say you have a small tabular data set. Most of the mentioned frameworks would be absolute overkill. Strive for the easiest tool to solve the problem.

TensorFlow simplifies deep neural network creation with a focus on high performance. PyTorch enables real-time adjustments to computational graphs, while Scikit-learn offers a variety of algorithms. Keras streamlines deep learning model tasks. These libraries blend statistics with machine learning for pattern recognition and predictive modeling.

Much more important than knowing the frameworks is knowing the math they’re implementing. For example, you can fiddle the knobs of the framework to settle upon what type of regulation to use … or you can understand the difference between Ridge and Lasso from the beginning and know which is more applicable to the problem you’re working with in the first place. In addition to being more efficient … if you expect (continuing the example) Lasso to be more suitable than Ridge but find the opposite … why? It could be a sign of your misunderstanding, but it could also be a sign of something else going on in the data. Or, Ridge could actually be better because Lasso is over-fitting. Knowing what’s actually going on is vital.

I love LinkedIn Learning. I create courses for them, but I also learn from courses for them. I have also shared my own online courses with freeCodeCamp on YouTube. And, finally, I'll point my students to Khan Academy when they need more help with basic concepts.

Explore DataCamp for interactive learning, Kaggle for datasets and micro-courses, and university courses on Coursera and edX. Enhance statistical skills through various platforms like LinkedIn Learning, open courseware, and webinars. Tailor learning objectives to your skill level, preferences, and budget.

To become the best, master the below: Measures of central tendency - Mean, Median, Mode. Measures of dispersion - Variance, SD, Interquartile Range. Most topics in Statistics boil down to above. 1. Linear regression : One models the expected value. 2. Probability distributions : Most distributions are characterized by location parameter (mean) and scale parameter(standard deviation). 4. Outlier detection: We classify something as an outlier if some data point is 2SD or 3SD. 5. Time series forecasting : A stationary time series is the one whose properties such as mean, variance and autocorrelation structure stays constant over time. 6. Hypothesis testing : Hypothesis testing of mean and difference in means. For e.g. t-test or ANOVA.

Statistics is, fundamentally, an interpretive discipline. You are trying to make sense of the mess of life for a client so they can get some actionable insights and know what they need to do next. At least as important as any technical skills is the ability to communicate well with your clients about their goals, and then communicate the actionable insights. This part is much harder than people expect, and it also needs specialized training. For every hour you spend on your technical skills, spend at least one hour on your business understanding, human understanding, and communication understanding. Those skills will make or break you as a professional statistician.

To excel as a statistician, mastering interdisciplinary skills is key. Delving into computer science, mathematics, economics, and psychology can spark innovative approaches. Engaging with cutting-edge research in deep learning, causal inference, and topological data analysis pushes boundaries. Soft skills are equally crucial. Exceptional statisticians are effective communicators, strong leaders, and strategic thinkers. Honing data storytelling, project management, and business acumen bridges the gap between technical expertise and practical impact. A growth mindset and continuous learning are essential in this evolving field.

To excel as a statistician, master essential tools such as R or Python for statistical programming. Become proficient in statistical software like RStudio, Jupyter Notebooks, or SPSS. Learn data visualization tools such as ggplot2 or Matplotlib. Develop expertise in SQL for efficient data manipulation. Familiarize yourself with machine learning libraries like scikit-learn. Excel in using version control systems like Git. Stay updated on emerging tools and technologies in the statistical and data science domains to remain at the forefront of the field. Continuous skill development is key to becoming a top statistician.

The various tools are important, yes. But all are meaningless without a solid foundation in actual statistics. Your first step in approaching any problem should generally be to work through the math. Once you’ve got the math figured out, the implementation in code will generally solve itself. Either there will be off-the-shelf tools that already do the number crunching for you, or else you’ll write it all as matrix / vector operations. Well-written code to do the latter will look vaguely like the LaTeX you’d use to typeset it, if you squint — and will be just as clear to a programmer who knows the math as the symbology is to the mathematician.