Here's how you can build a solid base in data analysis before delving into data visualization.

To lay a solid foundation in data analysis before delving into data visualization, start by mastering the basics. Get familiar with statistical concepts like mean, median, mode, variance, and standard deviation. These are the building blocks for analyzing datasets and are crucial for identifying trends and patterns. Learn to use spreadsheet software to perform simple calculations and gradually advance to more complex functions and formulas. This fundamental knowledge will be invaluable as you later interpret visual representations of data.

In college I got the privilege to learn the foundations of handling data and its integrations. Kickstarting your journey in data analysis begins with understanding basic statistical concepts such as mean, median, mode, variance, and standard deviation. Mastering spreadsheet software like Excel or Google Sheets for performing simple to complex calculations is crucial. Additionally, learning to create and interpret charts and graphs will help you effectively identify trends and patterns in data. This foundational knowledge is essential for analyzing data sets and making informed decisions.

?? When I first started my journey in data analysis, I found that studying the basics through books was helpful. However, what really made a difference was getting my hands dirty with hands-on projects. Tackling small demo projects or participating in Kaggle competitions helped me break down the theoretical parts and reinforced my understanding through repetition. Begin with Excel (as always i guess) to perform simple calculations and gradually work your way up to more complex functions and formulas or start working with data models or pivot/power pivot tables in Excel. This foundational knowledge will be very valueable when you later work with more sophisticated tools like Power BI, Tableau or SAP SAC.

One interesting thing about building on the basics is that we leverage transferred knowledge from basic mathematics. Aggregate and statistical functions such as sum, count, mean, mode, median are functions we perform within regular activities whether at home, school or in the mall hence it becomes easier understanding and practicing these functions on Data analytics tools such as Excel or PowerBi. These basic functions are very crucial is determining the numerical value of datasets which are the founding blocks for data driven insights. Without them, data analysis is vague and can't be used to drive informed decision-making.

Building a solid base in data analysis is crucial before delving into data visualization. Start by mastering Excel, focusing on functions like VLOOKUP, SUMIFS, and pivot tables to manipulate and analyze data efficiently. Learn SQL for querying databases and handling large datasets. Practice cleaning and organizing data to ensure accuracy and reliability. By solidifying these skills, you'll be well-prepared to create insightful and meaningful visualizations that effectively communicate your findings.

One thing I’ve found helpful is learning and knowing the tools before delving into analysis. Start with the basics. -Understanding spreadsheets using either Microsoft excel or Google Sheets. -How to Query/Manipulate/update and manage database using SQL, you could learn from either of the software like MySQL, SQL Server, PostgreSQL amongst others. Understand statistics using SPSS for Qualitative analysis, testing hypothesis and the likes. Then Data visualization using either PowerBi, Tableau, Qlik, google data studio etc., then you can pitch your idea by learning advance analysis, predictive analysis, creating models, machine learning, NLP etc by learning python and its libraries. The likes of Numpy, Pandas, Matplotlib, Scipy, Seabon etc

Excel, Pivot Tables, Power Pivot, Databases, SQL, Power BI or Tableau, Python - From my experience, starting with Excel is key. Mastering Pivot Tables and Power Pivot helps in managing data efficiently and learning Power Query. Once you’re comfortable with these, move on to databases and SQL. Understanding how to query databases is a game changer for handling dataframes. After that, dive into Power BI or Tableau. These tools are excellent for creating great data visualizations. Finally, learning Python with classic data libraries like matplotlib or seaborn (and pandas) will improve your data manipulation and analysis skills, making your workflow much more efficient.

Mastering necessary softwares for Data analysis is very key. Getting your feet wet with Excel for cleaning and transforming data is a good start to comprehending analytics tools. SQL, a query language tool is very vital for mastering queries and retrieving tables and information from a database. Programming languages such as Python and R will enable one hone greatly, the manipulation and analysis of data in advanced forms. Tableau and PowerBi are excellent visualization tools which can be used for interpreting the analysis of data obtained inorder to foster decision making amongst stakeholders.

I believe every data analyst should prioritize starting with Excel, especially for young, upcoming talents who may find themselves in smaller businesses where specialized software might not be available. Excel, being the most widely used tool, possesses enough power to drive businesses to a certain level of maturity. Its versatility and functionality make it an indispensable tool for data analysis. Mastering Excel equips analysts with the skills to handle various data-related tasks efficiently, from data entry and cleaning to complex calculations and visualizations. This foundational knowledge not only enhances their analytical capabilities but also prepares them to adapt to more advanced tools in the future.

As you progress in Basics, dive deeper into software tools like Python (with libraries like Pandas) for powerful data manipulation, R (with libraries like dplyr) for streamlined analysis, and SQL for unlocking data troves stored in databases. While the best choice depends on your needs, explore different options – experiment with tutorials and find what resonates with you. Remember, these tools aren't just for show; mastering them lets you clean, sort, and analyze vast datasets efficiently, freeing you to focus on extracting insights and crafting impactful visualizations that tell a clear story.

The most important aspect before delving into analysis is to understand the data. Having a clear and vivid understanding of the data opens door to hidden insights about the data, understanding the data triggers lots of questions on why what has happened had happened. Having a clear understanding of the data, then you can go ahead to transforming the data to best fit using your most preferred softwares likes (Excel/SQL etc) for the purpose of the analysis before the visualization stage.

A good data understanding is the foundation for deriving hidden insights found in data. Understanding data can help a Data Analyst spot, focus and filter to use required information from datasets. This can enable one adopt the most appropriate analysis and visualization to uncover the needed insights for informed decision-making.

Accuracy in data ensures decisions are based on reliable and precise information Trust builds confidence in the data among stakeholders. In my opinion, these are the most important factors when it comes to data quality. Try to solve these errors as much upwards in your data source as possible. Its similar to the shift-left concept in Devops.

Understanding your data is the cornerstone of meaningful visualization. Decipher between quantitative (numerical) data like sales figures and qualitative (descriptive) data like customer reviews. Quantitative data allows calculations and visualizations like bar charts, while qualitative data reveals the "why" behind the "what" through thematic analysis. Maintaining data integrity is crucial, so employ techniques to handle missing values and inconsistencies. Recognizing data types empowers you to choose the right tools – statistical analysis for numbers and content analysis for narratives. Ultimately, understanding data is an ongoing journey that unlocks insights for impactful visualizations based on the type of information you have.

Understanding the business processes, including the flow of data and the relationships between tables and dimensions, is crucial for effective data analysis. It is essential to take the time and effort to thoroughly comprehend how data is uploaded into the system and how it interacts within the database. This foundational task demands dedication and respect, as it significantly impacts the efficiency and accuracy of any data analysis work. Everyone involved should prioritize this aspect to ensure optimal performance and insightful analysis.

I explored platforms like Kaggle and took on challenges. For me, it wasn't about the rank but the skills I developed. Engaging with these projects helped me grow. Additionally, I learned optimization techniques like Scrum Agile and time management methods such as Pomodoro to enhance my productivity. Keep practicing and evolving! Hands-on experience in projects taught me how to ask the right questions.

Flex your data analysis muscles! Grab real-world datasets (government sites, Kaggle) and practice. Start simple with analyzing sales figures (peak seasons?) or website traffic (user patterns?). As you gain confidence, tackle complex datasets like market research (customer preferences) or social media sentiment analysis (public opinion). This hands-on practice not only refines your ability to ask the right questions ("What are the trends?") and interpret answers but also equips you to choose the most impactful visualizations that resonate with your audience.

Interpreting results is deeply intertwined with comprehending the data. It is essential to be mindful of every detail concerning the data and any external factors that might affect the interpretation. A data analyst should always approach the data with a critical mindset, continually questioning and validating their assumptions. To ensure the results are as unbiased and factually accurate as possible, analysts should repeatedly interpret the data, each time eliminating any preconceived hypotheses. This rigorous practice helps in minimizing bias and enhancing the reliability of the insights drawn from the data.

Don't jump to fancy charts yet! After analyzing your data, it's time to figure out what it means. Look for trends (like sales going up over time) and connections (like website traffic increasing after an ad campaign). Spot any weird data points that might be mistaken. Remember, pretty pictures don't matter if you misunderstand the data! Be a data detective – pay close attention and question your findings to make sure they're true. This careful interpretation is key to creating visualizations that tell a clear and accurate story.

Always start with research and context, and make sure you work with recently updated and credible information. Stay objective, as visualization can indeed affect minds and mislead. An example of a book I suggest starting with is 'Data Feminism' by C. D'Ignazio. It's a must-read.

The most interesting part of analysis is the visualization stage. After mastering data transformation, data manipulation, next is to bring the raw numbers into charts. There are several charts available to use. However, before publishing any dashboard/report, you need to understand the use of each graphs and when to use them. For example; line graph is used to track changes over short and long period of time. It could be a daily trend, monthly trend, or yearly trend. You need to know that your dashboard should be self explanatory. Easy to comprehend and understand by the stakeholders.

For me, the simpler, the better. I began with simple visualizations and gradually learned more complicated ones. However, I found myself transitioning back to simpler solutions because they are just clearer and more effective in conveying the right message.