What enables machine learning to work so well? It is Data
Data is generated enormous amounts in our surroundings, even on our cell phones. If you pick up your phone, you’ll see that it contains various types of data such as videos, audio, pictures, text, and documents. This abundance of data is a fundamental element that contributes to the optimal performance of machine learning.
Data Science
Data Science is the art of transforming raw data into actionable insights. It empowers businesses to make informed decisions, uncover hidden patterns, and drive innovation by leveraging statistical analysis, machine learning, and data visualization.
Forms of data
Data is in columns, and tables are a tabular form of data.
| Size of House (Square feet) | Price ($1000) |
|---|---|
| 523 | 115 |
| 645 | 150 |
| 708 | 210 |
| 1034 | 280 |
| 2290 | 355 |
| A | B |
Now, let’s discuss the features of data in machine learning. We have two columns: the first feature is the size of the house, and the second feature is the price. When discussing machine learning, we can categorize it as supervised learning, where we provide input (referred to as A) and output (referred to as B). So, what is supervised learning in it? It is A to B mapping.
Data is often unique to your business
Data is often unique to your business, and this is an example of a dataset that a real estate agency might have that they tried to help price houses.
It’s up to you to decide what is A and what is B and how to choose these definitions of A and B to make it valuable for your business.
Another example: If you have a certain budget and you want to decide what size of house, you can afford, then you might decide that input A is how much someone spends, B is just the size of the house in square feet, and that would be a different choice of A and B that tells you, given a certain budget, what’s the size of house you should be maybe looking at.
Data Analysis
Data Analysis is a field within data science that involves inspecting, cleansing, transforming, and modelling data to discover useful information, understand patterns and trends, and make informed decisions.
Importance of Data Analysis
Data Analysis is essential because it helps organizations make better decisions by providing insights into trends and patterns. It can improve efficiency, identify growth opportunities, and solve complex problems.
Steps in Data Analysis
- Data Collection: The first step is gathering data from various sources. It can include databases, surveys, experiments, or web scraping. The quality of your analysis depends heavily on the quality of your data.
- Data Cleaning: It involves removing errors and inconsistencies from the data. This step is crucial because dirty data can lead to incorrect conclusions. Familiar tasks include handling missing values, eliminating duplicates, and correcting errors.
- Data Exploration: In this step, you explore the data to understand its structure and main characteristics. It often involves visualizing the data using charts and graphs to identify patterns and outliers.
- Data Transformation: It involves converting data into a suitable format for analysis. It can include normalizing data, creating new variables, or aggregating data. The goal is to prepare the data for the next step.
- Data Modeling: The process of applying statistical or machine learning models to the data to make predictions or identify patterns. This step can involve techniques like regression analysis, clustering, or classification.
- Data Interpretation: The final step is interpreting the results of your analysis. It involves drawing conclusions from the data and making recommendations based on your findings. It is necessary to communicate your results clearly, often using visualizations to help stakeholders understand the insights.