Data Analytics
Python coding for data analysis is like having a versatile toolbox at your fingertips, ready to tackle a variety of tasks. At its core, Python provides powerful libraries such as Pandas, NumPy, and Matplotlib, which allow users to manipulate and visualize raw data with ease. Understanding this raw data is paramount before diving into deeper analysis. After all, it's much like preparing a meal; you wouldn’t want to start cooking without first preparing your ingredients.
Once you've got a grip on your data, you can explore regression analysis, a fundamental statistical method used to understand relationships between variables. In straightforward terms, regression helps answer questions about the relationships between items in the dataset. It's an essential tool for making predictions and can be implemented easily in Python with libraries like Statsmodels and Scikit-learn.
Speaking of predictions, let’s not forget about machine learning and the impact it has had in data analytics in recent years. Models can be created quickly that learn from your data and improve over time, all while employing user-friendly libraries like Scikit-learn and TensorFlow. Machine learning unleashes the potential to uncover trends and insights that simple analysis might miss. With these tools, you can transform data into actionable insights, making informed decisions that drive business growth.
In summary, Python not only simplifies data gathering and manipulation but also opens the door to more complex analyses like regression and machine learning. By taking the time to understand your raw data, you'll set a solid foundation for better insights and smarter decision-making.
Basic review and cleaning of the data uses very simple commands. This essential first step allows an analyst to plan and focus their efforts.
The early cleaning consists of reviewing the overall structure of the data, evaluating missing, corrupted, or mis-formatted data. This first major step provides the foundation for all future analysis. It ensures that all data is valid, complete, and formatted in a usable and useful structure.
This review of the data reduces the chance that an analyst will miss the mark or provide flawed results to stakeholders and ensures that results can be trusted and used for major decisions within an organization.
Data Cleaning and Review
Analyzing the Data
Now that the data has been fully prepared for use, we move on to analysis. In order to properly analyze the data we dive deeply into many wonderful tools which allow us to identify patterns and trends within the data.
Tools like Pandas, NumPy, SciPy, and Scikit-learn multiply a single analysts ability to analyze data and complete very complex operations with consistency and confidence. These are but a few of the deep ocean of tools available within Python that allow us to stand on the shoulders of all of those that pioneered the space. Every day we should strive to keep up with developments within the space and ensure that we are utilizing the most effective and efficient tools that we can access.
Interpret
The story that the data wants to tell us is deciphered here. The beating heart of data analytics is the analysis itself, but without the ability to interpret the analysis it is not really all that useful. The first step in interpreting the results of the analysis is to find themes and trends. As we pull back the veil on our data we start to find pieces of the story the data wants to tell. Each linear regression, every random forest, and all of the machine learning algorithms give us more and more pieces to show what the data has been trying to tell us.
Before moving on to the final major step in the process we review all of our assumptions, the questions we were asked to look at, and the data set. Taking the time to review again at this point ensures that we are providing the correct analysis for the business needs. Once we are confident that we are providing the more useful data analysis to answer the business question we can move on to sharing the results.
Translating
Not everyone speaks the same language. In order to ensure that the data we dug so deeply into we need to understand our audience. To make the data understandable to the largest audience we utilize visualizations and narrative presentations.
Take a look at some examples of data presentation by clicking here.