Quieres convertirte en analista de datos. ¿Qué habilidades necesitas aprender primero?

Gain proficiency in statistical analysis and the ability to interpret data trends and patterns. Develop critical thinking and a broader understanding of business. Understand how to use data visualization tools and techniques to communicate your findings effectively. Additionally, developing strong problem-solving abilities and acquiring knowledge in database management, particularly SQL, will equip you with the necessary skills to analyze and derive insights from large datasets.

Become a data analysis master chef with this recipe: Ingredients: Python (P), R (R), SQL (S), SAS (SAS) Instructions: 1. Start with Python (P), the versatile all-rounder. 2. Sprinkle in R (R) for statistical flair. 3. Add a SQL (S) pinch for relational database harmony. 4. Finally, a dollop of SAS (SAS) for extra pizzazz. Serve with a side of curiosity and a sprinkle of creativity. With this mathematical recipe, you'll become the master chef of data analysis, delighting your audience with every bite of insight! So roll up your sleeves and let's get cooking up some data magic! 🍳✨

One of the most important skills to learn as a data analyst, or any profession is to have the ability and attitude to learn new things. It is the only way one gets to improve and add to their skills. The basic skill to learn is how to extract, clean and group data via SQL or even Excel, and any statistical knowledge which may be needed for calculation or comparison between different metrics in a report. Then one must know what charts are appropriate for visualization of their reports. These combined with a sound understanding of business requirements is what makes a good data analyst.

To start specifically on a programming language, Python is highly versatile and widely used in data analysis. Learn the basics of Python and explore libraries like NumPy, pandas, and Matplotlib for data manipulation, analysis, and visualization. Secondly, R is a powerful language for statistical computing and data analysis. Familiarize yourself with R's syntax and utilize packages like dplyr and ggplot2 for data manipulation and visualization. Adding on programming language, Structured Query Language (SQL) is essential for working with databases. Learn how to write SQL queries to extract, filter, and manipulate data stored in relational databases. Understand concepts like joins, aggregations, and data manipulation using SQL.

Yes, Based on my observation, mastering SQL language should be the first priority. SQL serves as a backbone not only for the Data Analyst role but also for various other roles such as business and financial analysts. Following SQL proficiency, learning either Python or R would be the next step. These languages are versatile and widely used in data analysis and manipulation tasks, providing additional tools and libraries for advanced analytics and visualization.

In my data analysis journey, I have learnt that having simple and straightforward visuals is better than a fancy-looking and complicated dashboard, clearly, you would not want to waste time in explaining the visuals but what the data is saying and its relevance! Always tailor your visuals to suit the preferences of your audience(obviously stakeholders).

For Data Visualization part of becoming a Data Analyst, understanding Data Visualization Principles is the first thing to learn and understand. Familiarize yourself with the fundamental principles of data visualization, such as effectively representing data, choosing appropriate chart types, and conveying information accurately and clearly. Also, one needs to be familiar with data Visualization Tools. Learn popular data visualization tools such as Tableau, Power BI, or open-source options like Matplotlib and Seaborn in Python. Gain proficiency in using these tools to create visually compelling and interactive charts, graphs, and dashboards.

Data Analysis + Presentation = 💯 Success Data Visualization 📊 + Clear Communication 🗣️ = Understanding 🧠 Tools: Qlik + Tableau + Power BI + Matplotlib = Data Magic 🪄 Key Equation: 📊 + 🗣️ = 🧠 In simpler terms: Graphs + Talk = Smart Decisions! 🚀 Remember: Data is Power, but Visualization is the Key! 🔑

In the journey to becoming a data analyst, business intelligence tools play a pivotal role in presenting insights from data in an easy-to-understand manner. Excel and its pivot tables are fundamental and should be thoroughly understood, as it's often said that Excel is the mother of business intelligence. However, Excel has its limitations, which is why it's beneficial to learn one of the business intelligence tools such as Power BI, Tableau, or QlikView. These tools offer more advanced features and capabilities for data visualization and analysis, empowering analysts to extract deeper insights from data sets.

Data Visualization is one of the most important main stages in the Data Analysis process, and it is the penultimate in it, that is, before the storytelling stage, its importance is that it is a summary of the analysis process from cleaning, tranaforming, modeling and analysis to answer some questions or solve a specific problem. And it is by displaying data in graphs or charts in a consistent and easy way for stakeholders to understand to help them make decisions on the basis of this analysis. The most popular and powerful tools for data visualization are Power BI and Tableu.

Statistics and mathematics: To become a data analyst, understanding statistical analysis is crucial. Data analysts utilize statistical techniques to analyze and interpret data. This involves grasping concepts like probability, hypothesis testing, regression analysis, and descriptive statistics. These foundations allow for drawing meaningful insights from data. Also, Data interpretation skills are also essential. Statistics provide the tools needed to interpret and make sense of data. By acquiring knowledge in statistical concepts, one can uncover patterns, relationships, and trends within datasets. This ability enables the derivation of valuable insights and facilitates data-driven decision making.

Statistics and Mathematics: Descriptive Statistics: Understand basic descriptive statistics to summarize and describe key features of a dataset, such as mean, median, mode, and standard deviation. Inferential Statistics: Learn inferential statistics to make predictions and inferences about a population based on a sample. This includes hypothesis testing, confidence intervals, and regression analysis. Probability: Gain a foundational understanding of probability theory, as it is fundamental to many statistical concepts used in data analysis.

Indeed, learning the basics of statistics is very important, basics such as simple linear regression, mean, median and mode. Elementary statistics knowledge is a must for data analyst

Do not underestimate a good statistical analysis. When you have in your mind the correct mathematical descriptive approach to any set of data so basically you now what mean, standard deviation, percentile you can deal with every type of data. Every large distributed numerical data can be renormalize with a Normal distribution so once done it is easily to reply to question like "what will be my next month sales"? Up to 95% there is an interval where you could find them.

Math or statistics play a crucial role in data analysis but it is true that 20% math or statistics can manage 80% data analysis work. For a fresher like me I believe it's okay to start with the basic math and statistics.

Data Analysis needs understanding Business Objectives: Data analysts need to align their analysis with the goals and objectives of the business. By developing business acumen, you can grasp the context and purpose of the data analysis, allowing you to focus on providing insights that drive business value. Also, Identifying Relevant Data is an essential skill to power effective decision making. Having business acumen helps you identify the right data sources and variables that are most relevant to the business questions at hand. This skill allows you to gather and analyze data that directly impacts the decision-making process.

Understanding the business behind the data is critical. One should know what the data means and is used for. It gives you a clear understanding of the data and can help in framing the findings in a context that resonates with stakeholders, facilitating data-driven decision-making.

In my experience, having business savvy is just as important as technical chops in data analysis. It's about understanding the bigger picture—what problem are you solving with the data? How does it align with the company's goals? Being able to answer these questions helps you choose the right data sources and methods and ensures your analysis adds real value. You can develop business acumen by keeping up with industry trends, learning from experts, and asking smart questions. It's not just about crunching numbers; it's about making a meaningful impact on the business.

In the journey of becoming a data analyst, highlighting the role of business acumen is crucial. As data analysts, we primarily work on data to address various business problems or contribute to business growth in most projects. Therefore, a deep understanding of the business context is essential. Similarly, just as business analysts need to learn different terms and concepts relevant to their respective fields (e.g., financial terms for financial analysts, healthcare terms for healthcare analysts, e-commerce terms for e-commerce analysts), acquiring business understanding is indispensable for aspiring data analysts.

Domain knowledge is helpful in the process of analysis. As an analyst, business acumen will help in recognising and appreciating the KPIs and the metrics that the organisation invariably tracks. As an analyst with an interest in the medical supply chain industry, I am cognisant of the key metrics that the organisation tracks. These include lead time, stock turn over ratio, reorder points, etc.

It's one of the important, required and influential skills in the data analysis process, you can use this skill in all stages of data analysis to discover, clean, analyze, and even create the report or the Dashboard, and its use at all stages means the efficiency of each stage and creativity in it. Can be improved by taking advantage of mistakes, asking questions and repeated experience

Develop critical thinking skills to approach data analysis problems logically and solve them effectively. In summary, critical thinking skills are indispensable for effective data analysis. By following these strategies and approaches, analysts can approach data analysis problems systematically, identify meaningful insights, and drive informed decision-making in diverse domains.

In everyday terms, think of data analysis like solving a puzzle or mystery. Critical thinking is your superpower in this adventure. It’s about looking at the pieces of information you have, figuring out if they make sense, and questioning everything until you’re sure you’ve got it right. Imagine you’re investigating a crime scene. You need to think logically to connect the dots, analyze the evidence without jumping to conclusions, and challenge any assumptions along the way. It’s like being a detective in your own data world.

For data analysts, critical thinking is used extensively to interpret data accurately, identify trends, and spot inaccuracies. It enables you to ask the right questions and methodological exploration of alternative scenarios. This skill is crucial for deriving data-driven insights, making informed decisions, and effectively communicating results, ensuring the integrity and utility of their analyses in a professional context.

Critical thinking also involves asking the right questions that align with your objectives. Approach every situation with an open mindset, free from bias, in order to understand the story that your data is telling.

Collaboration and communication are some of the most important skills to learn first if you want to become a data analyst. Here's why: Collaboration: Data analysts rarely work in isolation. They need to collaborate with various teams, such as marketing, sales, and engineering, to understand their needs and translate data insights into actionable solutions. Communication: Data analysis is meaningless if you can't effectively communicate your findings to stakeholders. This may involve creating clear and concise reports, presentations, and visualizations that cater to different technical backgrounds. Strong communication helps decision-makers understand the story behind the data and make informed choices based on insights.

In data analysis, teamwork and communication are key, similar to working in a team at any job. You'll collaborate with other experts and different departments, like marketing or finance, to tackle tasks. Just like in any workplace, you'll use tools like Slack and Zoom to stay connected, share ideas, and update each other. Listening and understanding your teammates' needs is crucial, just like in any work setting. Tools like Google Docs and GitHub help keep everyone organized and on track. It's all about working together and communicating effectively to achieve success with your data projects!

The importance of them that is as a data analyst, you will communicate with a team of data experts that includes data scientists and engineers, or even outside the data framework will deal with various departments in the company, so you need to cooperate with them to find out what they need from you or what you need for your smooth workflow. The importance of communication and may be somewhat related to the storytelling stage, which may be the last stage of the analysis and may enable you to communicate by summarizing what you have done and discussing it with stakeholders to help them make decisions based on the analysis process. Platforms like slack, Zoom, Google Docs, and more are tools for communication and collaboration.

Collaboration and communication are particularly important in a data analyst role, as there is very often a need for the sharing of data across different departments or organisations, understanding the context and purpose of the data collected and exchanging perspectives across different disciplines, in addressing business problems or research needs. It is also equally important to first understand and communicate clearly the objectives of the data analysis (in other words, the research question) with stakeholders involved and after, to communicate findings in a manner that stakeholders can best understand and develop data-driven solutions accordingly.

Collaboration and communication are the most important soft skills that a data analyst should learn in their career as early as possible, because after all the report preparation and all you as a data analyst have to prepare and give a presentation to the stakeholders in a very simple and effective way.

Autodidactic Proficiency in Data Analysis The capacity for independent learning and skill acquisition in data analysis is crucial. Given the rapidly evolving nature of the field, with continuous changes and emerging techniques, it's essential to swiftly adapt by exploring various ideas and methodologies.

It is important to seek guidance on the resources you can use as a beginner in data analysis, there are so many tools and resources out there, it can be overwhelming for someone new to data analysis. Seek guidance and advice to seniors, they can share resources or refer you to platforms where you can get the content you need.