Contact Us
×

Category: Analytics

R – What’s in it for me?

Posted on by Imarticus Learning

R is a programming language widely used in data analytics, research and statistical computing. It can be used to retrieve, clean, analyze, visualize data, which makes it a hot choice of data analysts, statisticians and researchers. What makes R so popular is the ease of presenting the results as a presentation or a document.

Its syntax is very expressive, and its interface is very user-friendly which increases its popularity year after year. Here is why you should learn R and what is in it for you. Considered as one of the best tools for data scientists, R is considered as the bridging language of data science.

According to the survey conducted by O’Reilly Media in 2014 to learn about the popular tools among the data scientists, R turned out to be the most popular amongst the programming languages.

Why is R Used in Graphics and Statistical Computing?

  1. R Programming is an Open Source

Most of the R packages are licensed under GNU General Public license terms and you can download it for free and use them even for commercial purposes

  1. Cross-Platform Interoperability

In today’s technology-driven world, it is very important for any program to be flexible and adaptable. The ability to be able to run on popular platforms like Windows, Mac, and Linux makes R a popular choice.

  1. Career Prospects

Data science training and proficiency in R is highly desirable for software job openings. It makes you stand out from the crowd when you apply for a job.

  1. Popular Programme Among Tech Giants

Popularity and preference among tech giants show the potential of a programming language. R exhibits great potential this way. Better data analytics makes R a hot choice for many companies to aid them in the decision-making process. Learning R thus increases your chances to work with market leaders.

Companies Using R

As mentioned earlier, R is the hot choice amongst the market leaders. Listed below are some examples of renowned R users and an indication on how it helps them.

  1. Facebook – To analyze user behavior by considering profile pictures and status updates.
  2. Google – To enhance the effectiveness of ads and economic forecasting.
  3. Twitter – To visualize the data and for semantic clustering
  4. Microsoft – Uses R for a myriad of purposes that it eventually acquired Revolution R company!
  5. Uber – To analyze various user statistics
  6. Airbnb – To scale data science.
  7. IBM – The extensive application of R made them join R Consortium Group
  8. ANZ – To create and analyze credit risk modeling.

Real-World Application of R Programming

  1. Data Science

R programming facilitates real-time data collection and thus, makes it an extremely useful tool for data scientists. They can perform predictive as well as statistical analysis with these data. It also helps to create visualizations and to effectively communicate the results to respective stakeholders.

  1. Statistical Computing

R is very simple highly user-friendly that even a non-computer professional can import data from requisite sources and analyze them to create better results. The excellent charting capability of R program helps you to create good visualizations also has charting capabilities, which means you can plot your data and create outstanding visualizations from a given dataset.

  1. Machine Learning

R programming has found its application in machine learning as well. Machine learning professionals use R to implement the algorithms in various fields including marketing, finance, retail marketing, genetics research, and healthcare to mention some.

Conclusion

Most suited for graphics, statistical analysis and data visualization R is the most desirable tool that is leading the world of computer programming. One of the most preferred programs by the market giants, Learning R offers better career prospects.

Top 7 FAQs About Business Intelligence For Beginners: Answered

Posted on by Imarticus Learning

Top 7 FAQs About Business Intelligence For Beginners: Answered

Business Intelligence is referred to as the applications and practices used to represent business data. These practices help in making better decisions and also help in developing the business. The Business Intelligence system helps any particular organization/firm to collect, store and manipulate data/information in such a way that it helps in the growth of the business. A lot of questions are asked regarding this field, especially by the newcomers. Let us look at some frequently asked questions about business intelligence and their answers.

Q – What are the skills required for Business intelligence?

A – A person must have soft skills like communication and writing skills, problem-solving approaches, etc. Besides soft skills, one must know how to use various analytics applications such as SQL, ZOHO, Microsoft Business Intelligence, etc. He/she must be able to analyse the given data and use it for business development. A person should be good at taking business-related decisions. These skills are enough for a beginner but you will have to learn more and more if you want to grow after settling in this field of BI.

Q – Where to start learning about business intelligence?

A – There is no shortage of books and resources available for learning Business ntelligence. Some books like ‘Successful business intelligence’ by Cindi Howson, ‘Business intelligence roadmap’ by Larissa T. Moss & Shaku Atre, ‘Business intelligence guidebook’ by Rick Sherman are regarded worldwide as some of the best books on Business Intelligence. Besides these, you can get many post-graduation courses and online courses on Business Intelligence. The IIBA (International Institute for Business Analysis) also offers certification courses and resources on business intelligence. If you just need to add Business Intelligence as a skill in your resume, go for online certification courses.

Q – What are the top job roles in Business Intelligence?

A – Some top job roles in Business Intelligence are as follows –
• Business Intelligence project manager
• Business Intelligence specialists
• Business Intelligence consultants
• Business Intelligence director
• Business analyst

Q – What is the future of Business Intelligence?

A – The backbone of business intelligence is data and information. As you can see big data and its analytics is expected to grow more and more. According to reports, there will be a rise of 14% in the hiring of Business Intelligence experts in 2020. The Business Intelligence tools and applications are giving more accurate results day by day. The big data is supposed to keep flowing with an increased rate in the future too, so to collaborate and manage this big data, analysts will be required. The future of Business Intelligence is quite vast.

Q – What is the difference between analytics and Business Intelligence?

A – It may be confusing in starting as both these terms are used simultaneously. Business Intelligence is a subset of business analytics. Analytics uses the generated data to fulfil current business needs. It helps in increasing productivity and making better business decisions. Whereas Business Intelligence only focuses on current business needs.

Q – What is the average salary for job roles in Business Intelligence?

A – In the USA, the average business analyst salary is $68,075. In India, it is ₹ 7,98,671 per year. The salary also exceeds as you step up, the Business Intelligence Directors in India have an average salary of ₹30 lakhs per year.

Q – What are the focus areas of Business Intelligence?

A – Business Intelligence focuses on the following sectors
• Customer satisfaction level.
• Smart business predictions.
• Cost customization.
• Cross-selling and up-selling.
• Market share analysis.
• Defect/loss reduction.
• Profit and revenue management.

Conclusion

Business Intelligence helps in making smart business decisions that increase the profit as well as the market share of the firm/company. It also helps in finding customer satisfaction levels and customer loyalty. This article was all about FAQs asked regarding business intelligence and its answers. The questions and answers are framed for the beginner level. I hope it helps!

Also Read: Difference Between Business Analyst & Business Intelligence

What Is The Quickest Way to Learn Math For Machine Learning and Deep Learning?

Posted on by Imarticus Learning

Synopsis

Math is integral to machine learning and deep learning. It is the foundation on which algorithms are built for artificial intelligence to learn, analyze and thrive. So how do you learn math quickly for AI? 


Machines today have the ability to learn, analyze and understand their environment and solve problems on the basis of the data given to them. This intelligence of the machines is known as artificial intelligence and the ability to learn and thrive is known as machine learning. Algorithms form the crux of everything you do in technology and a Machine Learning Course provides you with an understanding of the same. 

 

Today, individuals who are proficient after completely a Machine Learning Certification is highly sought after and employed. Companies invest a large sum of money to have professionals trained in AI as the applications of AI are vast and cost-effective.  It is a lucrative career to pursue one that involves complex and challenging problems that need to be solved in creative ways. 

 

Mathematics forms the foundation of building algorithms as all programming languages use the basics. Binary code is the heart of machines and the language used to teach them things is the programming language. So do you pursue Machine Learning Training, and also learn math quickly at the same time? 

 

Here are a few ways to understand how math is applicable in AI 


Learn the Basics 

Important sections such as  Statistics, Linear Algebra, Statistics, Probability and Differential Calculus are the basics of math that one needs to know in order to pursue learning a programming language. While this may sound complicated, they form the basis for machine learning, so investing in courses that teach the above-mentioned functions will go a long way in programming.  There are plenty of online resources that are useful repositories when it comes to learning math for deep learning. 

 

Invest Sufficient Time

Learning math depends on the ability to absorb and apply the math learned in machine learning. Applications of statistics, linear algebra is important in machine learning and hence investing 2-3 months to brush up on the basics go a long way. Constant applications of the lessons learned also helps when it comes to math for AI. Since the principles are the same but the various derivatives and applications can change with the algorithm constant practice and brushing up will help while learning the code. 


Dismiss The Fear

One of the biggest ways to learn math quickly for machine learning is by dismissing the fear associated with numbers. By starting small and investing efforts, one can move forward in the code. Since there is no shortage of resources when it comes to learning math, taking the initial step and letting go of any fear towards the subject will greatly help. 


Conclusion

Learning a programming language whose principles are based on mathematics can sound daunting and tedious but it is fairly simple once you understand the basics of it. This can be applied while programming for machine learning and artificial intelligence. 

Certified Scrum Product Owner: What to Know and Who Can Become?

Posted on by Imarticus Learning

Businesses are finding new ways to increase the productivity and quality of their services or commodities offered to the customers. The product offered by the firm/company should meet all the necessary regulations and guidelines. There must be room for development. In this article, we will see what a certified scrum product owner is and how to become one.

Basic Terminologies

Scrum

Scrum is a framework on which people can collaborate among themselves and find solutions to problems. Scrum focuses on increasing the quality of services and teamwork. It is a simple platform with less complexity. Simple and basic solutions that are visible in the current scenario are taken into consideration in the scrum and then iterative work for increasing quality is done until the end goal is reached. People can complete a project more quickly and effectively using scrum. Jeff Sutherland made the first scrum project in 1993.

Product Owner

A product owner is responsible for increasing the value and quality of the product made by the scrum development team to the fullest extent. Market analysis, product designing, business strategy formation, etc. are some of the major roles of a product owner. A product owner is responsible for increasing the Return on Investment (ROI).

The Certified Scrum Product Owner

It is a certification course offered by the scrum alliance which helps us in becoming a product owner. You can find suitable courses from the scrum alliance website and then you have to attend a 16-hour course in which you will be taught about your roles and responsibilities in detail. There is an online exam after the training which is not famous for its toughness. The passing percentage for certification is 65%. If all goes well, you will acquire your CSPO certification. The license offered has a limited warranty. After the time duration expires, one has to renew his/her CSPO certification. Currently, the CSPO certification is valid for two years.

Prerequisites needed

There are no such mandatory skills or prerequisite which you will require for acquiring CSPO certification. But then again, if you know nothing about business analysis and strategies, then you can still acquire CSPO certification but in this era of competition, where will you stand? Also, anyone who is interested in taking the course can go for it irrespective of age, gender, and educational qualifications.

CSPO & PSPO

Both are certification courses offered specifically for product owner but there is a slight difference. CSPO is offered by the Scrum alliance whereas PSPO (Profession Scrum Product Owner) certification course is offered by scrum.org. The PSPO certification does not expire and the exam has a passing percentage of 80%. Often people confuse among these two. It is better to evaluate yourself first and be aware, and then choose the right certification for you.

Importance Of CSPO Certification In Personal Growth

The certification is a proof that you have attended the classroom session and have also cleared the exam. If you have decided to become a product leader, then this certification course will add value to your resume. The companies will see to it and you may get an edge over other candidates. This is a basic course which boosts your skills as a product owner, once you have taken it you can also take advance certification courses to grow further.

Conclusion

There are a plethora of online courses available on the internet. CSPO certification is one of them which can enhance your way of working as many new things are taught during this certification course. Your ideas will have more weight when a professional course is added to it. This article was all about certified scrum product owner and how to become one. I hope it helps!

Essential Skills For A Business Analysts Who’s New To The Business

Posted on by Imarticus Learning

Business Analysts s are very important persons in the field of business. They help in the growth of business and market share through analyzing data and give smart predictions and solutions for various problems. Many skills are embedded in good Business Analysts. In this article, we will see which skills are required for Business Analysts who’s new to the business.

Soft-Skills Required For A Business Analysts

Communication Skills

Communication is the key to finding solutions. In any field, you can perform better than others if you’re ahead of them in communicating. You can understand the point given by any person more clearly. You can take part in team meetings actively. One can also share his/her ideas more profoundly if he/she can communicate clearly.

Problem Solving Skills

A good Business Analyst will try to find the root of any particular problem and with communication and arguments; he/she must be able to solve it. The approach towards solving a problem is seen and monitored in a good Business Analyst.

Brainstorming Sessions

A good Business Analyst must ask questions that are to be answered for the betterment of the business. In team meetings, brainstorming sessions, a Business Analyst must show his soft skills of critical thinking in which he can find out loopholes and problems in the business environment and can put them in front of people until the solution is found to that particular problem. Some people may also call it constructive criticism.

Relationship Builder

A good Business Analyst tries to build business relationships with clients, superiors, customers and other stakeholders. He/she tries to learn from each of them and a good business relationship means he/she can take help from them to improve his/her work.

Other Relevant Skillsets

Fluency In Using Business Analysis Tools

Microsoft office applications are good for a beginner. For someone new to this field can learn office word, PowerPoint, etc. Many further applications are there such as Microsoft Visio which is to be learned by a Business Analyst which will give them an edge ahead of others.

Understanding Various Levels

There are various levels in a business environment like Business level, Software-level, and Information-level. These levels help in understanding the flow of data and information which results in finding effective solutions and accurate analysis of data/information.

Writing Skills

It helps in making good documentation and brings clarity. People can read your work and if they understand it easily, it will establish the prerequisites in their minds and the next time you talk to them about any particular idea, they can understand and grasp better.

Methodology Used

Many methods can be used for business analysis such as Six Sigma, Rational unified process, etc. A good Business Analyst must learn that method more profoundly which is used by his desired organization. Also, if you know more than one approach to solve a problem, it will never go in vain.

Domain Expertise

A good Business Analyst knows about the industry dynamics of their business. They must identify the domain in which their organization operates and has expertise in. Redundant information will not help to progress but finding new techniques and solutions for any particular domain/industry will help it to grow.

Conclusion

Besides the aforementioned skills, there is a lot to learn. Learning never stops. These skills, however, will be enough to get you started as a Business Analyst. These soft skills and technical skills will help you in bridging the gap between problems and solutions. As a Business Analyst, you look for ways that can enhance the business of your organization as well as developing personally simultaneously. This article was all about the skills required for a Business Analyst who’s new to the business. I hope it helps!

What Are The Steps To Become a Data Scientist From a Non-Technical Field?

Posted on by Imarticus Learning

A data scientist is a professional who is in the authority of collecting, analyzing and understanding large amounts of data. This job deals with advanced analytics technologies like machine learning and predictive modeling. Some of the basic responsibilities of this role include collecting and analyzing data, using different types of reporting tools to detect the trends, patterns, and relationships in various data sets. In today’s scenario, a data scientist is one of the best professions to pick as a career.

Scope of a non-technical person as a data scientist

To understand the scope, firstly, the term non-technical person should be defined. It refers to a person from a non-engineering background. Basically, the person may be from any educational background but should have the right approach. He or she should be ready to put in a lot of time and effort. Self-motivation is a must to mentally prepare oneself to learn whatever is essential to become a successful data scientist.

Try enrolling in a good data science course to give shape to the career. Eventually, you will realize the time invested in learning the matter will prove to be one of the best long term investments in your career.

Steps to become a data scientist from a non-technical field

Coming from a non-technical background, to become a successful data scientist following this six-step guide can prove to be really helpful:

  • Broaden the skill level with the help of a planned course – For those who are a fresher in the area of data science, they should enroll in a well-structured course. An ideal syllabus should cover the basics of the programming languages R and Java, Big Data handling, deep learning a part of machine learning, data visualization, probability, and statistics.
  • Get in touch with some mentors in this field – When venturing into any new field, a mentor’s guidance plays an important role in guiding one through the best path. Getting in touch with an experienced person helps in building a network and getting valuable lessons.
  • Try to attend every event held in the town or online – Attending such events are a great way to gather information from industry experts in this In-person events are better as that has a scope of open conversations.
  • Appear for mock interviews – Only preparations are not enough. When one is looking for a job opportunity in data science, having a basic idea of what the hiring managers are seeking, is very important. Attending mock interviews is the best way to measure one’s expertise level.
  • Never compromise with the basics – If one is serious about a career in data science, then familiarity with any one of the basic areas is really important. This helps in tuning the intellectual capacities of analyzing and interpreting data.
  • Stay open to learning new things – In the field of data science, remember there is no end to learning. So, don’t hesitate to learn new things from your peers or seniors as you move ahead in your career path. But remember there should be a methodical approach in whatever you do. Keep strengthening your basic knowledge and read books related to data science.

Conclusion

Following these simple steps will make one’s transition from a non-technical field to the domain of data science not only interesting but also hassle-free. To get the real feel of this process of shifting, it is recommended that one checks out the online videos of some real-life examples of people who made it possible and are successful data scientists today.

How AI and Big Data Can Be Used to Fight Against Coronavirus?

Posted on by Imarticus Learning

COVID-19, a deadly virus that originated from Wuhan, China, has been declared as a pandemic by the WHO lately. The whole world is in quarantine to stop the spread of the on-going pandemic to further extend. The world has united to fight against the common cause. The results are most anticipated from the AI and Big Data to sustain through this so uncalled time.

Artificial Intelligence training is already helping many countries to fight against coronavirus and executives from Amazon, Google, Microsoft, and Apple met officials at Downing Street recently to discuss their role in the Coronavirus crisis. It is no secret that “Data” is the new gold; it is no less than a miracle that even on such a large scale shutdown of the economy the countries are doing well in providing necessities to the citizens. It is done by proper modeling and tracking of data.

What is modelling and tracking data?

Machine learning (ML) an advanced version of AI, has come to play a significant role in fighting CONVID-19. Five years ago, many were asking whether these models could be used to optimize corporate performance but now is the time when these models are helping daily to fight against coronavirus. Tracking the data using parameters and altering the matrix could come in handy in maintaining the resources and handling the outbreak in a more optimized way possible.

How to use the available resources to fight against the coronavirus?  

Countries like South Korea have an advanced digital platform for big data mining and they are already running government-run big data platform that stores citizen information and monitors foreign nationals and integrates all hospitals, government organizations, final institutions, and all other services too.

AI and Big Data have surely revolutionized the approach to fight the outbreak. Tracking and forecasting the path of infection and detect the most infected area to send instant help to limit the spread.

The difference is the quality of the data

Pumping huge amounts of data into AI and machine -learning systems is no guarantee of success and it makes it difficult to ensure that people focus on relevant information and not get mislead by hysteria. A recent update by Facebook stated the concern about the public reaction on the outbreak. They told us that they are monitoring people’s response to this outbreak and detecting the most affected areas across the world. This has come to be of great help in monitoring the outbreak on a global platform.

Using fresh data in these circumstances is of high priority as early detection of the virus can save other people from getting infected. Many countries have also introduced a quick reaction team and total isolation chambers to limit the contamination. Many drive-through labs are operational where you can get your results while sitting in the car and get treatment instantly if infected.  AI and Big Data-based start-ups are busy in making thermometers which can detect CONVID-19 at early stages.

Finding the cure using AI and Big Data Analysis

Exscienta, a British start-up became the first company to test AI-designed drug molecules on humankind. There are some limitations in finding the cure as it takes a long time to study the pattern and create algorithms

Conclusion

AI and Big Data have surely revolutionized the campaign again coronavirus in all aspects possible be it keeping the people comfortable and safe in quarantine or let it be the fight against coronavirus on the front ground and it’s no wonder why AI and Big Data analytics is booming globally and many companies are shifting their focus towards this upcoming mega technology.

Why a Business Analyst is Important?

Posted on by Imarticus Learning

Business analysts (BA) can boost the productivity of project crews and this is, at times, ignored. While the development crew prepares the technical solutions, BAs offer insights, answer questions, remove hindrances and make sure that the technical solution is working well to cater to the expectations of stakeholders.

Business analysts not only bring immense value to every division that is a part of a project but also to the clientele. But having said that, people do ask about these values, and so these are some of the factors that highlight the importance of business analysts.

Curbing Overall Project Investment

Business analysts are essential in a company as they aid in curbing capital expenses (CAPEX) and operational expenses (OPEX). Even though it might look like the company is spending more cash as they would have to recruit and pay business analysts, yet eventually, BAs can help reduce the overall expenditure on the project they are contributing to. In a way, they help slash the project expenditure by eliminating the need for further re-work.

Consider this, when developers begin coding for a business runner, things might not go as expected and they might have to perform a re-check. In other words, anything that starts simple can scale up in its complexity level after customers’ requirements come in and you will see yourselves redoing the components you initially began your project with.

Business analysts, thus, can help address this rework as they are aware of the demands of business users and know how to convey them to the developers. This ward off project delays which can cost a fortune to organizations.

Secondly, it requires a considerable amount of effort for companies to discover the objectives of the project. This translates to regular meetings, which not only bother their bank accounts but also is a time-intensive affair.

This is when business analysts come in to play; they form a sound decision-making model; remind others about any suggestion they have made earlier and bridge those communication loopholes between various divisions contributing to the project. BAs would avoid myriads of meetings from taking place, which would ease off the burden on business expenses.

Improving the Odds of Potential Margins

Enhancing the value of projects can aid improve the possibilities of getting outsized returns as they are becoming more robust. Having the development crew create an array of, let’s say, 50 tasks that need to be achieved and sorting them by the domain of the system can result in further problems in the project as they might not have been sorted by value.

You might be giving priority to those things which actually are not that important by doing this. Not driving attention to prioritization leads to the devaluation of your project. Prioritization is among the crucial skills companies would desire in business analysts. With BAs in your organization to address the project requisites, the value of your project would scale up and, in turn, ensure potential margins.

Filling Communication Gaps

Having a well-experienced business analyst can be really beneficial for developers as they are solely focused on coding. Else, it would hamper the productivity if they interact with business users and understand their lengthy requirements in time-consuming meetings.

Developers would like to create a solution before going through the entire list of requirements that are essential for a project and, at times, this activity is not much appreciated by business users. As such, it creates inconvenience and confusion among business users and can negatively affect the overall project.

But BAs understand the stuff the developers have to go through to gill the loophole between technical and business requirements. Even though developers are highly capable to collaborate with business runners, it can lead to delays in projects and revamp because of the miscommunication.

Business analysts, hence, will bring value to organizations as they know technical as well as business demands. Moreover, they can communicate with developers as well as business users to make sure that there is no more any project delay.

 

What Is A Data Scientist’s Career Path?

Posted on by Imarticus Learning

The Data Career trajectory is probably the hottest career option you can do right now. As Glassdoor’s latest report shows, the $ 108,000 base salary is not only attractive to job seekers, but the Data Science career also boasts 4.2 out of 5.

Data Science Pipeline

A data science project is a whole process. It is important to understand this fact to get out of the labyrinth of data science.

Data science is not magic!

Embarking on a series of steps systematically first, the project goals are reached. Have you identified attractive business issues or market opportunities? You need to clarify what your company is trying to help you gain a competitive edge.

Next, you need to know where to collect data, plan resources, and coordinate people to do their job. The third part is data preparation. You must clear the data and investigate it carefully. The association begins to appear and the sample and the variable are corrected. The next step is to create, validate, evaluate and improve the form.

Finally, you need to communicate your team experience in the data science process. The data must be compelling and compelling. In the final reporting stage, visualization is essential to telling the complete story.

What did you learn?

At Imarticus Learning, the role of the data science team is not exclusive technology. Programming and statistics are essential to the basic steps in the Data Science Training, but contextual skills are essential to the planning and reporting stages. 

A role in data science

In fact, the role of data scientists is a common part of many different fields. Data scientists are highly capable professionals who have a big picture and are a data programmer, statistician, and a good storyteller.

However, the data science team counts people with different roles, all of whom contribute in different ways. If your career path in the data world is your ultimate goal, there are many ways to reach it.

For example, as an analyst, your data science career will be involved in day-to-day tasks that focus on data collection, database structure, modeling and execution, trend analysis, recommendations, and storytelling. Business intelligence (BI) analysts, on the other hand, should be able to see the trend and get an overview and state of the business unit in the market.

BI analysts usually have experience in business, management, economics or similar fields. However, you should also “interact with data”. BI analysts process a great deal of information and spend most of their time analyzing and illustrating data collected from multiple sources.

Are you fascinated by marketing issues? Marketing analysts are a special kind of data analyst. However, their main competency is associated with analyzing customer activities data with the help of special programs and not involved in programming or machine learning.

Data Science at Work

Data science training equips you with the skills for suggesting smart solutions for performing machine learning for beer and food molecules. Preparing beer with the right molecules to match the most popular meal ingredients on the market will be fun and make money. Imagine the perfect mix of top-selling beers like burgers and tikka masala!

Is Python Good For Data Analysis?

Posted on by Imarticus Learning

Is Python Good For Data Analysis?

In order to understand if Python is a good fit for data analysis, it is important to know the exact role of a data analyst. With a clear understanding of the job role, one can make better choices of the Python library tools that will fit each requirement of data analysis.

Role of a data analyst

In day-to-day life, we tend to make decisions based on our previous experiences. The role of a data analyst is quite similar; the only difference is they do it from the perspective of a business house. Data analysts are basically responsible for taking down the data and analyzing the results with the use of statistical techniques and preparing reports. They are also responsible for obtaining data from primary or secondary data sources and thereby maintaining a database. Their prime intention is to extract useful information from different data and take decisions based on the analysis.

An idea about Python

Python is a programming language of high-level, used for web development at the server end, app development, software development, system scripting, and mathematics. It is basically a programming language used for general purposes. Python allows you to focus on the core functionality of an application by taking care of the common programming tasks. It was designed by Guido van Rossum and released in the year 1991.

Python’s popularity is growing in several industries like retail banking, aerospace, insurance, finance, healthcare, etc., particularly in Machine Learning projects. It is a dynamic language supporting both object-oriented programming and structured programming. In this present IT scenario, to learn Python is probably the easiest thing and its huge library makes the tasks a lot simpler.

What makes Python ideal for data analysis?

The following points prove why Python is ideal for data analysis:

  • Data can be present in different forms. Assuming data is present in huge excel sheets with a huge number of columns and rows it is a challenge to search for a particular type of data from that sheet. It can be really time consuming and cumbersome. But with Python’s libraries like Pandas and Numpy that uses parallel processing, the task can be accomplished with ease.
  • Acquiring data is another challenge. It is not always the situation that the data is readily available. At times one needs to dig into the data from the internet, which can be a challenge. In this case, the two libraries of Python Scrapy and Beautifulsoup prove to be helpful.
  • The next stage is a pictographic representation of the data. The best way of representing the data in the visualization mode is through bar graphs, pie-charts, and histograms. Python has solutions for this too. For this, libraries like Seaborn and Matplotlib give the ultimate results.
  • Now comes the most important part, i.e. machine learning. This involves substantial mathematics like probability, calculus and matrix operations including thousands of columns and rows. All these turn out to be very simple with the help of the Python library Scikit-learn.
  • When the data is in the form of images, Python has a solution for that too. These images can be operated with the help of an open-source library of Python named Opencv.

Conclusion

So, we can see that Python forms a valuable part of the toolbox of a data analyst. With its help, data analysts can freely handle some of the toughest parts of their job making it interesting and attaining rewarding results. Thanks to the wide variety of Python libraries available. So it would be a wise choice to learn python which would help you in understanding the Data Analytics Courses.