Here's how you can acquire in-demand analytical skills for a career change in the data science industry.

I may recommend that everyone know the basic meaning of data visualisations, basic mathematical concepts in statistics, linear algebra and probability. After that, you start from the easiest software for data visualisations, go further in programming language and learn unceasingly. Lucky enough, you will be an expert.

The basics of Data Science revolve around understanding probability, statistics and the data itself. There are numerous platforms like coursera, data camp, khan academy, etc for learning probability and statistics. You can also refer to Youtube channels like Zedstatistics and StatQuest with Josh Starmer

To acquire in-demand analytical skills for a career change in data science, focus on learning programming languages like Python and R, mastering data manipulation with SQL, and becoming proficient in data visualization tools like Tableau. Enhance your knowledge through online courses, certifications, and hands-on projects. Networking and joining data science communities can also provide valuable insights and opportunities for growth in this field.

Pour ma part, j’ai trouvé que comprendre des concepts tels que la moyenne, la médiane, le mode, et l’écart-type m’a énormément aidé à interpréter les résultats avec plus de précision. L’utilisation d’outils comme Excel est également une étape cruciale, car elle permet de manipuler et de visualiser les données de manière intuitive avant de passer à des techniques analytiques plus avancées. Il est essentiel de solidifier ces fondations pour pouvoir aborder des sujets plus sophistiqués comme le machine learning ou l’analyse prédictive. Je recommande également de pratiquer régulièrement ces compétences à travers des projets concrets, cela aide à mieux comprendre leur application dans le monde réel.

1. Educational Foundations - Enroll in Online Courses: Platforms like Coursera, edX, and Udacity offer specialized courses in data science, machine learning, statistics, and data analysis. 2. Technical Skills Development - Programming Languages: Learn Python and R, as they are essential for data manipulation and analysis. - Data Visualization: Learn visualization tools like Matplotlib, as well as software like Tableau and Power BI. - SQL: Master SQL for database querying and manipulation. 3. Practical Experience - Projects: Work on real-world projects. Kaggle competitions are a great way to apply your skills and solve practical problems. - Internships: Seek internships or part-time roles that provide hands-on experience with data.

Studying SQL, or Structured Query Language, opens a gateway to understanding and manipulating relational databases. With SQL proficiency, individuals can efficiently manage, retrieve, and analyze vast amounts of data, crucial in today's data-driven world. Mastering SQL empowers professionals across various industries, from software development to business analytics, by enabling them to craft complex queries, optimize database performance, and ensure data integrity. Moreover, as data continues to proliferate, the demand for SQL skills remains high, making it a valuable asset for career advancement.

SQL is a data query language. In your daily job routine, SQL helps you collect specific data from a data storage (tables). Now you may use commands in excel, tableau, power BI, or maybe sql itself to analyse this collected data. The corporates work around sql softwares as SAS , while startups move around MySQL. There are additional syntax you need to follow with these softwares while basic functions remain same. You may follow Coursera, Datacamp, HackerEarth, W3School and ListenData for learning and practicing SQL.

apprentissage de SQL a été une étape clé dans mon parcours de data scientist. Apprendre à créer et manipuler des bases de données m’a permis de comprendre comment structurer les données de manière efficace. La ma?trise des requêtes SQL m’a ensuite permis de récupérer et analyser des données de manière plus performante. Je recommande fortement l’utilisation de ressources en ligne gratuites pour apprendre SQL. Les cours interactifs sont particulièrement utiles pour se familiariser avec les concepts de manière pratique. De plus, s’entra?ner avec des ensembles de données réels est crucial pour acquérir une expérience concrète et approfondir sa compréhension.

To study SQL, start with online tutorials and courses that cover basic commands like SELECT, INSERT, UPDATE, and DELETE. Practice writing queries using sample databases available on platforms like W3Schools or SQLZoo. Read SQL textbooks for deeper understanding and real-world applications. Join online forums and communities for peer support. Consistently practice by solving SQL challenges on websites like LeetCode and HackerRank.

Dive into SQL. Understand how to create, query, and manipulate databases. Learn about SELECT statements, JOINs, and GROUP BY clauses. Practical exercises will help reinforce your skills. Explore database design principles. Learn about normalization, indexing, and schema design. A well-structured database is crucial for efficient data retrieval.

Pour ceux qui commencent, je recommande vivement de se familiariser avec les bases de la programmation en Python avant de plonger dans ces bibliothèques spécifiques. Une fois les fondamentaux ma?trisés, Pandas et NumPy offrent des fonctionnalités incroyablement robustes pour la manipulation et l’analyse des données. Les tutoriels en ligne sont d’excellentes ressources pour acquérir des compétences pratiques. En créant de petits projets, on peut appliquer les connaissances théoriques dans des contextes réels, ce qui est essentiel pour consolider ses compétences. La pratique régulière est effectivement la clé de la ma?trise de Python dans le domaine de la science des données.

To master Python using free sources, follow these steps and utilize the following resources: Start with **Codecademy** and **W3Schools** for interactive Python tutorials. Move to **Coursera** and **edX** for free courses from universities, such as the University of Michigan's Python for Everybody. Practice coding with **LeetCode**, **HackerRank**, and **CodeWars**. Enhance your skills with projects using **Kaggle** and **GitHub**. Learn from YouTube channels like **Corey Schafer** and **freeCodeCamp**. Join Python communities on **Reddit** and **Stack Overflow** for support and networking. This approach will help you gain a comprehensive understanding of Python.

Uma das coisas que se tornaram indispensáveis em python s?o as bibliotecas Pandas e NumPy. Estou aprendendo fazendo alguns vídeos no youtube, sou bem iniciante, mas pelo pouco que fiz, n?o é uma linguagem de difícil aprendizado e indico muito que se a pessoa quer se aventurar nesta área de dados estude Python.

Python is versatile and widely used. Learn libraries like Pandas for data manipulation. Practice cleaning messy data, merging datasets, and handling missing values. Matplotlib and Seaborn allow you to create informative visualizations. Master these tools to present your findings effectively. Also R was designed for statistical analysis. Explore packages like dplyr and ggplot2. Use R for hypothesis testing, regression, and exploratory data analysis. ggplot2 is powerful for creating customized plots. Learn to visualize data distributions, correlations, and trends.

Engaging in projects is crucial for practical learning. Start with small, manageable projects that align with your interests. Use platforms like GitHub to collaborate and showcase your work. Apply theoretical knowledge to solve real-world problems, and experiment with different tools and techniques. Seek feedback from peers and mentors to improve. Document your process and results to reflect on your progress and share your learning journey with others.

Data science is 10% learning from theory and 90% learning from hands-on practice. For mastering data science, you can start with open source projects available on Kaggle , collaborate with peers and look for industry standard projects like credit fraud identification, loss forecasting, score modelling, etc. This will give you an idea around real world problems as well as tailor you for being a market-fit. Focus just not only on what steps you do in project, but also, question each step - why did you go this way, what if you have used a different method, how does objective of project drives the process , etc. After project completion, try to have a mock presentation to check if your audience is able to grasp and take feedback.

J’ai effectué des projets de collecte de données, par exemple, en utilisant des API publiques pour extraire des données financières. Ensuite, j’ai nettoyé ces données en éliminant les doublons et en gérant les valeurs manquantes. Ce processus m’a aidé à développer une compréhension approfondie des outils de nettoyage de données tels que Pandas en Python. Pour analyser les données, j’ai souvent employé des techniques de visualisation avec des bibliothèques comme Matplotlib et Seaborn. Par exemple, j’ai réalisé une analyse des tendances du marché boursier en visualisant les données historiques pour identifier les modèles de comportement des actions.

Hoje existem várias ferramentas de bases gratuitas que podem ser analisadas e utilizadas para cria??o de gráficos e análises na ferramenta que você domina, além de API′s gratuitas mas que tem uma quantidade de requisi??es diárias. A Documenta??o do projeto é de suma importancia e tendo isso em mente após a conclus?o do projeto fa?a esta documenta??o, existem ferramentas free para isso hoje, eu mesmo utilizo o notion, que acho muito boa e opensource.

Apply your skills to practical problems. Work on projects that interest you. For example: Analyze a dataset (e.g., Kaggle datasets) and derive insights. Predict stock prices using time series data. Build a recommendation system. Create detailed project documentation. Explain your approach, data preprocessing steps, and model selection. Showcase your problem-solving abilities.

Acredito que o networking é uma ferramenta poderosa para quem busca crescer e se destacar no campo de ciência de dados e análise de dados. Ele pode fornecer insights valiosos sobre as tendências do setor a auda a identificar diversas habilidades analíticas que tem maior demanda. Participar de fóruns, conferências, workshops, além de oferecer oportunidades para aprender, também pode criar conex?es e até parcerias. Hoje o próprio Linkedin é uma ferramenta poderosa para isso.

J’ai vu l’importance cruciale du réseautage pour se tenir informé des tendances du secteur et des compétences analytiques demandées. J’ai réalisé que participer à des conférences et ateliers en science des données est essentiel pour rester à jour. J’ai effectué des participations à des événements comme le “Data Science Summit”, où j’ai appris des nouvelles méthodes de deep learning. J’ai aussi rejoint des forums en ligne comme Reddit et Kaggle, ce qui m’a permis d’échanger avec d’autres professionnels et d’identifier les compétences recherchées, telles que le machine learning et le cloud computing.

Join data science forums, such as Stack Overflow, Reddit, or specialized platforms like Data Science Central. Engage in discussions, ask questions, and share your knowledge. Connect with professionals in the field. Attend webinars, conferences, and virtual meetups. Networking can lead to job referrals and collaborations.

Pour approfondir mes compétences, j’ai suivi des cours en ligne sur des plateformes comme Coursera et edX. J’ai notamment complété des cours sur l’apprentissage automatique et les réseaux de neurones, dispensés par des experts comme Andrew Ng. Ces cours m’ont offert une solide base théorique et des exercices pratiques pour appliquer les concepts appris. En outre, j’ai exploré les dernières technologies et outils en participant à des webinaires et des ateliers en ligne. Par exemple, un webinaire sur l’utilisation de TensorFlow m’a aidé à mieux comprendre cette bibliothèque et à l’utiliser efficacement dans mes projets de deep learning. Envisager des certifications ou des dipl?mes avancés a également été une stratégie que j’ai adoptée.

I couldn't agree less, because in programming specially new packages tend to make coding a bit easier than the previous one's, so if you get stocked on an Old package or later version then you tend to not understand how easy life could get by using the latest packages and updated methods. so it is essential to keep yourself informed with the latest the developments in the field.

As you grow on experience, you will find yourself detached from the hands-on practice or just doing the repetitive steps in routine like coding in python, data pulling from sql, calculations in excel, etc. But remember, in actual world, the end goal is to keep the efficiency and accuracy high. So, your everyday focus should shift towards reducing the number of steps you take, in repetitive processes. Or to say in simpler words, "Automate whatever you have to replicate". You may not always get the best tech exposure, maximum time for your projects or maybe the perfect teammates, and so all you need to do, is "optimise" and strike a balance with your resources and results.

C’est très utiles de pratiquer apprendre on appliquant et partager et demander de l’aide Plain de recourse open source pour s’améliorer et pratiquer

Embrace AI and Machine Learning If you are a beginner considering a career in this field, my answer is a resounding yes. Concerns about AI replacing human expertise are misplaced. AI enhances efficiency and improves outputs, not replacing humans. Understanding AI and machine learning is crucial today. Familiarize yourself with frameworks like TensorFlow or PyTorch. AI enables predictions, automates processes, and uncovers insights. AI is about augmenting human capabilities, leading to productivity and innovation. Embrace AI to enhance your skills and adaptability to future advancements.