Data Science Basics: A Quick Introduction

Introduction to Data Science: A Developer's Guide

Data Science is a multidisciplinary field which utilizes Computer Science, Math, Statistics and Business knowledge to extract meaningful information or insights from large amount of data. Insights thus produced can be utilized in many ways. For example: Crucial outcomes presented to the business leadership in form of presentations or reports The numeric output displayed to users in form of visuals like dashboards The output processed in a web frameworks The output acts as a further customizable option in a mobile app.

In short, insights derived from the data can be utilized in many ways. Most of the developers would like to enhance their capabilities by learning how to build data driven applications and how to build and deploy smart algorithms.

In this blog, we’ll explore the fundamentals of data science, its importance, and provide practical examples using Python.

What is Data Science?

Data Science involves the following activities: Collection of Data Cleaning, Sanitization and Manipulation of Data Analysis of data Interpretation of Results

Each of the above steps mentioned is a science in itself. In fact careers can be built on any one of the activities mentioned. For example, the collection of data from multiple software systems into one platform is called Data Engineering, which could be a career in itself. So each activity listed has its own complexities and scale. Many organizations have different teams doing each of these activities. However for the learners it is important to be aware of most of the things that happen.

All these activities together turn raw data that was useless into actionable insights that can create real business value.

Why is Data Science Important?

As the business got digitized, the speed at which businesses could scale, when drastically up. Business started to rely more on the data for its strategic decision making that managers inputs. Following are the reasons why organizations turned to data driven decision making. Informed Decision-Making: Organizations leverage data to make strategic decisions. Predictive Analytics: Data science enables forecasting future trends based on historical data. Personalization: Businesses can tailor their offerings to individual customer preferences. Efficiency: Automating data analysis processes saves time and resources.

Key Components of a Data Science project:

Data Collection: Gathering data from various sources. This could be a very complex exercise. Following are the questions one need to ask before starting the data collection process. Is it a one time activity or a continuous process? If its a continuous process then in what frequency does the data collection happen? Where and how do you store the collected data. What are the data sources? How are the different data collected together and combined together? There are millions of combinations possible and for each of the million combination there are millions of ways data collection can happen. However for the sake of simplicity let me mention some of the common tools used in this space. Excel, SQL, No-SQL, Python and all the proprietary ETL and ELT tools. At the very start of the project the developer might want to just pull the data samples using SQL and paste it in an excel sheet and just start! But later in the project life cycle the data engineering pipelines may be designed and deployed.

Data Cleaning: Preparing data for analysis by handling missing values and inconsistencies. This is the activity that occupies the Data Scientist the most. My suggestion to every learner is that they should try cleaning data for 2 months. If you are not enjoying data cleaning (Or at least staying alive while data cleaning, because lets face it, nobody enjoys data cleaning) please don't aspire to become a data scientist. The common tools used here is python. Pandas and NumPy are the two most common packages that will come in very handy for data cleaning. But the most important tool is the business understanding. We should be able to connect every data field to the business we are dealing with only then we will come to know if certain data values make sense or not.

Exploratory Data Analysis (EDA): Once we believe that the data is clean, we try to understand the data through visualization and summary statistics. Again the common tools are Python, some visualization packages such as matplotlib or seaborn and the business knowledge. Every data explored teaches you about the business. For example you are exploring the sales data of a retail store and you observe the average bill for a user varies over different geographies. Now your inherent detective should start asking questions and explore the data further to answer those.

Modeling: Applying statistical and machine learning models to make predictions. This is the most talked about and celebrated topic in the data science community. But to disappoint many of you, this is only 20% of the work. All the fancy and magical models are built on this stage. Common tools here are python and its modeling packages such as scikit learn, tensorflow, Keras etc.

Deployment: Integrating models into applications for real-time use. This step is where the real value of the models are realized. If you are able to derive brilliant insights from the data present and if you are not able to use the insight for further business growth, what the use of the insight. For example by learning your location, school name, subjects interested in, social media application can predict who your friend is. However that friend suggestion should be displayed on the mobile app and the desktop app of the social media platform. Hence the data science model needs to become a part of the overall engineering framework of the application.

Importance of Python

Python is a programming language that enables data science projects. Python has packages that make most of the above described activities very easy. Python is very English like and has a very easy to remember syntax. Python is a general purpose programming language hence, it can do anything that a programming language is supposed to do. It is incredibly fast and on top of all this ITS FREE!! So no wonder that python is the best programming language to perform all (or most of) the data science projects components.

Some of the commonly used libraries are:

Pandas: For data manipulation and analysis. NumPy: For numerical computations. Matplotlib/Seaborn: For data visualization. Scikit-learn: For machine learning.

No points for guessing. All these packages are FREE!

Conclusion

In conclusion, data science is a powerful discipline that transforms raw data into actionable insights, driving informed decision-making and strategic growth for organizations. By understanding the key components of data science—from data collection and cleaning to modeling and deployment—developers can enhance their skill sets and contribute significantly to data-driven projects.

Python stands out as the go-to programming language for data science, thanks to its simplicity, versatility, and a rich ecosystem of libraries that streamline the entire data science workflow.

I hope this introduction has sparked your interest in the fascinating world of data science. Stay tuned for more insightful blogs in this series, where I will delve deeper into each aspect of data science, share practical examples, and provide tips to help you succeed in your data-driven endeavors.

Don’t forget to follow Data Science Wonders for updates and join the conversation in the comments below! Your feedback and questions are always welcome as we explore this exciting field together. Happy coding!