Here's how you can master the most valuable skills for a data analyst in Information Systems.

Pour moi, tout commence par une compréhension solide de la gestion des données. Être capable de travailler avec différents types de données et les structurer correctement est essentiel pour tout analyste. L'apprentissage du SQL a été une des clés de ma réussite, car il me permet de manipuler les données efficacement. Plus je maîtrise le nettoyage et la transformation des données, plus mes analyses sont précises.

Data Proficiency: Strong understanding of data structures and management. Statistical Analysis: Ability to apply statistical methods to data for insights. Business Acumen: Understanding of industry-specific trends and needs. Data Visualization: Skill in presenting data clearly using visual tools. Programming Skills: Knowledge of languages like Python, SQL, or R for data manipulation. Continuous Learning: Ongoing self-improvement and adaptation to new tools and technologies. CORE SKILLS:EXCEL+SQL+POWER BI OPTIONAL SKILLS:R | PYTHON|SPSS

Mastering data proficiency is key to becoming a successful data analyst or even engineer. Understanding data types (binary, float, decimal etc) and tools (programming languages, shell script, relational databases) will ensure your are able to tackle any data problem with efficiency and reliability.

To excel as a data analyst in Information Systems, you need a solid foundation in data proficiency. Become skilled in handling diverse data types, using SQL to manipulate and retrieve data, and mastering data cleaning techniques to ensure accuracy. By transforming raw data into actionable insights, you'll unlock the true value of complex datasets.

For me, data analysis is like a game of continuous learning; each level excites me, and I’m determined to advance. I encourage curiosity and a playful approach to learning, similar to how children learn. Start by understanding the characteristics and distributions of data, their effects, and potential implications. Then, get familiar with various tools to determine which works best for you. Finally, focus on presenting your findings effectively. Take it one step at a time, and you’ll gradually master the most valuable skills needed as a data analyst in Information Systems.

Concordo plenamente, tanto que já estou procurando me atualizar com as novas ferramentas de trabalho com dados, eu já domino o SQL, poré hoje em dia precisa de mais por isso ja estou me especializando em Python, R, Machine Learning, Power Bi, Ciência de dados e por aqui vai.

Much of the work an Analyst does is pretty basic statistics. Taking some basic training into an introduction to statistics will enable you to provide statistical significance to most real world problems. For an introduction to statistics from a non maths background, I'd recommend Charles Wheelan Naked Statistics", which discusses statistics applying to every day real world scenarios.

Comprendre les concepts statistiques m'a permis de donner un sens aux données. Utiliser des outils comme Excel ou R pour les calculs statistiques m'aide à dégager des tendances et à justifier les décisions basées sur des faits. Ce n'est pas seulement une question de chiffres, c'est une façon d'écouter ce que les données ont à dire.

Sim a Ciência de dados que utiliza ferramentas estatísticas e de probabilidade e criação de machine Learning utilizando o Python. Curso que acabei de concluir de Ciência de dados.

Statistical analysis is key to interpreting data in Information Systems. Master statistical methods like probability distributions, hypothesis testing, and regression analysis, using tools that handle large datasets. Proficiency in these techniques helps you identify trends, make predictions, and drive decisions with quantitative evidence. It’s about understanding the story behind the numbers.

By mastering methods like probability, hypothesis testing, and regression, and using statistical software like SPSS, you can analyze large datasets to identify trends, make predictions, and make informed decisions based on quantitative evidence. Doing this is excellent practice to sharpen your skill and gather experiences

This point certainly is the most fundamental one. Knowing the business, the data behavior, the processes and how decisions are made will differ you from most data analysts. A professional who understands well the context may extract more value with an Excel workbook then the others with a full set of Python libs.

Le jour où j'ai vraiment compris l'importance du contexte commercial, mes analyses ont pris une toute autre dimension. En apprenant comment les différents départements fonctionnent, j'ai pu aligner mes analyses sur les objectifs stratégiques de l'entreprise. Cela m'a permis de rendre mes résultats compréhensibles et utiles aux équipes non techniques.

Understanding the business context distinguishes a proficient data analyst. Learn how different departments operate and how data analytics can support their goals. Familiarize yourself with key performance indicators (KPIs) across business operations to align your analysis with strategic objectives. Strong business acumen enables you to communicate complex insights effectively to non-technical stakeholders, making your findings more actionable and valuable.

Concordo totalmente, um alalista de dados precisa conhecer e se aprofundar no conhecimento dos negócios do mercado. Por isso que fiz 2 MBA de estratégia em negócios, o qual me aprimorei para estar apto para ser um analista de dados atualizado.

Don't stick to one tool for visualization, try to use different and newer methods to combine statistical analysis with visualization. Be careful in choosing colors and harmony to display what is going to be bold and what is placed in a category. The order of graphs is important in telling your descriptive story. We have two types of data storytelling: from the whole to the part or from the part to the whole

Pour moi, la visualisation est la clé pour transformer des données complexes en actions concrètes. Apprendre à créer des visuels clairs et percutants avec des outils comme Power BI ou Tableau a fait une énorme différence. Un graphique bien pensé vaut parfois bien plus qu'un long rapport.

Data visualization is essential for turning complex data into clear, actionable insights. Mastering tools and design principles enhances the impact of your visuals, making them engaging and easy to understand. A strong visual narrative helps stakeholders grasp key insights faster, driving smarter decisions. #DataVisualization #DataStorytelling

Dominar as ferramentas gráficas de visualização de dados como Python, R, Power Bi e excel entre outras, é fundamental para um profissional de dados, assim como um bom storytelling

Comecei a conhecer o Python e o R, já o Pitão eu ainda não conheço. Vou procurar para me informar sobre eata tecnologia e sua empregabilidade

Mastering Programming language skills can only be achieved by working through real world examples as part of training or through application within role. It then takes practice and thus time. One you have mastered one programming language it will be easier to learn new coding languages.

L'apprentissage continu est ce qui m'a permis de rester à jour et pertinent dans un domaine en constante évolution. Participer à des formations, suivre des webinaires ou rejoindre des communautés m'aide à m'adapter aux nouvelles technologies et à affiner mes compétences pour rester compétitif dans le monde des systèmes d’information.