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How often does a position in Business Data Analytics require the knowledge and use of software?

If possible, describe some of the software or strategies that you personally use when preforming a Data analysis role.

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Rob’s Answer

Always. Data is obtained through software that will sit on top of a database. The software is most often your data entry point although there are other ways for data to be put into a database. The software is essentially an interface for viewing data for specific purposes. Those purposes help to define how the data structures are built, their relationships, and other attributes. I am trying to simplify this in a way that isn't overly specific and technical, I hope it helps.

For example, I have software I use to track customer service issues. Some basic information is required to locate that customer in the database. If that information does not have the right attributes or is not "mapped" correctly, the software can not do its job or help a customer service agent do their job. Meaning, as a database administrator you need to understand both the purpose of the software and how the data will be used (this is very broad). Also, as a Business Data Analytics Guru, you need to understand where the data lives in the database and how it gets there. At the very least, you need to know some high-level functionality and how to use the software to successfully pull the correct data out. Something as simple as a vendor name could live in several different places and knowing which place you need to mine the data from will make you successful. The better you understand the use cases of the software, the more insight you will have to the data.

I hope this helps. Keep asking questions.
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Ron’s Answer

Business Data Analytics heavily relies on the knowledge and use of various software tools.
These will include spreadsheet programs like Microsoft Excel, programming languages like Python and R.
You may also need data visualization platforms like Tableau and Power BI, and database management systems like SQL to manipulate, analyze, and present data effectively.
All of these maybe open to you before you begin working but you may also attain this experience as a new hire.

So if you graduate from university or a college - no worries. They just need to know you have strong skills in the use of a computer with Office Tools and a strong command of the English language. For writing documentation and emails.
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Mario’s Answer

Hi Duran, It's important to have a good understanding of the tools that probably you will use such as Tableau, SQL, as Ron commented. Based on AI touch everything, its more important to leverage, evolve the use of those tools and innovate in the way the data is handled, applying AI, its crucial, trust on AI but always verify.
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Dinesh’s Answer

Dive into the exciting world of data analysis, where you'll use a variety of software and strategies to bring meaning to raw data. This field is all about gathering, refining, and interpreting data to uncover valuable insights that guide decision-making. Let's explore some of the essential tools and strategies that will be your companions on this journey:

Software Tools:
1. Python: With libraries such as Pandas, NumPy, Matplotlib, Seaborn, SciPy, Scikit-learn, and the interactive Jupyter Notebooks, Python is a data analyst's best friend.
2. R: Libraries like dplyr, ggplot2, tidyr, caret, and the development-friendly RStudio are your allies in data analysis.
3. SQL: Your go-to for querying and manipulating databases, with options like MySQL and PostgreSQL.
4. Excel: A versatile tool for data manipulation, basic analysis, and visualization.
5. Tableau: Your canvas for crafting interactive dashboards.
6. Power BI: Microsoft's powerful solution for data visualization and analytics.
7. Apache Spark: Ideal for large-scale data processing, with PySpark offering a Python interface.
8. Google Data Studio: A great platform for creating interactive dashboards and reports.

Strategies:
1. Data Cleaning and Preparation: Tackle missing values, outliers, and standardize formats to make your data shine.
2. Exploratory Data Analysis (EDA): Use descriptive statistics and visualizations to get to know your data.
3. Feature Engineering: Create new features to boost your model's performance.
4. Statistical Analysis: Apply tests and regression analysis to confirm your hypotheses.
5. Machine Learning: Harness supervised and unsupervised learning for predictive modeling and clustering.
6. Data Visualization: Craft charts, graphs, and dashboards to communicate your findings effectively.
7. Automation and Scripting: Automate tasks and ETL processes with scripts and workflow tools to save time and effort.
8. Collaboration and Documentation: Use version control like Git and document your processes for easy reproducibility.
9. Continuous Learning: Stay ahead of the curve by keeping up with the latest tools and methodologies, and engaging in communities and courses.

Embrace these tools and strategies to efficiently process data, extract insights, and champion data-driven decisions. Your journey in data analysis promises to be an exciting one!

Dinesh recommends the following next steps:

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