Analytical Mindset and Analytics Pipeline
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Analytical Mindset and Analytics Pipeline

Basty’s Notebook

Hi, everyone! My name is Basty, and I’m a data scientist and educator from Metro Manila. If there’s something I value so much, that is education. Education has allowed me to not only dive into the amazing world of code and data, but also to encourage and inspire others to do the same. Read more about me here.

Outside of work and school, I love playing video games like Valorant and League of Legends. I also love listening to Broadway musicals (HAMILTON, DEH, TICK TICK BOOM ALL THE WAY!). Lastly, I LOVE watching Friends, New Girl, HIMYM, and The Big Bang Theory.

Now, let’s take a look at my notebook!

March 2023 Notebook entry

Being a data scientist, it’s essential that you have to be critical or a deep thinker when it comes to analyzing data. We can’t simply dive into datasets without fully understanding the context of the data, problem, and goal. Usually, a lot of data people fall into the trap of adapting an ad hoc approach when it comes to solving tasks. While this kind of approach can be useful and simple, it gets unreliable and difficult if the tasks are complex or too big. 

With that in mind, this blog’s topic will hopefully teach you the importance of having an analytical mindset and the significance of having an analytics pipeline. Let’s get started!

Analytical Mindset

In today's data-driven world, data scientists are in high demand. They are responsible for collecting, analyzing, and interpreting data to help organizations make better decisions. To be a successful data scientist, it is important to have an analytical mindset and develop an analytics pipeline. So what is an analytical mindset?

An analytical mindset is the ability to approach problems in a logical, structured, and data-driven manner. It involves being curious, asking questions, and seeking out the best data and analytical tools to answer those questions. Having an analytical mindset means that you can break down complex problems into smaller, more manageable parts, and identify patterns and trends in the data.

Having an analytical mindset involves developing a curiosity and interest in problem-solving and data-driven insights. This involves looking at data objectively and rationally, looking for patterns and trends, and finding ways to draw meaningful conclusions. It also involves understanding the importance of data accuracy, reliability, and completeness, and having a willingness to explore new methods and techniques to analyze data more effectively. Analyzing data in a rational and logical manner can help identify solutions to a problem and empower individuals to make informed decisions.

Analytics Pipeline

As I’ve mentioned, not utilizing a methodological approach can backfire when tasks become too big or complex. In data, it’s often better to develop a process that captures the data science cycle—and that is an analytics pipeline. An analytics pipeline is a series of steps that a data scientist follows to analyze data and produce insights. It typically involves data collection, data preprocessing, exploratory data analysis, feature engineering, model building, and model evaluation.

  1. Data collection

    The first step in the analytics pipeline is data collection. This is the step where you need to identify what data you need to answer the question at hand, and then collect that data from various sources. This may involve accessing data from internal databases, web scraping, or working with third-party data providers.
  1. Data preprocessing

    The second step is data preprocessing. Data preprocessing involves cleaning and preparing the data for analysis. It is very rare that the data you’ll be working with is clean, so this step is a crucial part of the process because it will determine as well the quality of the insights you’ll get. This may involve removing missing values, dealing with outliers, and transforming the data into a format that can be used by analytical tools.
  1. Exploratory data analysis (EDA)

    The third step is exploratory data analysis (EDA). EDA involves using visualizations and statistical techniques to understand the data and identify patterns and relationships. EDA helps data scientists identify potential issues with the data and generate hypotheses that can be tested with models.
  1. Feature engineering

    The fourth step is feature engineering. Feature engineering involves creating new variables or transforming existing variables to improve the performance of models. This may involve using domain knowledge, statistical techniques, or machine learning algorithms.
  1. Model building

    The fifth step is model building. Model building involves using statistical or machine learning algorithms to make predictions based on the data. Data scientists need to choose the appropriate algorithm for the problem they are trying to solve, and then train and test the model using the data. Examples of algorithms are Linear Regression, K-Means, and Logistic Regression.
  1. Model evaluation

    The final step in the analytics pipeline is model evaluation. Model evaluation involves assessing the performance of the model and identifying areas for improvement. This may involve comparing the predicted values to the actual values, or using other performance metrics such as accuracy or precision.

Developing an analytical mindset and analytics pipeline takes time and practice. You won’t simply be analytical through one project, and you certainly won’t master the analytics pipeline by applying it once. These two are skills that are real-time, meaning they never end to update as they are always changing and improving. Every time you find yourself doing a task, you will always learn something new—that is why data scientists need to stay up-to-date with the latest trends and techniques in the field and to be willing to experiment with new tools and approaches. It is important to be flexible and adaptable, as different problems may require different approaches.

In conclusion, having an analytical mindset and developing an analytics pipeline are critical for success as a data scientist. I hope that you were able to pick up something from this blog and that it can help you generate insights that can help you and your organization make better decisions. Always remember that with the right mindset and approach, anyone can unlock the full potential of data and create real value for the organization and the society.

Never stop learning!