Cybersecurity professionals are in high demand, and two of the most common roles in this field are Cyber Security Analyst and Cyber Security Specialist. While both roles involve protecting organizations from cyber threats, they have distinct differences in terms of responsibilities, required skills, and educational backgrounds. In this article, we will compare and contrast these two roles to help you determine which one is right for you.

Definitions

A Cyber Security Analyst is responsible for monitoring computer networks for security breaches, investigating security incidents, and installing security measures to protect computer systems and networks. They also analyze security risks and develop strategies to mitigate them.

On the other hand, a Cyber Security Specialist is responsible for designing, implementing, and maintaining security solutions to protect computer systems and networks. They work closely with other IT professionals to ensure that security measures are integrated into every aspect of an organization’s technology infrastructure.

Responsibilities

The responsibilities of a Cyber Security Analyst include:

• Monitoring computer networks for security breaches
• Investigating security incidents and determining the cause of the breach
• Installing and configuring security measures, such as Firewalls and Intrusion detection systems
• Developing and implementing security policies and procedures
• Conducting security Audits and risk assessments
• Providing training to employees on security best practices

The responsibilities of a Cyber Security Specialist include:

• Designing, implementing, and maintaining security solutions, such as firewalls, intrusion detection systems, and Encryption systems
• Conducting vulnerability assessments and penetration testing
• Developing security policies and procedures
• Providing guidance to other IT professionals on security best practices
• Investigating security incidents and determining the cause of the breach
• Staying up-to-date with the latest security threats and technologies

Required Skills

The required skills for a Cyber Security Analyst include:

• Knowledge of security protocols and standards, such as SSL/TLS, IPsec, and DNSSEC
• Familiarity with security tools and software, such as Firewalls, intrusion detection systems, and antivirus software
• Strong analytical and problem-solving skills
• Excellent communication and interpersonal skills
• Ability to work well under pressure and meet deadlines
• Strong attention to detail

The required skills for a Cyber Security Specialist include:

• Knowledge of security protocols and standards, such as SSL/TLS, IPsec, and DNSSEC
• Familiarity with security tools and software, such as firewalls, intrusion detection systems, and Encryption software
• Strong analytical and problem-solving skills
• Excellent communication and interpersonal skills
• Ability to work well in a team environment
• Strong attention to detail

Educational Background

A Cyber Security Analyst typically needs a bachelor’s degree in Computer Science, information technology, or a related field. Some employers may also require a master’s degree in cybersecurity or a related field. In addition, many Cyber Security Analysts hold certifications such as CompTIA Security+, Certified Information Systems Security Professional (CISSP), or Certified Ethical Hacker (CEH).

A Cyber Security Specialist typically needs a bachelor’s degree in computer science, information technology, or a related field. Some employers may also require a master’s degree in cybersecurity or a related field. In addition, many Cyber Security Specialists hold certifications such as Certified Information Systems Security Professional (CISSP), Certified Ethical Hacker (CEH), or Certified Information Security Manager (CISM).

Tools and Software Used

Both Cyber Security Analysts and Cyber Security Specialists use a variety of tools and software to protect computer systems and networks. Some of the most common tools and software include:

• Firewalls
• Intrusion detection Systems
• Antivirus and Anti-Malware software
• Encryption software
• Vulnerability Scanners
• Penetration Testing Tools

Common Industries

Cyber Security Analysts and Cyber Security Specialists are needed in a variety of industries, including:

• Government
• Healthcare
• Finance
• Retail
• Technology
• Education

Outlook

The outlook for both Cyber Security Analysts and Cyber Security Specialists is positive, with job growth expected to be much faster than average. According to the Bureau of Labor Statistics, employment of information security analysts is projected to grow 31 percent from 2019 to 2029, much faster than the average for all occupations.

Practical Tips for Getting Started

If you are interested in a career in cybersecurity, here are some practical tips to help you get started:

• Obtain a degree in Computer Science, information technology, or a related field
• Gain experience through internships or entry-level positions
• Obtain certifications such as CompTIA Security+, Certified Information Systems Security Professional (CISSP), or Certified Ethical Hacker (CEH)
• Stay up-to-date with the latest security threats and technologies by attending conferences and networking with other cybersecurity professionals

In conclusion, both Cyber Security Analysts and Cyber Security Specialists play important roles in protecting organizations from cyber threats. While there are some differences in terms of responsibilities, required skills, and educational backgrounds, both roles require a strong understanding of security protocols and standards, as well as familiarity with security tools and software. By following the practical tips outlined in this article, you can start your career in cybersecurity and help protect organizations from cyber threats.

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The CASE statement is a powerful tool in programming and data manipulation that evaluates a series of conditions and returns a value based on the first condition that is met. Think of it as similar to the concept of an if-then-else statement.

Here’s an in-depth explanation of how the CASE statement works:

1. Condition Evaluation: The CASE statement systematically goes through a set of conditions specified by the programmer. It starts with the first condition and evaluates whether it is true or false.

2. Termination on True Condition: When a condition evaluates to true, the CASE statement immediately terminates its evaluation and yields the corresponding result associated with that true condition. This means that only the first true condition is considered, and the rest of the conditions are ignored.

3. Handling No True Conditions: In cases where none of the specified conditions is true, and if there is an ELSE clause provided, the CASE statement will produce the value specified in the ELSE clause. This is useful for providing a default value or outcome when none of the conditions match.

To illustrate this concept, let’s consider an example using a table called “EmployeeDemographics”:

• EmployeeDemographics Table: This table presumably contains information about employees, such as their ages.

Now, let’s create a CASE statement for this scenario:

In this example, we’ve set up three conditions:

1. If the employee’s age is greater than 30, the outcome is ‘OLD’.
2. If the employee’s age is between 22 and 30, the outcome is ‘YOUNG’.
3. If none of the above conditions are met (ELSE), the default outcome is ‘CHILD’.

This CASE statement will evaluate the age of each employee in the “EmployeeDemographics” table and return one of the specified outcomes based on the age range that matches the condition. If none of the conditions match, it will default to ‘CHILD’.

In summary, the CASE statement is a versatile tool for making conditional decisions in SQL and other programming languages, allowing you to handle different scenarios and produce appropriate results based on specified conditions.

Credit:Background image source was Google.

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The better you know your data the better is your analysis. Data needs to be analyzed so as to produce good results. Exploratory data analysis (EDA) is an approach to analyze and summarize data in order to gain insights and identify patterns or trends. It is often the first step in data analysis and is used to understand the structure of the data, detect outliers and anomalies, and inform the selection of appropriate statistical models.

Objectives of EDA.

1. Confirm if the data is making sense in context of the business problem.
2. It uncovers and resolves data quality issues like missing data, duplicate and incorrect values.
3. Data scientists can use exploratory analysis to ensure the results they produce are valid and applicable to any desired business outcomes and goals.
4. EDA helps stakeholders by confirming they are asking the right questions.
5. EDA can help answer questions about standard deviations, categorical variables, and confidence intervals.

Types of exploratory data analysis.

EDA can be classified into two category this is graphical and non-graphical each having Univariable and multivariable type.

Univariate non-graphical.
Data being analyzed consists of just one variable and it doesn’t deal with causes or relationships. The main purpose of univariate analysis is to describe the data and find patterns that exist within it.
Univariate graphical.
They provide a full picture of the data. Common types of univariate graphics include: Stem-and-leaf plots, Histograms and box plots.
Multivariate non graphical.
Multivariate data arises from more than one variable. Multivariate non-graphical EDA techniques generally show the relationship between two or more variables of the data through cross-tabulation or statistics
Multivariate graphical.
Multivariate data uses graphics to display relationships between two or more sets of data. Example is a grouped bar plot or bar chart.

Exploratory Data Analysis Tools.

In this article I will only focus on Python: We used python programming language for exploratory data analysis. Python offers a variety of libraries and some of them uses great visualization tool. Visualization process can make it easier to create the clear report.
To use python for EDA here are some of the steps you will use;
Step 1: Imports and Reading Data.

import pandas as pd
import numpy as np
import matplotlib.pylab as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
df = pd.read_csv('filename.data.csv')

With these code and libraries imported, you’re ready to start working with data and creating visualizations in your Python environment. Make sure you have the necessary data loaded and continue with your data analysis and visualization tasks.
Step 2: Data Understanding.
This involves getting a grasp of the data you’re working with, its characteristics, structure, and content. Here are some of the ways to archive data understanding using python code.

• Dataframe shape
  df.shape
• head and tail
  df.head(5)
• dtypes
  df.dtypes
• describe
  df.describe()

Step 3: Data Preparation.
In this step you will be focusing on dropping irrelevant columns and rows, identifying duplicated columns etc. In this phase, you transform and clean the raw data to make it suitable for analysis.

Step 4: Feature Understanding.
This step falls into Univariate analysis which involves creating, selecting, and transforming features (variables or attributes) in your dataset to improve the performance and interpretability of machine learning models or enhance the effectiveness of data analysis. Thus, plotting Feature Distributions, Histograms, KDE and Boxplot.

Step 5: Feature Relationships.
Here, you will be focusing on understanding how different features (variables) in your dataset relate to each other. This step helps you uncover patterns, dependencies, and interactions between features, which can be valuable for model building, feature selection, and gaining insights from your data. In this step you will be able to come up with Scatterplot, Heatmap Correlation, Pair plot and Group by comparisons.

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Exploratory data analysis (EDA) is an approach to analyzing and understanding data that involves summarizing, visualizing, and identifying patterns and relationships in the data. There are many different techniques and approaches that can be used in EDA, and the specific techniques used will depend on the nature of the data and the questions being asked. Here are some common techniques that are often used in EDA:

1. Visualization: Plotting the data in various ways can help reveal patterns and trends that may not be immediately apparent. Common types of plots include scatter plots, line plots, bar plots, and histograms.

2. Summary statistics: Calculating summary statistics such as mean, median, and standard deviation can provide useful information about the distribution and spread of the data.

3. Correlation analysis: Examining the relationships between different variables can help identify correlations and dependencies.

4. Data cleaning: Removing missing or incorrect values and ensuring that the data is in a consistent format is an important step in EDA.

5. Dimensionality reduction: Techniques such as principal component analysis (PCA) can be used to reduce the number of dimensions in the data, making it easier to visualize and analyze.

6. Anomaly detection: Identifying unusual or unexpected values in the data can be important in identifying errors or outliers.

7. Feature engineering: Creating new features or transforming existing features can improve the performance of machine learning models and facilitate analysis.

Overall, the goal of EDA is to gain a better understanding of the data, identify potential issues or problems, and develop hypotheses about the relationships and patterns in the data that can be further tested and refined.

Now we will study in more detail all the points mentioned above.

1. Visualization

Here is a simple example using a sample dataset of weather data for a single location. The data includes the temperature, humidity, and wind speed for each day in a month.

First, we will import the necessary libraries and read in the data from a CSV file:

import pandas as pd
import matplotlib.pyplot as plt

# Read in the data from a CSV file
df = pd.read_csv('weather.csv')

Next, we can use various types of plots to visualize the data in different ways. Here are a few examples:

Scatter plot:

# Scatter plot of temperature vs humidity
plt.scatter(df['Temperature'], df['Humidity'])
plt.xlabel('Temperature (°F)')
plt.ylabel('Humidity (%)')
plt.show()

Line plot:

# Line plot of temperature over time
plt.plot(df['Date'], df['Temperature'])
plt.xlabel('Date')
plt.ylabel('Temperature (°F)')
plt.show()

Bar plot:

# Bar plot of average temperature by month
df.groupby('Month').mean()['Temperature'].plot(kind='bar')
plt.xlabel('Month')
plt.ylabel('Temperature (°F)')
plt.show()

Histogram:

# Histogram of temperature
plt.hist(df['Temperature'], bins=20)
plt.xlabel('Temperature (°F)')
plt.ylabel('Frequency')
plt.show()

2. Summary statistics:

From same above weather data, we can do the following statistics visualization.

Mean:

# Calculate the mean temperature
mean_temp = df['Temperature'].mean()
print(f'Mean temperature: {mean_temp:.2f}°F')

Mean temperature: 67.50°F

Median:

# Calculate the median humidity
median_humidity = df['Humidity'].median()
print(f'Median humidity: {median_humidity:.2f}%')

Median humidity: 87.50%

Standard deviation:

# Calculate the standard deviation of wind speed
std_wind_speed = df['Wind Speed'].std()
print(f'Standard deviation of wind speed: {std_wind_speed:.2f} mph')

Standard deviation of wind speed: 15.14 mph

Minimum and maximum:

# Calculate the minimum and maximum temperature
min_temp = df['Temperature'].min()
max_temp = df['Temperature'].max()
print(f'Minimum temperature: {min_temp:.2f}°F')
print(f'Maximum temperature: {max_temp:.2f}°F')

Minimum temperature: 45.00°F

Maximum temperature: 90.00°F

Now, I am not sure but I can read your mind. I am sure you thought that I forgets the pandas describe data frame function but don’t worry it’s here.

df.describe()

Output:

I hope this helps! Let me know if you have any questions or if you would like to see examples of other summary statistics.

3. Correlation analysis:

Here is an example using a sample dataset of student grades:

First, we will import the necessary libraries and read in the data from a CSV file:

import pandas as pd
import seaborn as sns

# Read in the data from a CSV file
df = pd.read_csv('student_grades.csv')

To analyze the correlations between different variables, we can use a variety of techniques. Here are a few examples:

Scatter plot:

# Scatter plot of midterm grades vs final grades
sns.scatterplot(x='Midterm', y='Final', data=df)

Correlation matrix:

# Correlation matrix
corr = df.corr()
sns.heatmap(corr, annot=True)

Linear regression:

# Linear regression of midterm grades vs final grades
sns.lmplot(x='Midterm', y='Final', data=df)

As you know it is a hard task and also time taking to cover any topic in detail but here I have provided a summary of the Correlation analysis.

Correlation analysis is a statistical method used to identify the strength and direction of the relationship between two variables. It is commonly used in exploratory data analysis to understand the relationships between different variables in a dataset and to identify patterns and trends.

There are several different measures of correlation, including Pearson’s correlation coefficient, Spearman’s rank correlation coefficient, and Kendall’s tau. These measures range from -1 (perfect negative correlation) to 1 (perfect positive correlation), with 0 indicating no correlation.

To perform correlation analysis, you can use various techniques such as scatter plots, correlation matrices, and linear regression. Scatter plots can be used to visualize the relationship between two variables, and correlation matrices can be used to visualize the correlations between multiple variables. Linear regression can be used to fit a line to the data and assess the strength of the relationship between the variables.

It is important to note that correlation does not imply causation, meaning that the presence of a correlation between two variables does not necessarily mean that one variable causes the other. It is always important to consider other factors that may be influencing the relationship between the variables.

4. Data cleaning:

Here is an example using a sample dataset of student grades with some missing and incorrect values:

This dataset includes the names of students and their grades on a midterm and final exam. Some of the values are missing (indicated by empty cells) and some of the values are incorrect (e.g. a final grade of 105).

First, we will import the necessary libraries and read in the data from a CSV file:

import pandas as pd

# Read in the data from a CSV file
df = pd.read_csv('student_grades_with_errors.csv')

Here are a few examples of data cleaning techniques that can be used to address missing and incorrect values:

Identifying missing values:

# Check for missing values
df.isnull().sum()

Student 0

Midterm 1

Final 1

dtype: int64

Dropping rows with missing values:

# Drop rows with missing values
df.dropna(inplace=True)

Filling missing values with a placeholder value:

# Fill missing values with a placeholder value (-999)
df.fillna(-999, inplace=True)

Replacing incorrect values:

# Replace incorrect values (e.g. grades above 100) with a placeholder value (-999)
df['Midterm'].mask(df['Midterm'] > 100, -999, inplace=True)
df['Final'].mask(df['Final'] > 100, -999, inplace=True)

There is much more in data cleaning but I have provided some general things.

Data cleaning is the process of identifying and addressing issues with the data, such as missing or incorrect values, inconsistent formats, and outliers. It is an important step in the data analysis process as it helps ensure that the data is accurate, consistent, and ready for analysis.

There are a variety of techniques that can be used for data cleaning, depending on the specific issues with the data and the desired outcome. Some common techniques include:

• Identifying missing values: Use functions such as isnull() or notnull() to identify cells that contain missing values.

• Dropping rows with missing values: Use the dropna() function to remove rows that contain missing values.

• Filling missing values: Use the fillna() function to fill missing values with a placeholder value (e.g. 0 or -999).

• Replacing incorrect values: Use functions such as mask() or replace() to replace incorrect values with a placeholder value.

It is important to carefully consider the appropriate approach for addressing missing or incorrect values, as simply dropping rows or filling missing values with a placeholder value may not always be the best solution. It is often helpful to investigate the cause of the missing or incorrect values and consider whether there may be other factors that need to be taken into account.

5. Dimensionality reduction:

Here is a sample dataset of student grades with three variables (midterm grades, final grades, and attendance):

This dataset includes the names of students, their grades on a midterm and final exam, and their attendance percentage. The grades are out of 100 and the attendance percentage is out of 100.

First, we will import the necessary libraries and read in the data from a CSV file:

import pandas as pd
from sklearn.decomposition import PCA

# Read in the data from a CSV file
df = pd.read_csv('student_grades_with_attendance.csv')

One common technique for dimensionality reduction is principal component analysis (PCA). PCA is a linear transformation technique that projects the data onto a lower-dimensional space, reducing the number of variables while still retaining as much of the variance as possible.

Here is an example of using PCA to reduce the dimensionality of the data from three variables to two:

# Select only the numeric columns
data = df.select_dtypes(include='number')

# Perform PCA
pca = PCA(n_components=2)
pca.fit(data)

# Transform the data
transformed_data = pca.transform(data)

# Print the explained variance ratio for each principal component
print(pca.explained_variance_ratio_)

[0.90800073 0.06447863]

Summary for the same for tips and note point:

Dimensionality reduction is the process of reducing the number of variables in a dataset while still retaining as much of the information as possible. It is often used in machine learning and data analysis to reduce the complexity of the data and improve the performance of algorithms.

There are a variety of techniques for dimensionality reduction, including principal component analysis (PCA), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE). These techniques can be used to transform the data into a lower-dimensional space, typically by projecting the data onto a smaller number of orthogonal (uncorrelated) dimensions.

PCA is a linear transformation technique that projects the data onto a lower-dimensional space by finding the directions in which the data varies the most. LDA is a supervised learning technique that projects the data onto a lower-dimensional space by maximizing the separation between different classes. t-SNE is a nonlinear dimensionality reduction technique that projects the data onto a lower-dimensional space by preserving the local structure of the data.

It is important to carefully consider the appropriate dimensionality reduction technique for a given dataset, as the choice of technique can have a significant impact on the results.

6. Anomaly detection:

Here is an example using a sample dataset of student grades with some anomalous values:

This dataset includes the names of students and their grades on a midterm and final exam. The grades are out of 100. The values for Kim’s midterm grade (110) and Nancy’s final grade (105) are anomalous, as they are much higher than the other values in the dataset.

First, we will import the necessary libraries and read in the data from a CSV file:

import pandas as pd
from sklearn.ensemble import IsolationForest

# Read in the data from a CSV file
df = pd.read_csv('student_grades_with_anomalies.csv')

One common technique for anomaly detection is isolation forest, which is a type of unsupervised machine learning algorithm that can identify anomalous data points by building decision trees on randomly selected subsets of the data and using the number of splits required to isolate a data point as a measure of abnormality.

Here is an example of using isolation forest to detect anomalous values in the midterm grades:

# Create an isolation forest model
model = IsolationForest(contamination=0.1)

# Fit the model to the data
model.fit(df[['Midterm']])

# Predict the anomalies
anomalies = model.predict(df[['Midterm']])

# Print the anomalies
print(anomalies)

[ 1 1 1 1 1 1 1 1 1 1 -1 1 -1 1 ]

/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but IsolationForest was fitted with feature names warnings.warn(

The contamination parameter specifies the expected proportion of anomalous values in the data. In this example, we set it to 0.1, which means that we expect 10% of the values to be anomalous.

I hope this helps! Let me know if you have any questions or if you would like to see examples of other anomaly detection techniques.

More about it:

Anomaly detection, also known as outlier detection, is the process of identifying data points that are unusual or do not conform to the expected pattern of the data. It is often used in a variety of applications, such as fraud detection, network intrusion detection, and fault diagnosis.

There are a variety of techniques for anomaly detection, including statistical methods, machine learning algorithms, and data mining techniques. Statistical methods involve calculating statistical measures such as mean, median, and standard deviation, and identifying data points that are significantly different from the expected values. Machine learning algorithms such as isolation forests and one-class support vector machines can be trained on normal data and used to identify anomalies in new data. Data mining techniques such as clustering can be used to identify data points that are significantly different from the majority of the data.

It is important to carefully consider the appropriate technique for a given dataset, as the choice of technique can have a significant impact on the results. It is also important to consider the specific context and requirements of the application, as well as the cost of false positives and false negatives.

7. Feature engineering

Feature engineering is the process of creating new features (variables) from the existing data that can be used to improve the performance of machine learning models. It is an important step in the data analysis process as it can help extract more meaningful information from the data and enhance the predictive power of models.

There are a variety of techniques for feature engineering, including:

• Combining multiple features: Creating new features by combining existing features using arithmetic operations or logical statements.

• Deriving new features from existing features: Creating new features by applying mathematical transformations or aggregations to existing features.

• Encoding categorical variables: Converting categorical variables into numerical form so that they can be used in machine learning models.

It is important to carefully consider the appropriate approach for feature engineering for a given dataset, as the choice of features can have a significant impact on the results. It is often helpful to explore the data and identify potential opportunities for feature engineering, such as combining or transforming variables to better capture relationships or patterns in the data.

Here is an example using a sample dataset of student grades:

First, we will import the necessary libraries and read in the data from a CSV file:

import pandas as pd

# Read in the data from a CSV file
df = pd.read_csv('student_grades.csv')

Feature engineering is the process of creating new features (variables) from the existing data that can be used to improve the performance of machine learning models. There are a variety of techniques for feature engineering, including:

Combining multiple features:

# Create a new feature by combining two existing features
df['Total'] = df['Midterm'] + df['Final']

Deriving new features from existing features:

# Create a new feature by dividing one feature by another
df['Average'] = df['Total'] / 2

# Create a new feature by taking the square root of a feature
import numpy as np
df['Sqrt_Midterm'] = np.sqrt(df['Midterm'])

Encoding categorical variables:

# One-hot encode a categorical feature
df = pd.get_dummies(df, columns=['Gender'])

After doing feature engineering data frame look like this:

Did you learn something new from this post? Let us know in the comments!

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Are you a software engineer interested in advancing in your career and getting an advantage in the job market? Learning system design can help you with both. By understanding this process for creating modern systems to satisfy real-world requirements, you’ll be better prepared to provide resilient and scalable solutions in your day-to-day work. Having system design skills in your toolkit will also help you progress in your field and move up in your organization.

System design has become essential to the software development process. That’s why it’s good to take a step back and analyze the big-picture impact of good design on the long-term efficiency, longevity, and success of software and information systems. System design and the requirements that it targets don’t exist in a vacuum. As a software engineer, it works to your advantage to know why certain requirements are being targeted, how they relate to the business goals of your organization, and how these decisions impact the overall user experience.

To better understand the many factors that contribute to a system’s design and how information technology professionals respond when a system’s implementation runs into trouble, we need to talk about system analysis.

Today, we’ll look at system design and system analysis topics through a comparative lens. We’ll explore these processes, how they fit into the broader system development life cycle, and how to differentiate them.

We’ll cover:

• What is system analysis?
• What is system design?
• System analysis vs system design
  • 1. System analysis precedes system design
  • 2. Focusing on “what” vs “how”
  • 3. Completing different tasks
• Start mastering system design today

What is system analysis?

System analysis is a process for reviewing a technological system for troubleshooting, development, or improvement. Such a system might be a software implementation, like a system or application program. It’s important to consider system analysis as one phase in a larger process, the systems development life cycle (SDLC), which we’ll discuss in more detail later. In the system analysis phase, analysts are concerned with outlining a proposed solution to a defined problem. In doing so, they consider how viable and effective the product is or will be. System analysts consider the system’s overarching goals, which they can then break down into components or modules to enable individual analyses.

Two tasks commonly facilitate these analyses: feasibility studies and requirements engineering.

• Feasibility studies focus on several measures of how likely a proposed solution is to solve the defined problem
• Requirements engineering determines what the proposed solution must accomplish

We’ll explore both of these tasks in greater detail later.

While completing these tasks, system analysts generally work toward a key output: a finalized system requirements specification (SRS) or system requirements document. When system analysts hand off this product, they provide software engineers with the principal input for the next phase of the life cycle: system design.

What is system design?

In software development, system design is the process of defining the architecture, interfaces, and data model for a system to satisfy the requirements outlined in the SRSs. At this stage, software engineers translate business requirements into technical specifications to build a new physical system or update an existing one. If the system is designed well, it will serve clients’ needs and, ultimately, fulfill business objectives.

As its name implies, system design is a process that should happen systematically. By considering a system’s infrastructure completely and in an orderly manner, from hardware and software to data and how it’s stored, you can help ensure that the final design has important characteristics like reliability, effectiveness, and maintainability.

• Reliable systems are resilient against errors, failures, and faults.
• Effective systems satisfy users’ needs and requirements set by the business.
• Maintainable systems prove flexible and simple to scale up or down. It’s generally easier to add new features to maintainable systems, as well.

This overview barely scratches the surface of system design by intention. We’ll continue to unpack the design process in the next section on the differences between system analysis and system design, but a short primer or thorough guide could also be helpful.

System analysis vs system design

As sequential phases of the SDLC, system analysis and system design share a broader purpose: developing technological systems that serve the customer and business needs. From there, a number of differences emerge between the two processes. We’ll discuss three of these differences now.

1. System analysis precedes system design

Here’s where understanding the bigger picture of the system development life cycle (SDLC) helps. The SDLC, also called the software development life cycle or the application development life cycle, is a multi-phase process for creating an information system. It covers the life of the system from planning, through launch, to assessment. A key concept in information technology, the SDLC encompasses a mix of software and hardware configurations. These can include systems consisting of only software, only hardware, or a combination of both.

The SDLC is not a methodology. It is a description of phases. There are, however, several methodologies or models that fit within the SDLC. Some well-known examples include Waterfall, Agile software development, and Rapid prototyping.

Depending on your source, you may find variations in the numbers and names of SDLC phases. But developing a system will usually involve the same major tasks, and one way this process breaks down into phases is as follows:

1. Planning and preliminary analysis
2. System analysis
3. System design
4. Development
5. Testing and integration
6. Implementation
7. Operation and maintenance
8. Evaluation

The SDLC phases are generally meant to be implemented in sequence, with the completion of one preceding the initiation of the next. This sequencing lets developers, engineers, and programmers concentrate on one phase at a time and simplifies the development process. Depending on the organization and methodology, at times, not every phase is carried out. At other times, the phases may overlap.

Regardless, one thing usually stands true: system analysis and system design are essential and sequential parts of the process. Since the goal of the SDLC is the creation of high-quality information systems supporting identified needs, it follows that system analysis comes before system design. After all, in order for system design to start fulfilling requirements, software engineers first need to know what those requirements are.

2. Focusing on “what” vs “how”

System analysis and system design divide their responsibilities in multiple ways. We’ve already discussed the importance of timing and how requirements gathering needs to precede the technical solution’s design. In addition, system analysts and software engineers have different focuses for their deliverables, which we can label the “what” vs the “how” of system development.

System analysis: The “what”

As discussed earlier, system analysis is an early and fundamental phase in the SDLC. In the context of software engineering, system analysts review a technological system for various purposes, ultimately proposing a solution to a problem using a computer system. In other words, they identify what is required to serve the client and customer needs. After feasibility studies and requirements engineering, they record this information in a system requirements specification (SRS) document.

System design: The “how”

With a finished SRS, the process advances to system design, which amounts to a phase for determining how to satisfy requirements. To visualize the desired outcome, think of creating a unique combination of distinct components. To make this visualization more tangible, we’ll call these components building blocks, representing similarities shared across system design problems that have been extracted for easier reuse.

For our purposes, there are 16 building blocks of modern system design that you can draw from. To understand the utility of these building blocks, it can help to think of them as bricks, a kind of combinable raw material. Once you grasp what these building blocks do, you can use them to demonstrate how to create reliable, effective, and maintainable systems for virtually any design problem.

3. Completing different tasks

Another distinction to make between system analysis and system design is in terms of the work process. Two conventions are used in system analysis: feasibility studies and requirements engineering. Meanwhile, the complexity of system design prevents any single method from solving every problem, but engineers can use a variety of consistent procedures to solve problems systematically. We’ll discuss one reusable approach that can address a number of scenarios.

Feasibility studies

Recall that system analysis involves outlining a proposed solution to a defined problem. To gauge the suitability of potential solutions, system analysts turn to feasibility studies.

These studies typically involve the following steps:

1. Identifying deficiencies in the existing system. This can begin with preparing a flowchart of the system, including its subsystems, and then examining it for vulnerabilities or points of failure.
2. Identifying the new system’s objectives, scope, and responsible users.
3. Preparing a flowchart of the proposed system.
4. Determining whether the proposal is a feasible solution for the new or upgraded system.

This final step is mostly concerned with weighing three types of feasibility:

• Technical feasibility: Noting the current hardware and software resources of the client or customer and deciding whether the existing set-up can meet the technical requirements of the proposed system.
• Economic feasibility: Conducting a cost-benefit analysis of the proposed system and comparing the results with the project budget.
• Operational feasibility: Determining whether the system will work in the way that the users expect, considering the availability of the people who will be needed to develop and implement the system.

Additional types of feasibility may include social feasibility, management feasibility, legal feasibility, and time feasibility. But no matter how system analysts slice up feasibility, the expected outcome is the same: a determination of whether the proposed system for solving a defined problem can and should go ahead. When this analysis results in a green light, system analysts can work on requirements engineering.

Requirements engineering

In requirements engineering, also known as requirements analysis, analysts will define, document, and maintain requirements pertaining to the proposed system. In general, this process includes examining data about the system’s goals and objectives, such as:

• How the proposed system would work at a high level
• What qualities or properties the proposed system must have to provide the expected results

Later, software engineers will look for specific coding solutions that align with these findings.

A major focus of requirements engineering is ensuring a thorough understanding of client’s needs and expectations. Communication between the company producing the system and clients is key, and requirements engineering can include several activities to support alignment:

• Solicitation: Initially collecting the requirements from the client
• Analysis: Assessing the clients’ requirements in more detail
• Specification: Producing a formal document, sometimes called a requirements specification
• Validation or verification: Ensuring that the documented requirements are consistent and meet the client’s needs
• Management: Matching proposed system processes to requirements

During feasibility studies and requirements engineering, systems analysts might use several kinds of tools. These can include flowcharts (of the organization, existing system, or proposed system architecture) and user interface (UI) mockups (to understand how end users interact with the system).

After determining the feasibility and fine-tuning requirements, system analysts produce the SRS. This document enables system design engineers to begin working on the design for the new or updated system.

The RESHADED approach to system design

Although no one-size-fits-all method exists for the design phase, the RESHADED approach offers engineers a flexible way to break down many problems. This approach articulates the steps for designing almost any system from scratch, whether you’re working on a client project or sitting for system design interviews. We’ll quickly look at what the acronym stands for.

• Requirements: Gather all functional and non-functional requirements reflecting the needs of the client business or organization. Functional requirements represent core features, without which the system wouldn’t work as the end user expects, while non-functional requirements are essential considerations that don’t contribute to the core functionality.
• Estimation: Gauge the hardware and infrastructural resources needed to implement a system at scale.
• Storage schema (optional): Articulate a data model, with data flow diagrams, if relevant to the problem at hand. You’ll want to define the structure of the data, which tables to use, the types of fields in the tables, and the relationships between tables (optional). You might need this step when expecting highly normalized data, needing to store different parts of data in different formats, or facing performance and efficiency concerns around storage.
• High-level design: Select from the 16 building blocks we discussed earlier to fulfill certain functional requirements.
• APIs: Create interfaces that users can use to call various services within the system. Interfaces take the form of API calls and are typically a translation of functional requirements.
• Detailed design: Analyze and improve the high-level design, adding or replacing building blocks to meet non-functional requirements, then outlining these building blocks. This outline should identify how and why the components work, why they’re needed, and how they will be integrated.
• Evaluation: Compare the detailed design against the requirements. Justify tradeoffs and weigh the pros and cons of alternative designs. Identify areas for improvement and consider solutions to any overlooked issues.
• Distinctive component/feature: Discuss a unique feature added to your design to satisfy requirements. This discussion, which can follow various steps in the process, may be most relevant to system design interviews or presentations.

The utility of the RESHADED approach is most apparent in its flexibility as a general guideline for solving system design problems. However, it’s not meant to solve every design problem, so don’t be afraid to be creative and resourceful when it comes to designing new solutions.

Start mastering system design today

At this point, you should have a good idea of how system analysis and system design fit into the software development process and how they differ. Even if your primary focus will be on system design in your career, it’s good to understand what happens beforehand and what systems analysts contribute to the overall SDLC.

If you’re a software engineer or aspiring to become one, we hope the discussion of system design has inspired you to learn more about this essential process. To help you master system design, we’ve created the course Grokking Modern System Design Interview for Engineers & Managers. This interactive course offers a modern perspective on designing complex systems using various components in a microservices architecture. You’ll learn all about the building blocks and RESHADED approach discussed in this article and have opportunities to apply these concepts to real-world design problems.

Happy learning!

Continue learning about system design on Educative

• I’m new to System Design, where do I start?
• Simplify system design interviews with the RESHADED approach
• What are the components of System Design?

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