Here's how you can kickstart your Statistics career without a graduate degree.

I would like to start with the basics of statistics in an interactive way to understand the importance and applications of statistics. The basics should include topics such as descriptive statistics, probability, distributions, hypothesis testing, and regression analysis. I recommend reading "𝐓𝐡𝐞 𝐂𝐚𝐫𝐭𝐨𝐨𝐧 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬" which provides real-life examples in an engaging manner. After gaining a foundational understanding, you can begin implementing statistics using R or Python. For this, I recommend the "Statistics with Python" courses from DataCamp, which are particularly helpful for data analysts.

Start with inferential statistics, move into hypothesis testing, understand the various distribution and their role and contribution, central limit therom is a must. That should set you up for understanding the bases in ML.

En el ámbito profesional en relación a la investigación, la estadística es fundamental, ya que permite tomar decisiones más precisas y entender el porqué de las dinámicas bajo estudio; por otra parte, la estadística cada vez más está tomando una gran relevancia en la pubicación de artículos científicos de alto impacto, siendo uno de los filtros más fuertes para la recepción y publicación de los mismos.

If you love math, statistics is always open for you. The base is the foundation. Start with the basics like probability. Remember, Statistics is much more than mean, median, mode. Next learn about, regression, hypothesis testing, linear algebra. Your motive is to clear the concept. You have many online resources and ebooks. Pick those, and start.

There are a lot of jobs that use statistics that do not require a graduate degree (although without at least a Master's degree, it is harder to get a job with the title of Statistician). Some of the more common job titles include the following: Statistical Programmer, Data Analyst, Market Research Analyst, and Research Analyst. One common trend is that all of these jobs have some element of statistical programming. It does not necessarily matter what language you start out with (Python, R, SAS, etc.). What matters much more is that you develop a strong foundation of statistical knowledge and programming acumen.

Du fais des bases vient maintenant la pratique à un niveau un peu plus élevé, il se pose un problème d'application à plus grande échelle, de là un logiciel est nécessaire, choisir le logiciel le plus adapté en fonction du type de données et de là taille des données qu'on souhaite analyser. Il y'a plusieurs logiciels, à disposition allant de Excel, Power Bi, jusqu'à Tableau chacun avec ses spécificités et son environnement de travail, on peux d'autre part s'intéresser à l'aspect programmation afin de nous même rendre fonctionnel et opérationnel des mini logiciels conçus pour résoudre une analyse particulière de données. Ainsi des language comme le R, le Python et MatLab permettent l'implémentation des notions de statistiques nécessaires.

Concepts and theories are important, but statistics is incomplete without computer science. You can't neglect coding. But the good thing is, for a non tech student as well, learning Python and R will be easy. Python's libraries like NumPy, Pandas, Matplotlib, Seaborn, all are important if you want to make a career in Data Science. Rely on these two languages. There are plenty of good resources. Choose them, and start.

Não podemos esquecer que o bom e velho Excel sempre será muito útil. Seja para fazer uma pré-analise, ou para sumarizar os dados e reportar para outras áreas, o domínio dos softwares de planilha é essencial.

General advice would be to concentrate your efforts on Python + foundational knowledge of statistics, rather than R. Choose R only if you have verified that in your particular target domain it is a main requirement for the job.

Knowledge is of NO use WITHOUT execution and practical experience. Once you get confident in your skills, look for internships and some good projects. Hands-on experience is a must. It'll make your concepts crystal clear.

Existem milhares de datasets públicos que podem servir como base para projetos de análise estatística para melhorar seu aprendizado e te ajudar a criar um portfólio. Nesses projetos, é necessário sempre aplicar uma metodologia clara de análise, orientando as descobertas ao contexto dos dados analisados.

The saying "experience is the best teacher" holds true. Hands-on practice is indispensable, and learning from mistakes is vital. Novice statisticians are bound to make numerous errors. Accelerating your knowledge acquisition while avoiding critical mistakes that could affect production systems is crucial. Engaging in part-time or freelance work can alleviate some pressure compared to a full-time job. Additionally, taking courses and attending workshops can provide valuable experience.

Build your portifolio to make your theorical understanding more solid. By doing that, you will not just review and apply the theorical concepts, but also bring life to real world projects and showing to the world your problem solving skills.

L'expérience est une chose immuable pour la progression personnel, elle est représente de façon plus précise les acquis obtenus des problème ou des projets pratiques auquel l'on a pris part. L'expérience est à la fois personnel et professionnel, s'entraîner régulièrement pour consolider ses compétences, avoir la chance de mettre en pratique ses compétences pour résoudre des problème d'analyse simple ou complexe pour des projets simples ou complexe afin de faire de ses compétences des acquis preuve de la maîtrise des concepts et outils dont on prétend avoir connaissance. Pour un analyste digne de ce nom, l'expérience est le moyen nécessaire d'avoir des acquis en fonction des différents projets d'analyse de données auquel il se confrontera.

If you lack background knowledge, don't neglect networking. Approach it diligently. Identify leaders in the field and learn from them. Whenever possible, engage with these experts in person. Attending relevant conferences can significantly enhance your networking opportunities. Additionally, creating and contributing to projects can help you gain recognition. For instance, as I am the creator of RUP and DA-HOC machine learning algorithms, it often serves as a conversation starter for those interested in discussing these AI algorithms with me.

You probably have networking experience, even if you don't think of it in those terms. Reaching out to your existing coworkers is a great way to start networking skills. Networking can help you connect with professionals in the field. Attend conferences, seminars, and workshops.

J'ai constaté que se faire des amis, parler de sa vision, toujours aller vers les autres représentes un moyen plus sûr de se diriger plus vers l'idéal qu'on souhaite atteindre. Aussi il est donc cruciale de se faire un réseau idéal, adapté, et qui nous permettra de mieux réaliser nos objectifs. Choisir avec beaucoup d'intérêt les gens à qui l'on accorde notre confiance, de plus être capable de savoir saisir des opportunités, comme des conférences, des salons de travail ou d'exposition, utilisation sage et intelligente des outils de la tech. En somme le réseautage est la clé qui nous permettra de décrocher de meilleures opportunités nécessaires pour exprimer et faire valoir nos compétences pour l'analyse des données.

O que chamamos de Análise e Ciências de Dados são áreas extremamente relacionadas a estatística. Entender melhor sobre algoritmos computacionais, modelos e ferramentas, além do conhecimento estatístico, pode te auxiliar a conseguir uma posição nessas áreas.

Clairement il y'a bien de domaines de prédilection qui nécessite l'analyse de données, donc il est important de s'orienter tout d'abord vers celui qui nous correspond le mieux mais qui saurait sur le long terme donner raison à votre choix. Des spécialisations comme la biostatistique, les statistiques environnementales ou l’économétrie sont en effet des exemples particulier de domaines. Le domaine des finances et des travaux publics sont aussi des secteurs où la digitalisation est de plus en plus en ascension fulgurante, nécessitant des compétences solides en analyse et traitement de données. Choisir de façon intelligente son domaine de prédominance est aussi une plus qui vient parfaire le parcours d'un analyse de données débutant.

A statistician is fundamentally a researcher, and the essence of research is experimentation. Continuous learning and improvement are crucial. Adopting a growth mindset ensures ongoing development. What we understand today may become obsolete by next year, making it essential to stay updated with the latest technology and algorithms. With the rapid advancements in artificial intelligence and machine learning, the significance of statistics has become even more pronounced.

Expand your skills and knowledge by continuous learning. Reinforce what you have previously learned and develop new knowledge. Observe more experienced people, take online courses and practice the use of your skills. Attend webinars and workshops.

Perform pre analysis Monte Carlo simulations of different sampling configurations ( more facilities, or more subjects ) to find most powerful study design.