DEV Community: Judy

Data Engineering for Beginners: A Step-by-Step Guide

Judy — Mon, 06 Nov 2023 10:00:21 +0000

Data engineering has become critical to the data ecosystem due to the influx of massive amounts of data from a variety of sources. Organizations are also looking to establish and expand their data engineering teams.
Some data professions, such as analyst, do not require prior expertise in the industry if you have excellent SQL and programming skills. However, prior knowledge in data analytics or software engineering is often beneficial for breaking into data engineering.

What is Data engineering?

Data engineering is a subfield of data science concerned with the practical applications of data analysis and acquisition. Like other areas of engineering, it is concerned with the application of data science in the actual world.
Data engineering has nothing to do with experimental design. It is more concerned with establishing systems for improved information flow and access.

What Does a Data Engineer Do?

A data engineer is responsible for creating and maintaining data architectures (such as a database). They are in charge of data collecting and the processing of raw data into useable data.
You can't collect data until you have a data engineer. Companies demand data engineers to be knowledgeable with SQL, Java, AWS, Scala, and other programming languages.
A background in backend development or programming is required for data engineering.
As a data engineer, you'll be responsible for managing data collection, storage, and processing for future use.

Data Engineering Concepts

Data Sources and Types

The data coming from these sources can be classified into one of the three broad categories:

Unstructured data
Lacks a well-defined schema. For example; Images, videos and other multimedia files, website data
Semi-structured data
Has some structure but no rigid schema. Typically has metadata tags that provide additional information. For example; JSON,XML data, emails, and zip files.
Structured data
Has a well-defined schema. For example; spreadsheets.

Data Repositories: Data Warehouses, Data Lakes, and Data Marts

The raw data collected from various sources is staged in a suitable repository.
There are two data processing systems, OLTP and OLAP systems:

OLTP or Online Transactional Processing systems are used to store day-to-day operational data for applications such as inventory management. OLTP systems include relational databases that store data that can be used for analysis and deriving business insights.
OLAP or Online Analytical Processing systems are used to store large volumes of historical data for carrying out complex analytics. In addition to databases, OLAP systems also include data warehouses and data lakes (more on this shortly).

The source and type of data frequently influence the choice of data store.

- Data warehouses: A data warehouse is a centralized repository for receiving data.

- Data lakes: Data lakes enable the storage of all data kinds in their raw form, including semi-structured and unstructured data. ELT processes (which we will explain subsequently) frequently end up in data lakes.

- Data mart: A data mart is a smaller subset of a data warehouse that is targeted to a certain business use case.

- Data lake houses: Recently, data lake homes have gained popularity because they combine the freedom of data lakes with the structure and organization of data warehouses.

Data Pipelines: ETL and ELT Processes

Data pipelines encompass the data's travel from source to destination systems via ETL and ELT operations.

The ETL (Extract, Transform, and Load) process consists of the following steps:
Data extraction from several sources
Clean, validate, and standardize data before transforming it.
Load the data into a database or a destination application.
The destination of ETL processes is frequently a data warehouse.

ELT(Extract, Load, and Transform) is a variant of the ETL process in which the phases are reversed: extract, load, and transform instead of extract, transform, and load.
That is, before any modification is conducted, the raw data gathered from the source is loaded into the data repository. This enables us to apply modifications tailored to a certain application. Data lakes are the final endpoint of ELT procedures.

Tools Data Engineers Should Know

Dbt (data build tool) for analytics engineering
Apache Spark for big data analytics and distributed data processing framework.
Airflow for data pipeline orchestration.

-Cloud computing fundamentals and experience working with at least one cloud provider, such as AWS or Microsoft Azure.

In conclusion, Data engineering is a vast field. And there is a high need for persons with this skill set. It only takes one step, so begin your learning adventure right away.

The Complete Guide to Time Series Models.

Judy — Fri, 27 Oct 2023 18:57:56 +0000

Time series data is everywhere in our lives. It can be found in almost any domain: monitoring, sensors, stock prices, weather forecasts, exchange rates, application performance, and a plethora of other metrics on which we rely in our professional and personal life.

What is time series data?

Time-series analysis refers to the technique and mathematical tools used to examine time-series data in order to discover not only what happened but also when and why it happened, as well as what is most likely to happen in the future.

‌‌Types of Time-Series Analysis

Exploratory analysis
When you wish to describe what you observe and why you see it in a given time series, an exploratory analysis can help. It requires breaking down the data into trends, seasonality, cyclicity, and abnormalities. ‌‌
We can explain what each component symbolizes in the real world and, maybe, what caused it once the series has been deconstructed. This is not as simple as it appears and frequently involves spectrum decomposition to identify any specific frequency of recurrences and autocorrelation analysis to determine whether present values are dependent on prior values.
Curve fitting
Because time series is a discrete set, you can always tell how many data points are in it.
But what if you want to know the value of a time-series parameter at a point in time that your data does not cover?

To address this question, we need to add a continuous set—a curve—to our data. This can be accomplished in a variety of methods, including interpolation and regression. The former is a perfect match for parts of the given time series and is mostly useful for guessing missing data points. The latter, on the other hand, is a "best-fit" curve, which requires you to make an informed guess about the form of the function to be fitted (e.g., linear) and then modify the parameters until your best-fit criteria are met.

Forecasting
The process of generalization from sample to whole is known as statistical inference. It can be done over time with time-series data, allowing for future predictions or forecasting: from extrapolating regression models to more complex techniques involving stochastic simulations and machine learning.
Classification and segmentation
Classification is the process of identifying patterns in a series and assigning them to one of several classes. Segmentation, on the other hand, is the process of dividing a time series into a number of segments based on some specified criterion. ‌‌‌‌‌‌

Time series data visualization

Time series data visualization is often conducted with specialist tools that offer users a variety of visualization kinds and formats from which to pick. Let's look at some of the most popular data visualization methods.

Time series graph
Time series graphs, also known as time series plots, are the most commonly used data visualization tool for illustrating data points at a temporal scale where each point corresponds to both time and the unit of measurement.
Real time graph
Time series data is displayed in real time using real time graphs, often known as data streaming charts. This means that a real-time graph will refresh automatically every few seconds or when a new data point is received from the server.

Data models used for time series data

Autoregressive (AR) models

AR model is a representation of a form of random process, it is used to describe data reflecting time-varying processes such as changes in weather, economics, and so on.

Integrated (I) models

Integrated models are made up of a series of random walk components. These series are called integrated because they are the summation of weakly steady components.

Moving-average (MA) models

Univariate time series are modeled using moving-average models. The output variable in MA models is linearly dependent on the current and various historical values of an imperfectly predicted (stochastic) factor.

Autoregressive moving average (ARMA) models

ARMA models combine the AR and MA classes, with the AR part regressing the variable on its own historical values and the MA part modeling the error term as a linear mixture of error terms happening concurrently and at different times in the past. ARMA models are commonly employed in analytics for forecasting future values in a series.

Autoregressive integrated moving average (ARIMA) models

ARIMA models are a generalization of an ARMA model and are used when data show evidence of non-stationarity, where an initial differencing step, corresponding to the integrated part of the model, can be applied one or more times to eliminate the mean function's non-stationarity.

Both the ARMA and ARIMA models are commonly employed for analytics and forecasting future values in a series.

Autoregressive fractionally integrated moving average (ARFIMA)

ARFIMA models, in turn, generalize ARIMA models (or, more broadly, all three fundamental types) by allowing non-integer differencing parameter values. ARFIMA models are commonly used to simulate so-called long memory time series, in which deviations from the long-run mean dissipate more slowly than exponential decay.

Autoregressive conditional heteroscedasticity (ARCH)

The ARCH model, for example, describes the variance of the present error term or innovation as a function of the actual sizes of error terms in earlier time periods.

Challenges in Handling Time-Series Data

While time series data provides great insights, it also brings distinct obstacles that must be handled during analysis.

Dealing with missing values
Time-series data frequently contains missing or partial values, which can impair analysis and modeling accuracy. Depending on the nature of the data and the level of missing values, several techniques such as interpolation or imputation can be used to handle missing values.
Overcoming noise in time-series data
Noise in time series data refers to random fluctuations or anomalies that can conceal underlying patterns and trends. Moving averages and wavelet treatments, for example, can assist minimize noise and extract the most important information from data.

In conclusion, time series analysis attempts to understand how patterns develop over time. These patterns aid in the generation of exact estimates for things like future sales, GDP, and global temperatures.
One thing to keep in mind is that time series models take into account the fact that time flows in only one direction.
Events that are near in time often have a greater link than more distant discoveries.
Time-series data, like all data, has random oscillations. This randomness has the potential to hide the underlying patterns. Smoothing techniques help to wipe out these swings, revealing the trends and cycles more clearly.
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How to use python in data visualization for credit risk assessment.

Judy — Thu, 19 Oct 2023 08:07:42 +0000

Most individuals rely on credit to finance vehicles, real estate, student loans, and the start-up of small enterprises. Assessing credit risk data is crucial for financial institutions when deciding whether to offer the loans.

Dataset used for credit assessment was sourced from kaggle.com.com. Therefore proceed to load the relevant libraries in python which will be used for credit risk assessment.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

Data is then loaded on python as csv file;

data = pd.read_csv('credit_risk_dataset_test.csv')

After the data is loaded, data cleaning is done next to format any inconsistencies and ensure the data is in order to avoid any errors.
First check the data types for the columns as

print(data.dtypes)

In this data set there are a mix types of data which may make data manipulation a bit hard hence convert integers to float. I prefer using floats as it allows me to represent data on plots accurately and ensures compatibility with various libraries.

data['person_income'] = data['person_income'].astype(str)
data['loan_amount'] = data['loan_amount'].astype(str)
data['loan_int_rate'] = data['loan_int_rate'].astype(str)
data['debt_to_income_ratio'] = data['debt_to_income_ratio'].astype(str)

Once the data is cleaned, data visualisation is next. Histograms will be used to show visual representation of ages. In this data set, shows most people who have loans range 20-40 years. Highest number are in their 20s.

plt.figure(figsize=(8, 6))
sns.histplot(data['person_age'], bins=20, kde=True)
plt.title('Distribution of Person Age')
plt.xlabel('Age')
plt.ylabel('Frequency')
plt.show()

Next, creation of boxplot to visualize the distribution of loan amounts by loan grade.Each box in the plot represents a specific loan grade, and it shows the distribution of loan amounts for that grade. The boxplot provides information about the median, quartiles, and any potential outliers in the data for each loan grade, making it a useful tool for understanding the distribution of loan amounts across different grades. Grade F loans have a median income that is superior to other grades.

plt.figure(figsize=(10, 6))
sns.boxplot(data=data, x='loan_grade', y='loan_amount')
plt.title('Loan Amount Distribution by Loan Grade')
plt.xlabel('Loan Grade')
plt.ylabel('Loan Amount')
plt.show()

The next step,use of scatter plots to provide insights into the relationship between a borrower's debt-to-income ratio, interest rate, and whether they defaulted on their loan. A higher debt-to-income ratio indicates that the borrower has a larger proportion of their income committed to debt payments. In the data set, loans with lower interest rate, lower debt to income ratio have not defaulted while loans with higher interest rate have defaulted.

plt.figure(figsize=(8, 6))
sns.scatterplot(data=data, x='debt_to_income_ratio', y='loan_int_rate', hue='cb_person_default_on_file')
plt.title('Debt-to-Income Ratio vs. Interest Rate')
plt.xlabel('Debt-to-Income Ratio')
plt.ylabel('Interest Rate')
plt.legend(title='Default')
plt.show()

Count plots of homeownership will be done to provide a visual representation of different types of home ownership among borrowers. In this data set the "RENT" is the most common home ownership type among borrowers, followed by "MORTGAGE." The "OWN" category has the fewest borrowers.
This plot helps one understand the characteristics of borrowers and can help identify potential factors that impact credit risk.
In this case, most of the borrowers are renters, followed by those with mortgage. The least borrowers are home owners

plt.figure(figsize=(8, 6))
sns.countplot(data=data, x='person_home_ownership')
plt.title('Count of Home Ownership Types')
plt.xlabel('Home Ownership')
plt.ylabel('Count')
plt.xticks(rotation=45)
plt.show()

Lastly, relationship between person income and loan amount is established by using scatterplot. In the data set, most borrowers are low income earners and have different intent use for the loans.

plt.figure(figsize=(10, 6))
sns.scatterplot(x='person_income', y='loan_amount', data=data, hue='loan_intent', palette='Dark2')
plt.xlabel('Person Income')
plt.ylabel('Loan Amount')
plt.title('Person Income vs. Loan Amount')
plt.show()

Python being an open source gives one access to different libraries that enables you to handle large data sets and easily customize to what you prefer.

To view the output for the above, visit github.com

Thanks for reading!

Exploratory Data Analysis using Data Visualization Techniques.

Judy — Tue, 10 Oct 2023 18:19:16 +0000

What is Exploratory Data Analysis?

Exploratory data analysis is a process used to analyze and summarize datasets.
Using various statistical and graphical tools, EDA tries to find patterns, detect anomalies, test hypotheses, and validate assumptions. It is an important part of the data science process because it allows analysts to obtain a thorough grasp of their data before going on to more advanced modeling and machine learning tasks.

Steps involved in Exploratory Data analysis

Data Collection
The data that will be evaluated is collected as the first step in the EDA process. Data can be obtained from a variety of sources, including structured databases, APIs, and even web scraping. To ensure compatibility with the analysis tools you intend to employ, it is critical to understand the many types of data sources, as well as their formats and architectures.
Data Cleaning
Once the data has been acquired, it must be cleaned and preprocessed to assure its quality and dependability. Handling missing numbers, deleting duplicates, changing data types, and detecting and addressing outliers are all examples of this process.
Data Exploration
Data exploration is the process of studying a dataset using various statistical and graphical approaches in order to discover the underlying structure, relationships, and trends in the data. This process is divided into three parts: univariate, bivariate, and multivariate analysis.

Data Visualization

Data visualization facilitates the efficient communication of insights and patterns identified throughout the exploratory data analysis process. Choosing the correct style of visualization, following best practices, and utilizing popular visualization libraries can all help to increase the impact of your study.

Choosing the Right Type of Visualization

The suitable visualization is determined by the nature of the data and the insights you wish to express. Bar charts, line charts, pie charts, scatter plots, and heatmaps are examples of common visualization types. Each of these visualizations has a specific function, such as comparing categories, exhibiting trends through time, or demonstrating relationships between variables.

Visualization Techniques Used for Exploratory Data Analysis.

Several visualization tools and techniques are in use. Here are the mostly used for the same.

Univariate Analysis

Univariate analysis examines the distribution, central tendency, and dispersion of a single variable. This study aids in comprehending the unique properties of each variable in the dataset.

Histograms
Histograms are graphical representations of a variable's distribution that divide data points into bins based on their values. Histograms aid in the identification of the distribution's form, any gaps or clusters, and probable outliers.
Box plots
Box plots show the quartile distribution of a variable, showing the median, interquartile range (IQR), and any outliers. Box plots show the dispersion of a variable in a compact manner.

Bivariate Analysis

Involves examining the relationship between two variables, exploring potential correlations, trends, or patterns between them.
Below are bivalents plots used for EDA;

Correlation plots or Heatmaps
Heatmaps use color intensity to depict the strength of a relationship between many variables in a matrix format. This visualization aids in quickly recognizing groups of similar variables.
Bar Graphs
They are used to compare nominal or ordinal data. They are helpful for recognizing trends.

Multivariate Analysis

Multivariate analysis examines the relationships among three or more variables simultaneously, providing a more holistic view of the data.
Below are multivariate analysis techniques

Multiple linear regression
A dependence method that examines the relationship between one dependent variable and two or more independent variables. A multiple regression model will tell you how well each independent variable correlates with the dependent variable. This is useful because it allows you to forecast future outcomes by understanding which elements are likely to impact a specific event.
Multiple logistic regression
The chance of a binary event occurring is calculated (and predicted) using logistic regression analysis. A binary result has only two possible outcomes: the event occurs (1) or it does not occur (0). So, logistic regression can forecast the likelihood of a given situation based on a set of independent factors. It is also employed in classification.
Multivariate analysis of variance (MANOVA)
MANOVA is a statistical method for determining the effect of many independent factors on two or more dependent variables. It's vital to remember that the independent variables in MANOVA are categorical, whereas the dependent variables are metric.
In MANOVA analysis, you look at different combinations of independent variables to see how they differ in their influence on the dependent variable.
Factor analysis
Factor analysis is an interdependent technique for reducing the number of variables in a dataset. Finding patterns in data might be tough if you have too many variables. Simultaneously, models built with too many variables are vulnerable to overfitting. Overfitting is a modeling error that occurs when a model fits a dataset too closely and specifically, making it less generalizable to future datasets and potentially less accurate in its predictions.
Factor analysis works by identifying groups of variables that have a high correlation with one another. These variables can then be merged to form a single variable. Factor analysis is frequently used by data analysts to prepare data for further examination.
Cluster analysis
Cluster analysis is used to group similar items within a dataset into clusters. When grouping data into clusters, the aim is for the variables in one cluster to be more similar to each other than they are to variables in other clusters.
Cluster analysis helps you to understand how data in your sample is distributed, and to find patterns.

Exploratory Data Analysis tools and libraries

Python Libraries

Commonly used python libraries are:

Pandas
Referred as pd. It used for data analysis and manipulation
Numpy
Referred as np. It is used for numerical computing, offering support for arrays and mathematical functions.
Matplotlib
Referred as plt. It used for creating static, interactive, and animated visualizations.
Seaborn
Referred as sns. It is a statistical data visualization library based on Matplotlib, providing a high-level interface for creating informative and attractive visualizations.

R Libraries

R is another popular programming language for data analysis that has a robust ecosystem of EDA modules. R libraries that are regularly used include:

dplyr
A data manipulation package that provides a consistent collection of methods for filtering, sorting, and aggregating data.
ggplot2
A sophisticated and versatile data visualization library based on the Grammar of Graphics, allowing complex visualizations to be created with minimal code.

Data Visualization Tools

Aside from programming languages and libraries, EDA use a variety of data visualization tools. These tools provide an easier-to-use interface for producing and customizing visualizations. Among the most popular data visualization tools are:

Tableau
A popular data visualization software that enables users to create interactive and shareable dashboards.
Power BI
A Microsoft business analytics service that provides data visualization and reporting capabilities.

In conclusion, EDA is an important step in the data analytics process because it allows data scientists to understand the features of the data, uncover patterns and relationships, and make educated judgments in following analysis or modeling stages. Data scientists may extract useful insights from their data and construct more effective and interpretable machine learning models by utilizing various EDA approaches and tools.

Always keep in mind, EDA is an iterative and exploratory process, and that constantly improving your research will result in a greater knowledge and more robust findings. You will develop a deeper understanding for the data and its underlying patterns as you gain expertise in EDA, making you a more effective data scientist and analyst.

Data Science for Beginners: 2023 - 2024 Complete Roadmap

Judy — Sat, 30 Sep 2023 08:52:47 +0000

Data science is an emerging field that has blended statistical, programming, and domain expertise to get insights from data.

What is Data Science?
Data science is a field of study that involves extracting knowledge and insights from large and complex datasets using techniques like data mining, statistical analysis, machine learning and visualization.

Who is a Data Scientist?
A person responsible for collecting, cleaning and analyzing large data sets to extract valuable insights. They work with unstructured, semi structured and structured data to find patterns that help them solve problems and predict future events.

Why Data Science?
The pace of change globally is really rapid. There are new technologies being introduced every day. To be competitive in such a situation, businesses must employ innovative strategies. Big data and data science are now important instruments for corporate growth as a result of this.
Data science is applicable to all industries, but most significant in those that generate enormous amounts of raw data on a regular basis, such as healthcare, retail, and finance.
Companies are now collecting more data on their customers to help them gain insights into their customer behaviors and preferences that aid in making better decisions.

How easy is Data science for beginners?
Data science is an exciting and rewarding field for a beginner to learn however it has some challenges;

Understanding the mathematical and statistical concepts that underpin many of the approaches used in data science like probability, statistics, linear algebra, and optimization can be difficult to grasp, especially for people who have never studied them before.
Learning the numerous tools and technology used in data science including programming languages like Python and R, as well as libraries and frameworks like NumPy, Pandas, and sci-kit-learn. These tools can be overwhelming for those who are new to programming and are unsure how to begin with data science. Programming language tools necessitate familiarity with concepts such as variables, functions, and loops.

Despite these challenges,beginner in data science can still succeed by being persistent, curious, eager to learn and practice. With time they become proficient at it.

How do you start learning Data science as a beginner?
As a beginner, learning data science entails learning the essential tools and technologies, comprehending the underlying concepts, and applying and implementing what you have learned. You may create a good foundation in data science and become adept in the discipline with perseverance and devotion. So, if you're not sure where to start learning data science, here's a step-by-step data science roadmap for beginners to get you started.

1.Learning Fundamentals and SQL
If you are a beginner with no background in statistics or mathematics, you can familiarize yourself with probability and statistical concepts.
You then can proceed to learn a query language such as SQL. SQL (Structured Query Language) is a database management and manipulation programming language. It is an essential ability for any data scientist since it helps you to obtain, filter, and combine data from a variety of sources. SQL learning tools for beginners include online classes, tutorials, and textbooks. You can also hone your skills by completing SQL exercises and projects. You can go to the following phase once you have a solid foundation in SQL.

2.Learning Programming language like Python/R
The next stage in data science for beginners is to learn a computer language such as R or Python. R and Python are popular data science programming languages for data manipulation, visualization, and machine learning.
To begin, select one of the languages and begin studying the fundamentals. Variables, data types, loops, and functions are examples of such ideas. There are numerous tools accessible for studying R or Python, including online courses and tutorials on the greatest data science websites. As you continue, you will be able to delve into more sophisticated themes and improve your talents. You can access online courses on Microsoft Azure Learning, AWS learning platform udemy and coursera.

3.Learning Data visualisation tool-Tableau/Power BI
Once you've mastered programming and data manipulation, the next step as a data science beginner student is to study a visualization tool such as Power BI or Tableau. To share your data insights, you can use these tools to create dynamic and visually appealing charts, graphs, and dashboards.
To get started, select one of these tools and begin studying the fundamentals. Topics like as producing charts and graphs, constructing dashboards, and connecting to data sources may be covered. There are numerous resources accessible for learning visualization technologies, such as online classes, tutorials, and documentation. As you continue, you will be able to explore more advanced features and approaches.

4.Learning statistics for Machine Learning
After learning a programming language and a visualization tool, the next stage is to understand fundamental statistics for machine learning. Machine learning is a data science subject that involves utilizing algorithms to learn from and predict data. To begin, you should understand fundamental concepts such as probability, statistics, and linear regression. There are numerous resources accessible for understanding the fundamental statistics of machine learning. Data science online courses, tutorials, and textbooks are examples of resources. As you improve, you can delve into more advanced topics and hone your machine learning skills.

5.Learning Machine Learning Algorithms
After you've mastered fundamental statistics, the next step is to learn about machine learning methods. There are numerous algorithms used in machine learning, each with its own set of strengths and disadvantages. To begin, familiarize yourself with supervised and unsupervised learning like as decision trees, linear regression, and k-means clustering.

6. Practice and Implement your skills
The final stage in learning data science as a beginner is to put what you've learnt into practice. Working on projects and exercises to put your skills to use, as well as engaging in online groups and forums to learn from others and get feedback on your work, can all be part of it. You should also think about joining a data science group or club, which will provide you more possibilities to study and work with others. You can use real-world data sets to practice and apply your skills by exploring, visualizing, and analyzing the data with the tools and techniques you've learned. You can also experiment with creating machine-learning models and testing them on various data sets.

In conclusion, data science provides an amazing potential to have a significant influence in a variety of areas, ranging from healthcare to finance and beyond. Whether you want to be a data scientist, data analyst, machine learning engineer, or specialize in a particular topic, your commitment to mastering this profession will open doors to a world of opportunities. So, enjoy the journey of data science, adapt to new difficulties, and never stop learning. As a data science expert, your future is full with promise, innovation, and the opportunity to shape a data-driven world.