Python, the data Science language
Nowadays, there are thousands of people who want to acquire digital skills to find better employment. One of the options available is to become a professional in data analysis, Data Science, or Data Analytics. Many individuals consider learning Python as the right path to secure a job as a data analyst. But is it enough to know the fundamentals of Python? Or what else is needed?
At Ubiqum Code Academy, we’ll briefly explain the role Python plays in the process of becoming a professional data analyst. Let’s start at the beginning.
What is Python?
Python emerged in the early 90s as a hobby for Guido Van Rossum – a Dutch engineer working at the Center for Mathematics and Computer Science in Amsterdam.
Inspired by the British group Monty Python, Guido chose the name Python for the new language that arose as an open-source software project.
Python, when compared to other programming languages, is simple, but this simplicity doesn’t imply limitations.
Python serves two primary purposes as a programming language.
On one hand, it’s a backend language for web application development, much like Java or PHP. Its use in software engineering, as a pure programming language, differs from the use described below. On the other hand, Python is used for data analysis; its libraries offer powerful tools for data processing and mathematical computation used in any data analysis process. Think of libraries as a set of ready-to-use tools that someone has already developed and offers you, ready for use. Instead of programming a function to perform a specific operation, you can simply use an already created function.
Examples of some of the most widely used Python libraries for data science include:
- Numpy (Handling vectors, matrices, and mathematical operations at high speed).
- Pandas (Data manipulation and cleaning).
- Plotly (Generating interactive visualizations).
- Scikit-learn (Data preprocessing, model selection, Machine Learning models, metrics).
- Category Encoders (Weighting and transformation of categorical data to continuous for use in Machine Learning),
among many others that are continually being created and enhanced as the data analysis community grows and shares knowledge.
One more note to clarify another use of Python in the field of data analysis is process automation. This involves automating processes that require complex decision-making. It’s trendy to call this field “Artificial Intelligence,” and although we may not entirely agree, this isn’t the place for discussion. This automation can be more or less complex and tailored to a specific problem or could involve the creation of a widely used Machine Learning algorithm.
Python is the most in-demand programming language for 2022 according to job postings on LinkedIn. Source: CodingNomads.
At Ubiqum, we exclusively use Python in the data analysis process. To better understand Python’s use in data analysis, it’s worth delving into what the data analysis process entails.
The Data Analysis Process and Python
The Data Analysis process consists of five steps and can be applied to simple or very complex problems, utilizing small data quantities, from a few hundred thousand to very large ones—millions, tens of millions, or billions of data points. However, it always follows a similar 5-step process:
- Formulate a business hypothesis that requires validation or refutation through data analysis.
- Create, clean, complete, and prepare a dataset with the available data.
- Model the business problem using Machine Learning algorithms to process the data.
- Obtain and validate results. Iterate steps 2, 3, and 4 until satisfactory results are achieved (train the model).
- Convert the obtained results into recommendations for business improvement.
This general data analysis process is supported by three disciplines and a programming language. Throughout any data analysis activity, concepts and tools from these three disciplines—Statistics, Probability Calculations, and Linear Algebra—are consistently utilized. The application of these disciplines is done through a programming language (Python or R) and the use of their libraries.
Why learn to program with Python?
In the context described, Python is the programming language that efficiently executes step 2, of the complete Data Analytics process.
Mainly Python is used to perform two main functions in this process:
EDA, Exploratory Data Analysis and FE. Feature Engineering.
Exploratory Data Analysis
Exploratory Data Analysis (EDA) is an approach in data analysis that involves examining, summarizing, and visualizing data sets to understand their main characteristics and patterns. EDA is an initial phase in the data analysis process where analysts aim to gain insights and understand the nature of the data before applying more advanced techniques or modeling.
Key aspects of EDA include:
Data Summarization: This involves calculating descriptive statistics such as mean, median, standard deviation, and variance to summarize numerical data. For categorical data, frequency counts and percentages help understand the distribution.
Data Visualization: Graphical representations like histograms, box plots, scatter plots, and bar charts are used to visually explore relationships, distributions, and trends within the data. Visualizations make it easier to identify outliers, patterns, and potential correlations.
Handling Missing Data: Identifying and addressing missing values within the dataset is a crucial part of EDA. Strategies might involve imputation (replacing missing values with estimated values) or deciding to exclude incomplete data.
Identifying Patterns and Relationships: EDA helps in understanding correlations between variables, detecting anomalies or outliers, and recognizing trends within the data.
Dimensionality Reduction: EDA can involve reducing the number of variables by employing techniques like principal component analysis (PCA) or feature selection to focus on the most significant variables.
EDA is an iterative process that guides data scientists and analysts in understanding the nature of the data, identifying potential issues or peculiarities, and making informed decisions about further analyses or modelling techniques to apply.
Feature engineering is the process of creating new variables (features) from existing data variables or transforming raw data into a format that enhances machine learning model performance. It involves extracting valuable information, selecting relevant features, and representing data in a more informative way to improve the accuracy and effectiveness of predictive models.
Key aspects of feature engineering include:
Creating New Features: Generating new variables by combining, transforming, or extracting information from existing features in the dataset. For instance, creating new features from date/time data, extracting keywords from text, or converting categorical variables into numerical representations (e.g., one-hot encoding).
Handling Missing Data: Addressing missing values by imputing them with mean, median, or other statistical methods, or creating new features to flag missing values.
Scaling and Normalization: Scaling features to a similar range or normalizing them to ensure uniformity, especially in cases where variables have different scales or units.
Encoding Categorical Variables: Converting categorical variables into a numerical format that machine learning models can interpret, such as one-hot encoding or label encoding.
Binning or Discretization: Grouping continuous variables into bins or categories to simplify complex data and capture non-linear relationships.
Feature Selection: Identifying and selecting the most relevant features by using techniques like correlation analysis, statistical testing, or employing algorithms that automatically rank or eliminate less important features.
Handling Outliers: Transforming or treating outliers in a way that mitigates their impact on model performance.
Effective feature engineering is essential as it directly influences the performance and efficiency of machine learning models. Well-engineered features can help models better capture patterns and relationships within the data, leading to more accurate predictions or classifications.
Python or R?
Finally, a few lines to clarify a debate that isn’t really one. History tells us that R is an academic language, extensively used in universities worldwide. R is a programming language designed for statistical analysis and data visualization, providing a wide array of statistical tools (linear and non-linear models, statistical tests, time series analysis, classification, clustering algorithms, etc.) and graphics.
In contrast, as mentioned earlier, Python is a software engineering language. Both are powerful and have their strengths and weaknesses. In the early days of data science, the first practitioners came from the academic world and hence preferred (and still prefer) the R language.
As the profession expanded and many software engineers joined the field, Python gained ground. So, the answer to ‘Python or R?’ is simple: both. At Ubiqum, you’ll learn both languages and become proficient to work in any data analysis department, whether your manager is a mathematician who prefers R or a telecommunications engineer who opts for Python. Ultimately, the essential aspect is learning to think like an analyst; the tools are interchangeable.
For more information, you can access the Data Analytics and Machine Learning course or request an interview with our career advisors by filling out the attached form.