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Category: Analytics

What Are the Six Business Analysis Knowledge Areas

Posted on by Imarticus Learning

In today’s world of rapidly changing business dynamics, business analysis is quickly becoming a crucial aspect that determines the success of any establishment. While with the advent of technology, understanding and analysing business problems have become much simpler, even today it is necessary that a business analyst knows the six knowledge areas that were originally outlined in the Business Analysis Body of Knowledge also known as BABOK.

BABOK, created and published by the International Institute of Business Analysis (IIBA) can be described in the most simplest terms as being a framework that describes the various aspects that encompasses a business, the tasks that are undertaken on a regular basis, and the competencies as well as techniques that are necessary to execute the same. In layman terms, BABOK can be compared to the Bible for aspiring as well as established business analysts in today’s global business environment.

What is Business Analysis

According to the second version of BABOK, business analysis can be described as being, “the set of tasks and techniques used to work as a liaison among stakeholders in order to understand the structure, policies, and operations of an organization, and recommend solutions that enable the organization to achieve its goals.”

In simple terms, this definition states that business analysis is a tool that helps organizations and businesses understand and analyze their problems as well as opportunities. Along with this, when a potential solution presents itself, business analysis helps them establish if the solution is valid and most importantly measure the implementation success of the solution in the longer run.

Mentioned below are some of the key areas in which business analysis serves organizations:

1. Establishing and understanding key parameters as well as strategic objectives.
2. Listing out the projects that can be pursued along with analyzing the feasibility of pursuing them.
3. Understand and analyze the opportunities presented by the market as well as decide if skills should be acquired in order to fulfil the demand.
4. It helps establish that all the stakeholders share the same vision for the future as well as are aware of the steps that will be undertaken to reach the same. In a way, it helps keep the company on track by envisioning its future and subsequent goals.

The popular belief around BABOK is the fact that it is purely technological information, but this is not entirely true. One of the most significant benefits of business analysis is the fact it can be used in any aspect of the business and be applied regularly on objectives the company wishes to fulfil. This can be anything ranging from undertaking the development of a new software, acquiring a competitor as well as opening a new branch.

One of the most important sections of BABOK is “Requirements” as it helps businesses analyse capabilities as well as conditions that it needs to acquire or analyse in order to meet current business demands. BABOK mainly focuses on the process of identifying requirements, categorizing them, communicating it to the stakeholders, assess the risks as well as benefits, along with managing all the requirements simultaneously.

Six Knowledge Areas

The six knowledge areas outlined in BABOK are the following:

1. Planning and Monitoring
2. Elicitation
3. Requirements Communication and Management
4. Requirements Analysis
5. Enterprise Analysis
6. Validation and assessment of solutions

Conclusion

The six areas of knowledge outlined in BABOK along with all the other sections are crucial parts of any business analysts knowledge and thus if you are aspiring to become a business analyst, you should not understand and analyze them, but also implement them in real life establishments on a regular basis, along with undertaking a formal business analysis training.

Does SAS Base Programmer Certification Help In Building A Career In Analytics?

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SAS is an acronym that stands for Statistical Analysis in AI or business intelligence models and is important analytics and artificial intelligence tool. The use of this tool has become a crucial part of businesses nowadays. The primary features or the roles that SAS has an effect on are: 

  • Data mining,
  • Predictive Analysis,
  • Reporting,
  • Econometric and time series analysis, etc. 

With that being said, anyone who wants to have a career in advanced analytics, being a SAS programmer in today’s world is the best option. 

Does getting a certification for a SAS base programmer help your career?

For anyone who is looking to get into analytics and artificial intelligence, or any other type of profession, getting certified is what helps you move forward and build a career. Certification for the chosen fields helps you to find a way to create a mark and tell your employers what you are capable of. And the same stands true for those who wish to create a career in advanced analytics. 

Here is why certification for a SAS based programmer helps you boost your career:

  • As the name suggests, the certification allows you to understand the basics of SAS. It helps you to get an overview of the analytics and artificial intelligence field.
     
  • Since it is an introductory certification, attempting the exam and securing a grade will help you to be eligible for the next certifications. Just in case you wish to ensure that you want to study or get a certified future or grow your career in advanced analytics, the base programmer certification will help.

  • The certification course of a base programmer is the perfect start for anyone in this field. Furthermore, the certification can be applicable globally!

Apart from the above-mentioned points, the certification of base programmer in SAS helps you build your career in advanced analytics by helping you with:

  • Improving your credibility and marketability in the field. A certification says that you know what you’re doing and that you have professional training in the given field.

  • SAS certifications are structured by keeping the role of the person or the trainee as per the job, which is to say that you can find on-job-training right from the beginning.

  • It increases your future prospects in the analytics and artificial intelligence field. The industries are growing and the need for professionals with SAS certifications in high on demand.

  • Lastly, the SAS certifications can be cross-checked, meaning your employers or potential employers will be able to check the authenticity of your certification.

The field of prospective jobs and the requirement of a programmer, or a SAS professional is increasing by the day. As more businesses join the e-commerce industry and more of them wish to connect to their consumers and customers in a better manner, the role of SAS programmers from beginners to highly-skilled professionals is getting broader. If you wish to establish a good career in advanced analytics, then this is the time to pick up SAS programming.

What Are Business Analysis Techniques?

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With the growth of AI and Data Science, the field of Business analysis has gained significant value in the past decade. More and more businesses today rely on the acumen of the business analyst to obtain data-driven solutions for their business problems. Let’s delve into how the complex process of analysing business needs and providing solutions to the same is carried out by business analysts.

Business Analysis Techniques

Some of the most popular and relevant business analysis techniques are as follows:

SWOT Analysis

The term SWOT is the acronym for Strength, Weakness, Opportunity, Threat. SWOT analysis forms the most rudimentary business analysis techniques to get a holistic view of the most important aspects concerning the business. This analysis techniques categorise the Strength & Weakness as internal factors of the business, the strong suit of the business could be its brand image and the weak points of the business could be its high price point given the alternatives in the market.

This analysis technique considers opportunity and threats as external factors to the business. The threat could be in the form of increased competition, an example of an opportunity could be expanding into an untapped market. SWOT analysis techniques is a one that is widely used and the application can be done at any stage of the project.

MOST Analysis

MOST stands for Mission, Objective, Strategy, Tactics. These four elements form the core of the MOST business analysis technique. It provides a clear picture when it comes to understanding the purpose and approach of the business. Let’s break each element individually to gain more insight into this valuable business analysis technique.

Mission

Every organisation is started to fulfil a purpose, to solve a problem, to accomplish a goal, that goal and purpose is the mission statement of the company. The primary step to business analysis is to be familiar with the mission of the company, everything else is secondary.

Objectives

What is the objective? How is it different from a mission? Well, the mission takes a broader approach to the problem whereas objectives are the set of goals that the organisation intends to achieve within a certain time period. The objective breaks down the broader mission into more defined goals and a measurement approach.

Strategy

Strategy again takes a broader angle; it can be defined as the approach and plan the organisation will use to obtain its business objectives within the set time frame. Strategy answers the ‘how’ of the business.

Tactic

Tactics are more refined methods to carry out a broad strategy to achieve the goals of the organisation. Tactics are more about short term operational plans to obtain the objectives.

Business Process Modelling

Business Process Modelling considers the process improvement techniques which will help the organisation to achieve the objectives in a more efficient and effective manner. It helps in analysing the gap that exists between the existing business process and the more efficient business process which could be opted instead. The pros and cons of both the business process are taken into factored in before reaching a decision. Some of the major steps involved in the Business Process Modelling technique include strategic planning, business model analysis, process designing, and complex technical analysis. The technique of process improvement is widely used in the IT industry.

PESTLE Analysis

The organisations do not operate in isolation, there are multiple stakeholders in a business ranging from customers to employees and suppliers. All stakeholders are influenced by the functioning of the business and have the power to influence its operations. In the contemporary scenario, sustainability if of utmost concern, it’s not only about profit maximisation for the business but to achieve it in a more sustainable way. PESTLE is the acronym for the crucial factors that influence the business, these include Political, Economic, Social, Technological, Legal, Environmental factors. PESTLE technique analyses these factors and identifies how each of these elements will influence the performance of the business.

Also Read: Business Analysis Trends in 2020

What Does an AI Researcher Do?

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Developments in the AI Industry

The fourth phase of the industrial revolution is all about automation and artificial intelligence which is powered by data science to a great extent. The need to reduce the chances of human error and automate repetitive mundane tasks has been paramount. We are moving to a stage where human inputs are needed only in critical situations and machines are handed over the responsibility to carry out day to day operations.

Even the AI is evolving on a continuous basis, making the machines able to carry out fairly complex tasks as compared to their traditional use in repetitive functions. Autonomous driving vehicles are the talk of the town; this indicated the degree to which the AI has penetrated human lives and the way of living. The

AI with machine learning algorithms made its presence felt across various industries including healthcare, education, e-commerce, finance, etc. From robot advisors to customized product recommendations it has directly or indirectly influenced the lives of masses and will aggressively continue to do so in the coming future.

The role of an AI Researcher

The role of a researcher in any field is more focused on gathering information and discovering new aspects of the field, it’s more concentrated towards exploring and discovering the unknowns of a subject matter. According to computer science, AI research has been established as the study of intelligent agents, intelligent agents here are described as machines or devices that comprehend its surroundings and act accordingly after analyzing all the variables that increase its chances of achieving set goals successfully.

The role of an AI researcher incorporates multiple things; it requires people to work on problems related to machine learning technology. In a brief sense, you have to identify a problem in your domain, get relevant information regarding the problem through research reports and other sources, understand the maths and prepare the algorithm that gives you a sense of the real problem in coding terms. After this, you’ll need to experiment and test the algorithms on various data sets, adjust with the results obtained from the experiment and finally write your research report based on your findings of the problem.

People working as AI researcher have a thorough understanding of the fundamentals of this technology and they apply this understanding to develop algorithms to solve a problem, these algorithms are then tested on multiple data sets before coming to a general conclusion. The day to day task for an AI researcher includes algorithm building, natural language processing, building data sorting, and organizing mechanisms. AI researchers also have an in-depth understanding of multiple computer programming languages that helps them to do their task with relative ease.

A large number of AI specialists work in areas of applied AI where they program computer smart systems. These systems are used with progressive gadgets that help to perform tasks like voice recognition, facial recognition, and other complex assessments. The AI research scientists are responsible for designing, undertaking and analyzing relevant data and information.

Conclusion

The work of an AI researcher broadly includes engaging in behaviors to solve real-life challenges. While identifying the problem it is also important to identify the bias attached to it and to exploit the bias in the AIs to learn some important aspects of it.

The learning and insights are further applied to real-world data samples to make an observation and then modify accordingly as per the evolving nature. The three important aspects of it are to design an intelligent system to solve real-life problems, to understand the fundamental properties and know the limits of the system and to imitate the natural intelligence found in humans and animals into man-made machines.

What Are The Things You Need To Know About Cluster Programming?

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The term clustering or cluster programming is used to refer to a group of servers that are connected via networks, software, and necessary hardware equipment to act as part of the same single system.

Clustering aims to make a group of computers look like they are one single entity to the rest of the world. You can learn cluster programming as part of any Data Science Training module at any certificate course.

With the evolution of big data and analytics, information is also constantly evolving into a more dynamic and abstract concept. To further learn cluster programming and its working as a computer code, you need to understand certain things about clustering:

Limits of parallel computation and Amdahl’s Law

In 1967, Gene Amdahl, a computer scientist, presented a paper to talk about parallel computation and the limits of parallel computers, at the AFIPS Spring Joint Computer Conference that year; this eventually was what came to be known as Amdahl’s Law. This law is used to describe the set limit of the maximum speedup that is achievable for a problem that is mixed, with components that are both serial and concurrent. This further explains how for a that is problem computational, parallel and concurrent upto 95 %, there is 5% remaining that is computed serially. In this case, the maximum speedup that can be reached is 20.

How to cluster computing is different from parallel, cloud, grid, distributed computing

It is essential to understand how cluster computing is different from all other kinds of computing. Distributed computing relies heavily on parallel computing however parallel computed doe not require a distributed computing for it to work. Parallel computing most commonly works in most desktop computers that are standardly available in the market with multicore processors.

The software is average and mostly written in programming languages that are multiparadigm and concurrent.  Parallel computing works on sharing resources with and distributing them among a large number of computers, connected together in a network. The most basic difference between grid cluster and cloud computing lies in the fact which decides how these resources work together and how they fit in.

Why is Cluster Programming so important?

Cluster Programming deals with combining the resources from a distributed computer network to service a single task or a single user. Clusters are high utility owing to the following factors that only level up their importance in the world of data science.

Higher availability

The world of data science is vast, where core systems need to be functioning all the time and front-end web servers need to be running. In instances like this, high availability is a must-have. This is exactly where clustering fits in. It not only provides backup that is transparent in nature but also ensures high-speed delivery of systems, data, and peripherals.

 Server Integration

While a server can be purchased to handle an organization’s sole and personal needs, however, all processing needs cannot be magically solved by one single computer in the cloud. Often, server resources need to be customized for certain applications to meet a company’s needs, which are:

  • Web application servers
  • Data transaction servers
  • Appliance servers

Applications that work withing all these classes need to share and update real-time data o be in sync with each other. Functions are being centrally arranged instead of following the traditional model of being connected to individual servers which also means, scalability is soaring and working is getting more efficient. Cluster programming plays its role here in seamlessly bringing together all of these three servers along with making the network more centralized and storage of recourses more compact. Young enthusiasts can learn cluster programming by pursuing a course in data science with Spark.

Want to Learn Advanced Analytics For Marketing Purposes, In What Order Do You Need to Learn Big Data?

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Generally, Data Analysis is a comprehensive process that involves taking unstructured information, inspecting it, cleansing, and then transforming key insights in a structured form. With advanced analytics, we use these data to further find specific patterns and draw conclusions that assist a large organization to make precise decisions for growing in a positive direction.

Data analysis nowadays is used across several businesses with a different approach, diverse techniques, and methods to help them make a precise decision for improvising efficiently. At Imarticus Learning, we help new age professionals to learn advanced analytics with dedicated courses to upskill them to match the corporate world requirements. 

A simple data project follows this structure in the form as:

SQL for Extracting and transforming data,

Tableau for Data Visualisation & insights building as Hypothesis building,

R for Statistical Data Analysis with Bivariate & univariate analysis of variables, and

Python for Model development / Hypothesis testing.

Data analyst professionals deal with a very high amount of data daily. The first step is to learn SQL to analyze, extract, aggregate, and transform data for a more purposeful understanding. So, as a professional working in the data analysis field, SQL is the foremost priority to learn and manage data properly.

These datasets can have 1 million+ rows, here Tableau will work on visualizing data to bring insights or hypotheses. With Tableau, one can effectively track a million rows of information in data form to create useful insights.

R is another programming language used specifically for data analysis with the environment suited for statistical computing. One can also visualize data, blend data, build a statistical model, and perform complex transformations. R language is also preferred for developing statistical software so data analysts must have an understanding of its effectiveness.

Python is a general-purpose high-level programming language that most coders prefer to use. Python is used to develop algorithms from these large sets of data variables with scripts that make effective management to find relations and goals from the data itself. One must learn Python programming for building a sophisticated career in data scientists. 

Although a data structure follows a specific path from SQL to Tableau to R to Python, still the goal and objective of the project define the purposeful use of that language. SQL helps us to query data properly; with Tableau, we learn to visualize data, R is better for exploration, while Python works better to get high production.

A well-organized course can help you to understand the right purpose for each of these languages precisely. Though an individual may not have expertise in each of these languages still, if you are opting for a career in Data analysis, you must understand the scope of SQL, Tableau, R, and Python to grow in the right direction.

At Imarticus Learning we offer several programs for professionals to learn advanced analytics and offer their expertise to the corporate world with definite preparations as well as courses to match their expectations. 

How Do You Make A Career From Python Programming?

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Ease of use, versatility, powerfulness, and flexibility make Python an all-in-one choice for novices and masters alike. However, this is not the end of the language’s prominence. Being open-source, it is a significant attraction for the developers worldwide and rightly so. From data science to artificial intelligence and mobile app to web app development, Python presents a win-win situation for those who know how to consolidate it with the requirements. For this exact reason, it is often regarded as the successor to C++ and Java. 

Making a career in Python

For an individual who is new to the programming world and endeavors to make a career in the same, there is no better time to opt for Python programming training. With continual learning, a person can, assuredly, pave his or her way in a very competitive market. Here are the steps you should follow to make a career from Python programming.

 

  • Opt for an ideal course

 

It is crucial that you start your learning from day one and with Imarticus Learning at your doorstep, why wait? Opting for python programming training would mean that you are potentially aligning yourself with the demands of the market. Besides, you will get to know about the technologies closely associated with the use of Python.

 

  • Utilize the abundant libraries that Python facilitates

 

The open-source nature of Python makes it one of the most productive languages in the world with regard to the diverse libraries and packages. Because everything is inbuilt, unlike C or C++, using Python can be relatively easy. Thus, there is no need to have an in-depth knowledge of the complexities associated, something that most novice developers dream of.

 

  • Leverage the flexibility and readability

 

If you are someone plying your trade as a software intern, you are possibly in for a roller-coaster ride with Python programming. The fact that it stretches to numerous domains, including Data Science, Network Programming, Application development, and Artificial Intelligence, makes it apt for someone on a learning curve.

 

  • Become a data scientist

 

Python programming is widely preferred for data science. In fact, machine learning can be entirely practiced with the use of Python. This is due to the relatively easier syntax, regular open-source enhancements, and the efficiency of code. Python is potentially capable of rendering optimal solutions to complex problems. Additionally, with data science in demand, knowledge of Python is like digging a gold mine.

 

  • Use python programming for app development

 

Again the access to numerous frameworks, libraries, and packages make the language suitable for both backend and frontend app development. Mobile app development, in particular, is a booming domain with more than 250 million users on average. From impeccable functionality to first-class animations, Python is an all-round player for mobile as well as web app development.

Making a career with Python is not that intricate; however, that does not imply that it doesn’t require strategizing. Amidst the ever-increasing competition, it is essential that you are well-versed with an in-depth knowledge of the language. For this, you should undertake a Python programming training, which can assist you in recognizing the peculiarities of the market and the employment of Python. 

What Are The Ways Big Data is Being Used To Create The Next Generation of Mobile Apps?

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Big data tops the charts when it comes to providing a considerably better user experience by increasing app engagement and optimizing resources correctly. While it not only makes content for users more relevant but also personalized content and when analyzed from a business point of view, it improves conversion rates. To put it in simpler words, the future of big data is the gold mine that app developers need in providing information and creating apps that users want.

Here is a breakdown of the various ways in which big data is being used to create the next generation of mobile apps:

Seamless and easy to use UX
Big data is incredible in providing insights that help tract every movement of a user by crunching numbers to improve overall user experience. Additionally, it also helps in signaling developers when apps do not meet either the design standards or the UX. Studies suggest that most app users stick to or delete an app based on its user-friendly quotient, which is the ease of use. This kind of information helps big data constantly make improvements for user interface and reduce friction.

Machine learning and artificial intelligence

With the help of machine learning and usage of artificial intelligence, big data can recognize failure patterns if any and suggest improvements.  Also, this helps understand any glitches that might be acting as slowdowns, including loading time for a website or a page.

Predictive analytics and customization
Big data helps customize the user experience and deliver content based on previous usage patterns. This is where predictive analytics come into play by suggesting what you should buy or what you should watch. This gets increasingly better as you consistently use a particular service.

Widely used by companies like Netflix and Amazon, predictive analytics shows up an image or shows pricing options based on user data buying patterns and more. Basics of predictive analytics are taught during a Data Analytics Course.

Increase app engagement

Users often get more engaged with a particular app and keep returning to it frequently. A term referred to as- app stickiness, this actively engages customers more than its competitors and factors like duration session, the flow of content on the screen and churn tracking help in contributing to stickiness.

Real-time analytics

Real-time analytics help an app developer to analyze data related to that app and make dynamic changes based on the present situation. The mobile app market in itself is a pretty dynamic one, where things significantly change every minute. Organizations are using real-time analytics o predict patterns that include flying for airlines when visibility is good, avoiding certain roads to get rid of traffic, avoiding extreme weather conditions, sharing driver and customer live locations, estimates fares at a given point in the day and more.

Evolve marketing strategies

Big data can help make better marketing strategies, by capturing user data that helps app developers understand the kind of people their users are. Existing strategies are reworked on to reach out to new users and rearrange older users. Study of user demographics, buying patterns social behavior of apps, posts liked, websites visited, all of which can be used to build individual user personas which are then used to strategize marketing strategies.

Considerable cost reduction

Lastly, big data helps understand and predict app development costs, since building a standard app might often be time-consuming and quite expensive. This not only includes the app development process costs but also calculates the number of developers, designers, testers and more will be needed to have an app up and running. Additionally, the longer time it takes to build the app, the higher the cost graph goes.

Is Statistics Required for Machine Learning?

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What is Statistics?

Statistics is a branch of mathematics that is used for comparing and analyzing various data points and the numbers associated with them. It also includes the study of numbers and drawing out insights from those numbers. Some of the statistical measures include average, median, mode, variance, standard deviation, correlation, regression, etc. Some of these help in analyzing single sets of data while others are used in comparing two or more sets of data and then making a comparative analysis in the form of trends and patterns. Often these tools are also brought into play when it comes to predicting future numbers.

What is Machine Learning?

Machine Learning is the application of artificial intelligence where the systems are programmed to perform a specific set of tasks. The computers are programmed to function automatically depending on the various scenarios and come up with the required results. It enables the analysis of huge data for drawing out various business insights.

Also, it makes the sorting and analysis of data quick and easy as the automation is brought into play with the help of machine learning. It is a really powerful tool in this data-driven world of today. It collects data from various sources as given by the algorithm, prepares it for analysis and then evaluates this data for bringing out insights and also throws light on various performance indicators in the form of patterns and trends.

Statistics and Machine Learning

Both Statistics and Machine Learning deal with the analysis of data therefore one could guess that the two areas are interrelated. Various statistical methods are used to transform raw data and bring out various results. Many believe that knowing Statistics is a prerequisite for understanding Machine Learning. Statistics is important as the data sets have to be created which can be easily made if one has prior knowledge of Statistics. Also, with the help of statistics, the observations are transformed and put to good use.

Machine Learning has a deep relation with Statistics and the elements of statistics such as the collection of data, classification, and sorting of data, analysis of data, etc. Predictive modeling can be done by someone who at least has a basic understanding of Statistics. Machine learning is also known as “Applied Statistics” as it practically uses various statistical theories and principles to drive growth and various results.

Data analysis is important for machine learning and statistics is an art of handling data. It is the primary skill that drives machine learning algorithms. Statistics plays a very important role when it comes to machine learning. One needs to know about the various parameters on which the data shall be analyzed to bring out desired results.

Methods such as Correlation and Regression are often used to compare various sets of data and these tools are built into algorithms with the help of machine learning so that these numbers of comparison can be automatically calculated and a comparative study can be made based on these numbers. Learning Statistics before getting into machine learning is the best way to go about it. Various Machine Learning training will also give you an idea about statistics and how it is applied to Machine Learning.

Conclusion
Machine Learning and Statistics are two parts of the same coin. Machine Learning makes use of statistics for sanitizing data and on the other hand, Statistics is given a practical shape and is made applicable with the help of machine learning. Therefore, it becomes easy to conclude that one must have at least a basic understanding of statistics to understand the aspects of Machine Learning.

Understand The Random Forest Model in Data Science

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Data science is used for predicting smart solutions to modern problems nowadays by processing big data. But that processing is a very tedious job. The data produces in such cases are large, unstructured chunks of data often referred to as raw data.

This unstructured data has to be classified and separated into different clusters of data. Random forest model is a classification algorithm offered by Data Science Training that arranges data in a structured way using decision trees. It is ranked highly among other classification algorithms because of its high performance and efficiency. Let us know more about this algorithm and its working.

To dive deeper into this algorithm, we must have a pre-requisite about decision trees. A decision tree is a way of dividing a data set into different categories/classes by mapping the elements of the given data set in a tree based on decisions and at each level of the tree a question is asked which leads to the branching of the tree into several categories. For example, suppose we have a data set of 1s which are of two colors and are either underlined or not. Now, we have to make a decision tree and classify the given data set into various categories.

Given data set = ( 1 , 1 , 1 , 1 , 1 , 1 , 1 )

Decision tree –

1  1  1  1  1  1  1

1  1  1                         1  1  1  1

1  1                                   1

As you can see in the above figure that on the first level of the tree a question has been asked i.e. is 1 red? Based on this question the 1s are divided into two categories and then based on whether the 1 is underlined or not, the branching of nodes is done on level 2. So, it is a very simple yet powerful means of data classification and helps even more when the data set is huge.

When the data is large in volume then a lot of individual decision trees are made from the given data set. These decision trees have classified the data depending on their attributes and characteristics. Once the trees are made, they are brought together to form a forest of trees which has different sets of data. These trees act as a community and serve their purpose of data classification. Together, they perform very well and give far better results as compared to other models/algorithms.

These individual trees in the forest perform as an ensemble which is further used for predictive analysis and other data science operations. The outcome of this model is uncorrelated. Uncorrelated outcomes do not affect each other and as we have many trees, the accuracy of our prediction increases. There are ways to ensure that the trees don’t affect each other i.e. the trees should be uncorrelated to each other. It is done in two ways that are feature randomness and bagging.

In bagging, different trees are made by slightly changing the sample data set which is random, as the decision trees are very sensitive even to a slight change in the data set. This ensures that the trees are uncorrelated. In feature randomness, whenever we branch the decision tree, we use that property of the data set which results in the highest number of branches. If we have numerous possibilities then we predict with more accuracy using each and every value the given data set can possess.

Conclusion

The forest serves as a great deal to analysts and is widely used. Each decision tree in the forest is made by changing the data set. The change in the data set is done through random values that replace the original data set and thus create more possibilities and a way for better and accurate prediction. This article was all about the random forest model for data classification in data science.