DEV Community: Vee

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

Vee — Mon, 30 Oct 2023 21:00:25 +0000

In today's data-driven world, the effective management and processing of data are critical for organizations and individuals alike. Data engineering plays a crucial role in this process, enabling the collection, storage, and transformation of data into valuable insights. If you're a beginner eager to dive into the world of data engineering, this step-by-step guide is here to help you get started.

Data Engineering

Data engineering is the foundation of data-driven decision-making. According to Wikipedia, it refers to the building of systems to enable the collection and usage of data. It involves designing, building and maintaining data infrastructure and platforms, and making data accessible and usable for data scientists, analysts, and decision-makers. Without data engineering, raw data remains untamed and untapped, limiting the potential for valuable insights.

Data engineers play an important role in an organization’s success through providing easier access to data that data scientists, analysts, and decision-makers need to do their jobs. To create scalable solutions, data engineers mostly require programming and problem-solving skills.

How to develop a data engineering career

To become a data engineer, you need to be conversant with the following fundamentals:

Programming basics
You need to understand the basic of python programming including the syntax, operators, variables, data types, loops and conditional statements, data structures and standard libraries such as Numpy and Pandas. SQL is also fundamental when working with databases. Other programming languages you will need as you build on your skillset are Java and Scala which are also used in data processing.
Database Knowledge
Databases rank among the most common solutions for data storage. You should be familiar with both relational and non-relational databases, and how they work. For relational databases, you need to learn the querying syntax and commands in SQL including the keys, joins and subqueries, window functions and normalization. For non-relational databases that deal with unstructured data, MongoDB and Cassandra are vital to learn.
ETL (extract, transform, and load) systems
ETL is the process by which you’ll move data from databases and other sources into a single repository, like a data warehouse. Common ETL tools include Xplenty, Stitch, Alooma, and Talend.
Data processing with Apache Spark
Data Processing refers to converting raw data into meaningful information that is machine readable. Apache® Spark™ is a fast, flexible, and developer-friendly open-source platform for large-scale SQL, batch processing, stream processing, and machine learning. Data engineers constantly work with big data and therefore incorporating Spark into their applications helps them rapidly query, analyze, and transform data at scale. As a data engineer it will then be vital to comprehend Spark architecture, RDDs in spark, working with Spark data frames, understand Spark execution, Spark SQL, broadcast and accumulators
Apache Hadoop-Based Analytics
Apache Hadoop is an open-source platform that is used to compute distributed processing and storage against datasets. They assist in a wide range of operations, such as data processing, access, storage, governance, security, and operations. You'll need to understand MapReduce architecture, working with YARN and how to use Hadoop on the cloud for example, AWS with EMR.
Data Warehousing with Apache Hive
Data warehousing helps data engineers to aggregate unstructured data, collected from multiple sources. It is then compared and assessed to improve the efficiency of business operations. Apache Hive is a data warehouse infrastructure built on top of Hadoop. It provides tools to enable easy data ETL, a mechanism to put structures on the data, and the capability for querying and analysis of large data sets stored in Hadoop files. It is important to learn the Hive querying language, managed visa vis extensional tables, partitioning and bucketing, and types of file formats.
Automation and scripting: Automation is a necessary part of working with big data simply because organizations are able to collect so much information. You should be able to write scripts to automate repetitive tasks.
Cloud computing
Cloud computing stores data remotely, accessible from nearly any internet connection. This makes it a flexible and scalable environment for businesses and professionals to operate without the overheads of maintaining physical infrastructure. Cloud computing also make collaboration in data science teams possible. It is therefore vital to understand cloud storage and cloud computing as companies are increasingly shifting to cloud services. Beginners may consider a course in Amazon Web Services (AWS) or Google Cloud.

Conclusion

Data engineering is the backbone of successful data analysis and decision-making. As a beginner, you now have a solid foundation to start your data engineering journey. Remember to continually explore new tools, technologies, and best practices as the field evolves. With dedication and a curious mindset, you'll be well on your way to becoming a proficient data engineer.

The Complete Guide to Time Series Models

Vee — Tue, 24 Oct 2023 16:54:49 +0000

Have you ever wondered how meteorologists are able to predict future weather, or how businesses predict future trends, and easily detect changes in the market? Those are just a few eye openers of how Time Series works in various aspects of our daily lives. In this guide, we are going to get an understanding of Time series models work and the way they apply in our daily life.

Understanding Time series

According to Wikipedia, a time series is a series of data points indexed in time order. A time series model is a type of machine learning model used to analyze and forecast the future based on verified previous data observed at regular intervals (Engineering Statistics Handbook, 2010). In this model, time is usually the independent variable.

Time series models use non-stationary data. Non-stationary data is data that keeps fluctuating with time. The data is analyzed to give insights on different trends over time. This is what makes time series models part of predictive analytics

Characteristics of time series models

Seasonality: Refers to patterns that occur regularly ( weekly, quarterly, monthly or annually) in the data due to various seasonal factors. For example, ice cream sales are higher during summer and low during winter. The figure below is an illustration of seasonality

Image: Marco Peixeiro

Stationarity: This is the state of statistical properties of a time series not changing over time. This means a constant mean, variance and covariance is independent of time. Stationarity is very crucial as it influences how data is perceived and predicted. The following diagram shows an example of a stationary process:

Image: Marco Peixeiro

Autocorrelation: This is the degree of similarity between a given time series and a lagged version of itself over a certain period of time. It measures the relationship between a variable's current value and its past values. An autocorrelation of +1 represents a perfect positive correlation, while an autocorrelation of -1 represents a perfect negative correlation. For instance, in businesses, autocorrelation can be used to evaluate how past prices are likely to influence future prices.

Image: Marco Peixeiro

Types of Time series analysis

There are various types of time series analysis used for different purposes. They include:

Forecasting: It is used to predict the future. It utilizes past data as a model for future data to predict future events.
Explanative: It strives to understand the data and the relationships within it including cause and effect.
Curve fitting: It plots the data along a curve to examine the relationships between variables in data.
Segmentation: It attempts to understand the underlying properties of the source information by splitting the data into segments.
Classification: It identifies and assigns categories to the data.

Time series models

Time series models are classified into three broad categories:

Combinations of these three models brings forth autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) models.

Autoregressive moving average (ARMA) model

The ARMA model is a regression model where the dependent variable is a linear function of past values of both the dependent variable and the error term. The order of an ARMA model is represented by ‘p’ for the autoregressive part and ‘q’ for the moving average part. For example, if p=0 and q=0, then it means that we are predicting time-step (t) based on time-step (t) only. If p=n and q=m, then we are predicting time-step (t) based on n past time-steps of the dependent/response variable and m past time-steps of the error term.

Autoregressive integrated moving average (ARIMA) model
The autoregressive integrated moving average (ARIMA) model is a generalization of the ARMA model. The ARIMA model is a regression model in which the dependent variable is a linear function of past values of both the dependent variable and the error term, where the error term has been differentiated ‘d’ times. The model's goal is to predict future securities or financial market moves by examining the differences between values in the series instead of through actual values.

Seasonal autoregressive integrated moving average (SARIMA) model
SARIMA is a type of time-series forecasting model that takes into account both seasonality and autocorrelation. SARIMA models are based on a combination of differencing I(d), autoregression model AR(p), moving average model MA(q) and seasonality S(P, D, Q, s), where s is simply the season’s length.

Image: Utkarsh Soni,Helical IT Solutions(2023)

SARIMA models are generally considered to be more accurate than other types of time-series forecasting models, such as ARIMA models. SARIMA models are also relatively easy to interpret and use. The SARIMA model can be used to forecast demand for a product or service over the course of a year, forecasting stock prices and weather patterns.

Applications of time series models

Finance − It includes sales forecasting, inventory analysis, stock market analysis, price estimation.

Retail - Retailers may apply time series models to study how other companies’ prices and the number of customer purchases change over time, helping them optimize prices.

Meteorology − Time series models can be used in temperature estimation, climate change, seasonal shift recognition, and weather forecasting.

Healthcare - Time series models can be used to monitor the spread of diseases by observing how many people transmit a disease and how many people die after being infected.

Conclusion

In this article, you have had an introduction to the fascinating world of time series models. Understanding time series data, its components, and the models used to analyze it is a crucial skill in today's data-driven world. For further study, consider the following resources:
The complete guide to Time series models

Different types of Time-series Forecasting Models

Time series analysis

Exploratory Data Analysis using Data Visualization techniques

Vee — Wed, 11 Oct 2023 23:42:56 +0000

Exploratory data analysis (EDA) is the process of studying data using visualization and statistical methods to understand the data. In other words, it is the first look on your data. It is a vital step before you begin with the actual data analysis. EDA helps to discover relationships within data, identify patterns and outliers that may exist within the dataset. Data scientists use EDA to ensure the results they produce are valid and applicable to any desired business outcomes and goals.

Objectives of EDA

The main objectives of EDA are to:

Confirm if the data is making sense in the context of the problem being solved. In the case where it doesn't, we come up with other strategies such as collecting more data.
Uncover and resolve issues on data quality such as, duplicates, missing values, incorrect data types and incorrect values.
Get insights about the data, for example, descriptive statistics.
Detect anomalies and outliers that may cause problems during data analysis. Outliers are values that lie too far from the standard values.
Uncover data patterns and correlations between variables.

Types of EDA

Exploratory data analysis is classified into three broad categories namely:
Univariate
Bivariate
Multivariate

Steps in EDA

The following is a step-by-step approach to undertaking an exploratory data analysis:

Data Collection

Gather relevant and sufficient data for your project. There are various sites online you could get your data from irrespective of the sector you're in. Here are a few examples to check out: Kaggle, Datahub.io, BFI film industry statistics

2.Familiarize with data

This step is important as it helps you to determine whether the data is adequate for the analysis about to be done.

3.Data cleaning

This where any missing values, outliers and duplicates are identified and removed from the dataset. Also, data that is irrelevant for the anticipated analysis is removed at this stage.

4.Identify associations in the dataset

Look for any correlations between variables. You can use a heatmap or scatterplots to make it easier for you to identify the correlations.

Example: Exploratory Data Analysis using NYC Citi Bike data.

We will now perform an exploratory data analysis on NYC Citi Bike data to get a better understanding of the process. You can access the data here.

1.Import data

The first step is to import all the modules you are going to use in your project. In this case, we will need pandas for data wrangling, seaborn for data visualization. This is how I would do it:

`import pandas as pd
import seaborn as sns

Then import your dataset. If you're using Google colab, this is how you would load the data:

from google.colab import files uploaded = files.upload()

You will then read in the data as a pandas data frame like this:

2.Get an overview of the data

You can approach this in various ways. For example, using .info() helps us to know the data types, number of columns, column names, and number of values in the data frame. The following is an example:

The other alternative is to use .describe(). This gives you a statistical summary of the data. Here's an example:

3.Visualize the distribution for trip duration
This will help us to have a glimpse of how long most trips took. Using seaborn, this is how I would do it:

# visualize distribution for trip duration sns.histplot(data['tripduration'])
Here's the sample output:

From the output, it is evident that most trips were ranging within 10 minutes.

4.Visualize correlation between gender and trip duration

# checking for association between tripduration and gender using scatterplots sns.pairplot(data[['tripduration', 'gender']])

Sample output is as follows:

5.Calculate the percentage of subscribers

we need to find out the share of subscribers from the total number of riders in New York city. Here's how to find out:

6.Evaluate how trip length varies based on trip start time

data['hour'] = data.starttime.apply(lambda x: x[11:13]).astype('str') data

# visualize correlation sns.scatterplot(x= 'hour', y= 'tripduration', data = data, hue= 'usertype')

The output is as follows:

7.Determine the bike stations where most trips starts

First we get the count of bike stations and store the output as a new data frame. We we then drop the duplicates from the original data frame then merge the two new data frames for visualization.

# Get the count of trips from each station new_data = data.groupby(['start station id']).size().reset_index(name= 'counts')

#remove duplicate values from the start station id column temp_data = data.drop_duplicates('start station id')

# left join to merge new_data and temp_data dataframes newdata2 = pd.merge(new_data, temp_data[['start station id', 'start station name', 'start station latitude', 'start station longitude']], how= 'left', on= ['start station id'])

#install folium !pip install folium import folium

# initialize a map m = folium.Map(location=[40.691966, -73.981302],tiles= 'OpenstreetMap', zoom_start= 12) m

The output is as follows:

Conclusion

EDA is very crucial as it affects the quality of the findings in the final analysis. The success of any EDA is dependent on the quality and quantity of data, the type of tools and visualization used, and proper interpretation by a data scientist.

Understanding data science. A complete beginner roadmap

Vee — Sun, 01 Oct 2023 16:52:32 +0000

Imagine you are a CEO in an ecommerce platform and you want to understand what your customers think about your services. First you need to do a survey to get views and opinions from your customers. Next step will be to explore your data to draw meaningful insights. The insights will now inform how the customer service will be improved.
That is data science for you. Let's get started and understand what this term "data science" is all about.

What is data science

Data science is the process of drawing meaningful insights from large data sets. It acts as a third eye by foreseeing problems and creating solutions before they occur. This is how it acts as a third eye.

Based on the field you're in, data can help you find solutions to your problems. For instance, in an ecommerce company, data can help you know:

What products are more preferred by customers
How to improve customer service

The roles of a Data Scientist
As a data scientist in an organization, your roles will range from:

identifying crucial areas for research
knowing where to get your data from
data cleaning, data exploration
creating statistical models
presenting the findings to stakeholders

Application areas

Data science is crucial in the decision making in all aspects of our daily lives. The following are some applications of data science in various sectors:

Education: Data science is being used by teachers to assess the comprehension of students in various units in order to come up with better teaching mechanisms to improve the students' performance.
Business: Data science is used in businesses to predict future market trends, improve quality of products based on customer preferences and enhance product marketing based on customer previous purchase history and browsing behavior.
Meteorology: Data science is applied in this sector to improve the level of accuracy in weather forecasting to help save lives from extreme weather events.
Transportation: In the transport sector, data science is applied to help optimize routing, improve safety, and reduce emissions.
Energy: Data science is used in energy to optimize energy production and distribution, reduce costs, and improve efficiency.

Skills required in Data Science

To be a Data Scientist, you require skills in the following fields:
Programming languages :As a beginner you need to learn the basics of programming. In data science some of the programming languages include:

Statistics
You need have a foundation in calculus, linear algebra and statistics. Calculus is useful in learning how to design optimization algorithms for machine learning. Learning linear algebra helps you work with vectors and matrices. This will be very crucial in conducting analysis on data.

Machine Learning
This is a subfield of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic how humans learn, gradually improving accuracy.

Learning Guide

You might wonder where to get the resources to start learning. There are numerous resources available to get you started, including online courses, YouTube videos and Bootcamps. The following are a few resources you can check out:
https://youtu.be/ua-CiDNNj30?si=Ddwk1opxqIMbeeJd
https://youtu.be/rGx1QNdYzvs?si=ZvMVq6r9o8czTjFe
Here are a few tips for you to accelerate your learning:
Start with the basics- Try to learn only some things at a time. Focus on learning the basics of programming, statistics, and machine learning first.
Practice regularly- The best way to learn data science is by practicing. You can check out Hackerrank and kaggle for practice.
Always reach out for help- There are many online communities and forums where you can ask questions and get help from other data scientists.

Conclusion

Data science is a dynamic and rewarding field that opens up countless opportunities. Whether you're driven by curiosity, the potential for a lucrative career, or the desire to make data-driven decisions, data science has something to offer. With a growing job market and diverse roles, such as data wrangler, machine learning engineer, and business intelligence analyst, there's a niche for everyone in this exciting field.
As you embark on your data science journey, remember that continuous learning and hands-on experience will be your best allies. Enjoy the adventure!