Strategies & Tips To Help You Get Started In The Field Of Data Science

As technology professionals, we are constantly pushing the boundaries of innovation, applying our skills to improve a product or service across our organizations. Data Science, one of the fastest-growing fields in recent years, extracts insights from data using scientific methods, processes, algorithms, and systems.

Tips to get started in data science for beginners: Determine the Primary Business Issue

Before we begin analyzing any data set, we must first define our end goal. What is the problem we're attempting to solve with data? Are we attempting to forecast our customers' behavior, or are we introducing a new product line for our company? Unfortunately, we frequently get caught up in finding the best model for our analysis without truly understanding the underlying business problem. Once we've identified the problem we want to solve with data analytics, we'll have a better idea of what steps to take to ensure we're on the right track.

Choose the Correct Data

First and foremost, to use data analytics to solve any business problem, we must ensure that the data we have is the correct data. This begins with examining the features, or columns, in our data. It is important to note that a feature is a measurable quality of the object in question. In a data set, they are also known as "variables" or "attributes." A data set containing customer information, for example, will include attributes such as Name, Age, and Gender. The quality of the features in your data set significantly influences the quality of the insights gained when using that data for machine learning. Of course, you can improve the quality of your features using methods such as feature selection and feature engineering. But it is critical, to begin with a set of relevant features that can be analyzed.

Prepare your data

If a raw data set is immediately fed into a machine learning model, the predictions will likely be highly inaccurate. This is because machine learning models are based on algorithms that learn from data patterns, which necessitates a pre-processed data set. The more accurate the models are, the more error-free the data is. Here are three critical pre-processing sub-steps to consider before the modeling stage:

Data cleaning entails dealing with missing values and removing noisy or meaningless data. Data Transformation: Normalization of numerical variables, creation of new attributes from existing features, and creation of concept hierarchies Data Reduction: Encoding techniques are used to reduce the size of the data. Principal Component Analysis and Wavelet Transformations are the two most effective dimension reduction methods.

Make your data visual

Data visualization is an excellent tool for comprehending existing relationships or patterns within our features. Scatter plots, Histograms, Bar Charts, and Correlation plots are a few of my favorite tools for providing descriptive analytics on features. For example, according to the Tableau dashboard below, Atlanta has the longest delay times, and the summer months are the busiest. The insights gained from data visualization allow us to delve deeper into our business problem, highlighting key data points and features.

Conclusion:

Overall, these methods are excellent starting points for machine learning projects. As a Data Analytics student, I found that implementing these methods before the modeling stage helped me succeed in my academic projects. Of course, they are not absolutely necessary, but if these preliminary guidelines are followed, you can expect better results from your models in the future.

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