What Are The New Advancements In Data Analytics?

Posted on February 7, 2022January 31, 2024 by Imarticus Learning

Data analytics is a field which witnesses a continuous revolution. Since data is becoming increasingly valuable with each passing time, it has been treated with great care and concern. New tools, techniques, theories, and trends are always introduced in the data analytics sector to cope with the constant changes in the industries and societies. You can opt for a sought-after data analytics course to get a deeper understanding.

In this article, we will go through some of the latest data analytics opportunities which have come up in the industry.

The Intelligent Data Mesh

The intelligent data mesh has been termed the next revolution in healthcare and medical diagnostics systems in the coming years by the Gartner Top 10 Strategic Technology Trends in 2018.

The “Intelligent Data Mesh” has been described by Gartner as a meeting point of the physical and digital worlds where humans, machines, data, and services have been entwined together into a mesh.

The purpose is to gather the benefits offered by all these individual entities into a single unit to find solutions to complex issues thought to be insolvable until now.

One major industry expected to benefit most from this system is the healthcare industry where Intelligent Data Mesh is being hailed as a game-changer in enhancing patient care.

Blockchain

Blockchain continues to be an exciting technology even in 2018 and is expected to remain so for at least another decade. New advancements are being made almost daily regarding this technology as blockchain finds wider uses in various industries with time.

It will not be wrong to describe blockchain as one of the greatest data analytics opportunities. The concept of blockchain started with the idea of a decentralized digital currency which came to be known as Bitcoin in the market.

However, even though the controversy regarding the currency rose the concept of a decentralized and open-source peer-to-peer modeled technology for storing and analysing data. The concept of blockchain is now applied in a wide range of industries with its use predicted to keep rising soon.

Artificial Intelligence

Artificial intelligence is one such data analytics opportunity which is finding widespread adoption in all businesses and decision-making applications. As per Gartner 2018, as much as 41 percent of organizations have already adopted AI into some aspect of their functioning while the remaining 59 percent are striving hard to do the same.

There is considerable research going on at present to incorporate artificial intelligence into the field of data science too. With data becoming larger and more complex with each passing minute, management of such data is getting out of manual capacities very soon. Scholars have now turned to AI for storing, handling, manipulating and managing larger chunks of data in a safe environment.

Augmented Reality

Augmented Reality is an interesting new technology coming up in recent years. As a source which facilitates the interaction between machine and humans in a unique manner, AR has the potential to be a game-changer in the field of data sciences making it another top data analytics opportunity in the future.

AR can provide for the development of simpler UIs for the man-machine interaction when merged with AI allowing the users to store and interact with data in a completely new manner.

Imagine going to an island where all your data is stored in different treasure chests and you are provided with customised keys to access the chests with your data in it. These things may be possible in the future because of the use of AR in data analytics.

Imarticus Learning offers select best data analytics courses that not only boosts your skillset but also your career as a whole.

Frequently Asked Questions

What is a data analytics course?

Data analytics involves examining raw data to extract valuable and actionable insights. These insights, once gleaned, serve as the basis for informed and strategic business decisions, contributing to the enhancement of overall business intelligence.

Do data analysts require coding?

Certainly, coding is a fundamental requirement when undertaking an online Data Analytics Degree. While highly advanced programming skills may be optional, mastering the basics of R and Python is crucial. Additionally, a solid command of querying languages such as SQL is indispensable for a comprehensive understanding of data analytics.

Is Python a mandate for data analysts?

Possessing a thorough grasp of Python programming proves highly advantageous for data analysts. Employers commonly anticipate proficiency in utilizing Python libraries to streamline various data-related tasks. Consequently, acquiring skills in Python emerges as a prudent and strategic career decision for aspiring data analysts.

Hands-on Linear Regression Using Sklearn!

Posted on September 28, 2021March 21, 2024 by Imarticus Learning

Linear regression in Machine Learning is a kind of analysis where there are both dependent and independent variables. Their relationship is depicted here by a linear line from the provided data. This technique is mainly used for predicting the results.

The data and the values from the linear regression are used to find the Mean Squared Error (MSE) to change the values of the variables so that they have the least value possible and get the most accurate results.

While there are several tools that are usually used for linear regression Sklearn is one of the popular tools.

What is Sklearn?

Sklearn is the short form for Scikit Learn which in Python Language, an open-source ML library to help unify the various tools of algorithms in machine learning.

Sklearn precisely works as a one-stop solution that helps with importing, preprocessing, plotting, and predicting data.

Its versatility, powerful nature, ample documentation, and active nature make it one of the popular toolkits in ML.

Linear Regression in machine learning

Linear regression comes at the last stage of the ML processing. Though there are several tools for the same, Sklearn is used more because of its versatility. When there is a log of consistent data it is easier to predict future patterns. The various steps towards the implementation of linear regression are,

Loading the data or object from its source
Training the model results in dividing the data into dependent and independent variables.
Later it is split the data into train and test sets.
It is the test set that will be used for the predictions.
Explore the results to find the suitability of the data for linear regression. A low accuracy data shows the non-compatibility of data to this.
Narrow down the data to a smaller portion and repeat the same steps above.
Now the data should be able to show the MSE which can be used for better predictions.

Importance of Linear regression

Linear regression is important as it determines the accuracy of the prediction from the data given and also finds out the predictors that are significant for better results.

It is useful in determining the power between the dependent and independent variables such as sales and expenditure, or in the case of health care dose and its effects on the body, etc.

Analyzing the regression model explains how much the variables do change with respect to each other. Forecasting weather or its impact and changes is a fine example of this application. In the case of sales and marketing, it explains how much increase is possible in income with a specific amount of expenditure.

Finally, it is one of the main tools for predicting future trends and possibilities. It can be used for a future prediction of any time span. Linear regression using Sklearn provides a near-accurate prediction of the values.

In addition, there are different types of linear regression – simple or multiple linear regression. Each type is suitable for a different purpose or type of data available.

Conclusion

Its possibility is too vast that it requires proper Linear Regression Training to use the best data for getting the most accurate output. Once learned it is easier to predict data and values in the fields of sales, marketing, financial assessments, insights into consumer behavior, risks in finance or loans, etc.

As already mentioned, the accuracy of the prediction highly depends on how compatible the data is for the linear regression model. The Sklearn method is a far better option to determine the same and can provide the closest values to help with a better prediction model for the business.

What Makes an Imarticus – UCLA Certified Analytics & AI Professionals To Be Forever in Demand

Posted on August 28, 2021March 21, 2024 by Imarticus Learning

Modern-day technologies like AI (Artificial Intelligence), ML (Machine Learning), data analytics, etc. are revolutionizing the working culture of businesses & firms. Analytics professionals use these technologies to ease & pace the analytics process.

Analytics professionals are required to extract meaningful insights from huge chunks of unorganized data to make better business decisions. Professionals with a certification from a reliable source surely get an edge over others when it comes to upward mobility.

Let us see how can you get the Imarticus – UCLA certification in Analytics & AI along with its benefits.

Course Overview

Imarticus provides a post-graduate program in Analytics & Artificial Intelligence in collaboration with UCLA Extension which is a major certificate issuing institution in the United States.

You will get dual certification from two reputed sources if you opt for this course. Many individuals have built their Analytics and Artificial Intelligence careers successfully with the help of this course.

You will be receiving live training from world-class celebrities from UCLA & Imarticus. This course has around 400+ hiring partners with a 3-30 lakh salary range. You should have completed your graduation with at least 60% marks to enroll in this course.

Let us see how Imarticus – Analytics & AI professionals are forever in demand.

Broad Course Contents

This 28 weeks long online course covers all the aspects of analytics & AI along with data science fundamentals which helps in building a successful career. You will get to know about various tools/languages via this course like Python, Scikit-learn, Keras, TensorFlow, NLTK, OpenCV, etc. The major topics which will be covered in this course are as follows:

Data Science Fundamentals – You will be introduced to statistics in the first four weeks. You will also get to learn Python basics during this tenure. After that, you will perform data analysis with Python. Various concepts of data processing & statistics like central tendency, standard deviation, Z-score, etc. will be taught to you.
Machine Learning – ML will be taught to you for 6 weeks. Various topics of machine learning like multiple regression, correlation analysis, dummy variables, etc. will be taught to you. You will also be able to run various ML models in Excel in the practical classes. You will also learn about data science business models developed with the help of machine learning.
Deep Learning – Deep learning is a cutting-edge technology used for forecasting & enhancing decision-making ability. You will be taught about deep learning with an industry-first approach for 6 weeks.
NLP – Natural Language Processing (NLP) has helped in enhancing the ways humans react with computers. It has also automated the analytics process in firms/organizations. You will be studying this topic for about 4 weeks.
Computer Vision – You will be taught about computer vision, an AI-based technology that helps computers in extracting information for digital images and videos. You will focus on this topic for around two weeks including practical classes.

Pros of the Course

Besides the gravity of certifying institutions, the teaching methodology of this course ensures that the students will have a long run in the industry.

The pros of opting for this Imarticu’s Analytics and Artificial Intelligence course are as follows:

This instructor-led training is self-paced so that you can take your time to understand concepts.
You will work on various in-class industry-oriented projects to know about the practices in the industry.
You will also come across practice projects, boot camps, capstone projects, workshops, etc. if you opt for this course.
You will also get to test yourself in a hackathon at the end of the course.
The last two weeks of the course will be dedicated to placement preparation where you will get to learn from industry experts. Imarticus also provide excellent placement support to its students.
You will be provided a dedicated program mentor to guide you through the course and also for career advice. You can also monitor your test results & course progress.

Conclusion

This course follows an industry-first approach to make you ready for the industry. You can build a successful career with the broad topic coverage & placement support of this course.

Enroll in the Imarticus – UCLA Analytics & AI course now!

Python Developer Salary in Terms of Job Roles

Posted on July 13, 2021April 2, 2024 by Imarticus Learning

What is Python?

The second most liked Programming language in the world, Python is one of the widely used term in the web-development world.

Who are Python Developers?

The web-developers who design and code the software applications with the help of Python language are referred to as Python Developers.

Roles and responsibilities of Python Developers

Python Developers as Data Scientists

Major businesses in today’s world require tools and skilled people for the data-related tasks such as data collection, data cleaning and processing.

Data Scientists are the programmers who do these tasks for the organizations. Data Scientists gather a large quantity of data and convert it into a useful form, followed by recognizing data-analytics solutions for organizational growth.

Data Scientists encourage the data-driven approach in organizations to deal with complex business problems.

Artificial Intelligence

In AI Python Developers create and implement the required Machine Language algorithms. They analyze the success and failure of the algorithm and rank them according to their performance for future use. Training and Retraining ML algorithms is one of the key tasks performed by Python Developers.

Salaries of Python Developers in India

The changing focus of the organizations on data-driven solutions is resulting in a manifold increase in the salaries of the Python Developers. In the coming years the demand for AI-skilled people will increase, and hence the salaries.

Major IT giants like Google, YouTube, Amazon etc. are adopting Python-driven systems and hence, manifold increases in the salaries of the Python Developers.

The entry level salary of a Data Scientist is approximately INR 500,000 per annum (Source: payscale.com) Data scientists with 5-9 years of work experience gets +100% more salary in India. With the experience and refinement of the skill, the salary of Data scientists shows exponential growth.

An entry-level engineer who develops the ML Algorithms earns the average salary of approximately INR 700,000 annually. With further experience and refinement of the skills, the average salary of the Data Scientist shows exponential growth.

Factors affecting the Salaries of Python Developers

Job location: Considering the increasing demand of Python Developers, not only in India, the faster salary growth is visible in UK and in US.

Location	Approximate Average Salary in INR
Gurgaon	700,717
Bangalore	669,787
Delhi	600,000
Mumbai	579,728
Chennai	540,131
Hyderabad	475,000

UK Python Developer annual salary	£67,000
US Python Developer annual salary	$117,000

Experience:

Python Developer	Approximate Salaries in INR
Entry Level Python Developer	427,293
Med-Level Python Developer	909,818
Experienced Python Developer	1,150,000

Refined Skills:

Mere understanding of the Python is of no use, till is integrated with the problems and solutions. How one uses the well-known Python tools define the person’s skill set, which is a determiner of the salary.

Job Role:

Python Developer	Approximate Average Salary in INR
Data Scientists	700,000
ML Engineer	670,000
DevOps Engineer	660,000
Software Engineer	500,000
Web Developer	300,000

Should know more interesting things about Python programming training and Python career.

Why Artificial Intelligence is Invaluable for Weather Forecasting and Disaster Prediction

Posted on July 7, 2021September 6, 2021 by Imarticus Learning

For most people, weather forecasts are simply indicators of whether they need to carry an umbrella or throw on a coat when they go outside. However, for many industries and types of individuals, weather changes and patterns have a direct impact on their lives and livelihoods.

Agriculture, for example, benefits from accurate weather forecasting because farmers can make better planting and harvesting decisions. For governments, weather forecasts factor in their budget plans and disaster relief fund allotments. Businesses that rely on clear weather (or rough weather) depend on weather forecasts to drive several of their operational processes.

From all this, it is easy to gather that accurate weather and disaster forecasting carries much more weight than we think. Artificial intelligence augments the accuracy and reliability of weather forecasting, especially given that so many details fluctuate every day and with every geographical location. It is a great fit, given the volume of data is nigh impossible to sift through with manual labor alone.

In short, the future of artificial intelligence will also see its increasing use in the weather and natural disaster forecasting domains. Here are a few more reasons why:

Managing several sources of weather data

There are currently more than one thousand weather satellites orbiting the Earth, each sending back weather data dumps to various collection points. These data dumps are a mix of information about temperatures, cloud patterns, winds, and pollution levels. Then there are thousands of government and private weather stations around the world, each conducting their own real-time research on weather and climate. It is nearly impossible to sift through all this data manually, but AI algorithms can do it in a matter of hours.

Sifting through multiple data categories

Suffice to say that the amount of data generated from satellites and personal weather stations is too much to fathom, and impossible for humans to sift through. However, Artificial Intelligence training can be applied to segregate and classify data from dumps, as well as to pull out key insights for analysis. This is a preliminary process in the weather prediction model, wherein AI segregates data based on indicators, flags significant shifts or patterns, and keeps data classified such that predictions are made as accurately and as scientifically as possible.

Preparing for potential disasters

Beyond real-time predictions, AI is also used to identify patterns and prepare for natural disasters in advance, off the back of previous circumstances. It may also split this data between geographies, allowing disaster management teams to evaluate which areas will be hit the hardest and prepare for that. This data is also invaluable for civil engineering teams, architectural firms, and city planning teams who need to take weather into account when mapping out residential and commercial areas.

Sending out warnings

Apart from predicting natural disasters, AI can also be leveraged to send out warnings to potential danger zones. This is invaluable when it comes to saving human and animal lives and generally preparing areas for the worse. Warnings can be sent out through media alerts, push notifications, and citizen broadcasts; whatever the method of delivery, AI is vital to sending such notices out in time and to the right people to curb panic and facilitate seamless planning.

Artificial Intelligence Training for Weather Forecasting

Weather forecasting teams and companies need skilled AI scientists and engineers to apply theory to practice in real-time. They need AI professionals who can create automated setups to free human minds for higher-order thinking; they also need pros who are fast on their feet and adept at creative problem-solving.

Using AI for weather forecasting is a whole new ball game – one on which many lives depend.

Things To Know About the Reinforcement Learning with MATLAB!

Posted on June 8, 2021June 11, 2021 by Imarticus Learning

With the advent of technology, human has started to function by largely depending on machines and technology. To make things far easier, automated technology has been introduced in various aspects of life. Reinforcement learning is also a segment of machine learning that is based on the premise of automation.

What is Reinforcement Learning with MATLAB

Reinforcement learning is a kind of machine learning which enables a computer to function on its own by interacting with the dynamic environment repeatedly. The main aim of this approach is to reduce human intervention in machine learning and automation as much as possible so that a state of one hundred percent automatic technology can be attained.

Under reinforcement learning, the computer is not properly coded or programmed to perform the tasks but is made to act accordingly with the trial-and-error method. The environment or the outside conditions are made dynamic for the computer to explore as much as possible.

Applications like MATLAB enable this kind of function to run smoothly by providing schematic and organized results and outcomes. MATLAB is a professional tool that is fully documented and properly tested for carrying out functions like these.

With the help of MATLAB, reinforcement learning is done to get the best outcome suitable for a particular outside condition. All these functions are undertaken by a piece of software called the agent. The agent interacts with the outside conditions to produce various outcomes.

Understanding the Reinforcement Learning Workflow

To train the agent or a computer, the following steps are deployed:

Creating the environment

The first step is to provide a suitable environment for the agent. The environment can be either a real-life condition or a simulation model. For technical and machine-based reinforcement learning, having a simulation model is preferred for smooth and safe functioning.

Setting up a reward

A specific reward in the form of a numeric number has to be set up so that the agent can function accordingly. A reward is sometimes achieved by the agent after constant trials. Once the reward has been met, the optimal way to achieve the reward can also be found.

Creating the agent

The agent for reinforcement learning can be created either by defining the policy representation or through the configuration of the learning algorithm of the agent.

Training and validating the agent

For this, all the training options for the agent are set and training is started for the agent to tune the policy. In case an ideal validation of the agent has to be done, simulation turns out to be the option.

Deployment of the policy

In the end, the policy representation is deployed using coding languages like MATLAB, etc.

A real-life example of reinforcement learning with MATLAB

Automated driving is the best example of machine learning, outcomes of which can be the result of reinforcement learning. The agent in the car uses various sensors to drive the car automatically without any human intervention. These sensors and video cameras give commands to the steering, gears, clutch, and brakes to take suitable action.

After a rigorous session of trial-and-error of various outcomes, the best way to automatically drive a car can be known. Reinforcement learning uses almost the same sort of applications while parking or reversing the car.

A shortcoming of reinforcement learning

Apart from its various benefits, reinforcement learning takes a lot of time and tries to achieve the optimal outcome.

For making stable analytics and artificial intelligence career, you may refer to the professional learning by Imarticus. Various subjects under the analytics and artificial intelligence course are offered by Imarticus Learning.

Career in Machine Learning – Check Job Profiles, Top Courses and Colleges, Fee Structure

Posted on May 3, 2021March 22, 2024 by Imarticus Learning

Professionals in the sphere that is Machine Learning are very important and sought after in the Information Technology Industries across the globe. Through Machine Learning, human work has reduced significantly and boosted efficiency.

Machine learning has also helped reduce errors and a large number of companies have begun automating their systems. Business systems are using machine learning training to reduce costs and improve productivity along with performance as a whole.

There are a variety of career parts to pursue in the sphere that is Machine Learning and the positions offered are very rewarding.

Career Paths for Machine Learning Aspirants

Software Engineer: Software Engineering aspirants need to know the nitty-gritty of code writing fluently as candidates will be needed to create code that would support the creation of specific algorithms. Using principles from engineering with computer science within mathematics from an engineering degree, designing and developing software is what computer software engineers are responsible for.The candidate for these jobs is required to have skills in listening to and understanding clientele requirements on a more detailed level. Along with this, they are also required to create a system in accordance with clientele parameters.
Software developer: The job of a person in software development entails creating flow charts to assist coders in their work. Software developers are known to be the true brains behind any computer program. They are responsible for creating models, illustrative representations, strategic groundwork, and plotting out the required working of a complete system. They are required to test machinery and look at the working of each component.
HTML Designer: These designers are involved in the creation of software for various social media platforms, big online stores, and banks as well. In banks the designs they put into effect help in increasing the number along with the efficiency of bank transactions that are managed and done online including those done electronically.
Data Scientists: Involved in the procedure of analysis, they are responsible for utilizing data to find out vital information using inspecting and modeling processes.
Computational Linguistics: The job of a computer linguist holds candidates responsible for helping computers in understanding spoken languages and to constantly improve currently existing systems.
National Language Processing Scientist: The people in this position are needed to do the designing and development of machines and also applications that can learn patterns and translate various words imputed by a speaker to various other languages.

The demand for professionals in the area that is machine learning is growing every day. Various other career paths in this area include Data Analyst, Cloud Architect, Intelligence Developer for a Business and also Data Architecture.

Best Courses and Facilities for Training within India

The top 3 machine language courses available in India are listed below. Students,

professionals in analytics and also data scientists pick the finest programs to increase their skills and improve themselves.

PGP in Machine Learning & Artificial Intelligence offered by IIIT-B
Offered by IIT in Hyderabad is Fundamentals of ML
PGP in Artificial Intelligence along with Machine Learning

Colleges Offering Machine Learning Courses

The various colleges offering machine learning courses in India are listed as follows:

Indian Institute of Technology, Hyderabad
DY Patil International University
University of Petroleum and Energy Studies
Jain University, Bangalore
Sharda University
Indraprastha Institute of Information Technology
Vellore Institute of Technology
SRM Institute of Science and Technology
Dehradun Institute of Technology University
SVVV (Shri Vaishnav Vidyapeeth Vishwavidyalaya)

Secure Your Finance & Analytics Careers With Imarticus!

Posted on October 21, 2020April 1, 2024 by Imarticus Learning

With the growing commercial and technological advancements in the global markets today, Finance and Analytics are quickly becoming one of the most lucrative careers in the business world. Finance & Analytics play a major role in data evaluation for businesses to streamline decision-making processes.

They mainly inform the investment decisions of the companies by researching the microeconomic and macroeconomic situations with the organization’s principles. The professionals in these fields also prepare financial models that project future economic conditions.

Many businesses also hire them to evaluate the efficiency of their marketing techniques in comparison to the cost using numerical data. They also work in franchise-based businesses to track the individual franchise or a group and evaluate their positive and negative points to speculate the growth of the business.

Importance of Finance & Analytics

In this competitive world, businesses today need on-time data to formulate decisions for business. Every company needs good financial and analytics planning to utilize its resources in the best way possible.

The changing needs of businesses and the emergence of new models are making it crucial for companies to have sound Finance and Analytics professionals that help them improve their decision-making

Shape the business goals and plans for optimal performance. The Finance courses and Data Science Course with Placement help businesses to run efficiently and attain their aims fast.

Scope of Finance & Analytics

The careers in these fields are highly coved in the business industry with a good pay scale. The professionals in these fields have ample job opportunities in sectors like banks, pension, insurance companies, mutual funds, and securities firms, etc. Since they assist people in their financial decisions and investments, they can also open their own offices and work as self-employed.

They can also do accounts, auditing, taxes, and prepare financial statements. They can also specialize in other segments like Cost accountancy. The work scope of Finance and Analytics professionals also includes tasks like account management, secretarial work, share valuation, etc.

Job Opportunities

The wide scope of Finance & Analytics allows professionals to find employment in a range of sectors. They can find job roles under the following titles:

Financial Analyst
Chartered Account Trainee
Analyst – Back Office
Accounts Trainee
Assistant Consultant – Business Analyst
Junior Financial Analyst
Management Trainee – Finance
Research Analyst – Option Strategist

The pay range in these roles is also good. The average pay of a Financial Analyst in India is around INR 40,000. With experience, this number multiplies manifold.

Educational Requirements

To build a substantial career in these fields, one needs to have in-depth knowledge along with a good practical skill-set.

For this, students can join vocational programs in Finance and Analytics from reputed institutions. Imarticus is one such renowned organization that is offering courses in these subjects to train students and make them job-ready for the business world. By learning these courses students get:

Guaranteed job interviews
Comprehensive, industry-approved curriculums
Hands-on, experiential learning with cutting-edge technology
Training by industry experts and veterans
Extensive career support and assistance
Certifications accredited by world-class organizations
… And so much more!

Learning with Imarticus

Imarticus Learning is organizing the ‘Great Indian Futuristic Talent Drive’ offering good discounts on its various courses for the festive season from 21st October – 27th October 2020. You can avail of the following discounts by registering in the drive:

Fee Waiver on PG Classroom Programs – Up to 25% off

Fee Waiver on Online ProDegrees – Flat 20%

Fee Waiver on Classroom CIBOP & ProDegrees – Flat 15%

With the online learning mode of Imarticus, you get the facility to learn at home.

Thus, keeping safe during the pandemic and productively using your spare time to upskill yourself for an excellent career.

How Do I Start a Data Analytics Study?

Posted on September 4, 2020May 30, 2025 by Imarticus Learning

Before commencing anything new a lot of questions and queries baffle the mind. When starting a data analytics study there are some factors one must keep in mind for a smooth and practical flow of the study. By investing some of the time in the beginning to follow these steps, a good amount of time and efforts can be saved while carrying out the actual study.

Keep the following points in mind before kick-starting a data analytics study.

Understanding the Capacity: Before you begin to explore a particular study in data analytics, it is significant that you know about the whole capacity of data analytics.

There is going to be a great requirement of the theoretical knowledge and deep insight about data and understanding data. Learning about the coding languages and syntax is paramount to make a hold on data analytics.It can prove to be advantageous if you take up a data analytics course online which can make you learn data analytics and its different elements in a precise and detailed manner. You may refer to Imarticus learning which can help you hone your undiscovered skills and make you a genius in data analytics.
Experimenting: Once you gain proper knowledge about the coding languages and their systematic usage, it is really important that before you jump on to the main data analytics study, you apply what you have learned by the way of an experiment. Internet is filled with data published by various renowned companies which can be used for the experimentation. Experimentation is the only means which can help you establish a relation between the cause and the effect.
Specifying the Pre-requisites: Once you are done and satisfied with your experimentation, to begin the actual study in data analytics you need to specify the requirement of a specific date on which the research is going to be based. This data can be in any form like the number of people from the general population or the number of people working from home etc. Understanding the specifications of the data, in the beginning, is paramount.
Collecting the Data: After specifying the requirements of data and recognizing the sources of that data, the collection has to be started. Data can be availed from various sources like the company portals or the organizational databases.The collection of data has to be appropriate and methodical so that it is not hard to decipher when the study begins. Sometimes the data collected is not at all in a usable manner and has to be filtered on various levels before beginning the actual study. In such a scenario, the data undergoes a processing and cleaning process.
Processing the Data: For better understanding, scattered data has to be represented methodically for the study to be smooth. In this step, various tools are used to make the data workable. With the help of bar-graphs, tables with rows and columns, data are presented systematically. Generally, the use of spreadsheets is done for a structured display of data.
Cleaning the Data: At this point, still, there would be a lot of information which is going to be of no use while carrying the study. There are chances that there is a duplication of the data. Most of the times there are certain errors in the data which may cause a lot of problems while studying and analyzing it. Such errors are got rid of in this cleaning process.

After following these steps, the study of data analytics can be taken forward in a hassle-free and smooth manner. To learn data analytics and how to communicate the data after analysing it, refer to Imarticus leaning which is an ideal way to learn data analytics through professionals.

How is Big Data Analytics Used For Stock Market Trading?

Posted on May 18, 2019May 6, 2022 by Imarticus Learning

How is big data analytics used for stock market trading?

Big Data Analytics is the winning ticket to compete against the giants in the stock market. Data Analytics as a career is highly rewarding monetarily with most industries in the market adopting big data to redefine their strategies. Online stock market trading is certainly one area in the finance domain that uses analytical strategies for competitive advantage.

Capital market data analysts are important members of a corporate finance team. They rely on a combination of technical skills, analytical skills and transferable skills to compile and communicate data and collaborate with their organizations to implement strategies that build profitability. If you’re interested in a career in financial analysis, there are several subfields to explore, including capital market analysis.

Organizations and corporates are using analytics and data to get insights into the market trends to make decisions that will have a better impact on their business. The organization involved in healthcare, financial services, technology, and marketing are now increasingly using big data for a lot of their key projects.

The financial services industry has adopted big data analytics in a wide manner and it has helped online traders to make great investment decisions that would generate consistent returns. With rapid changes in the stock market, investors have access to a lot of data.

Big data also lets investors use the data with complex mathematical formulas along with algorithmic trading. In the past, decisions were made on the basis of information on market trends and calculated risks. Computers are now used to feed in a large amount of data which plays a significant role in making online trading decisions.

The online trading landscape is making changes and seeing the use of increased use of algorithms and machine learning to compute big data to make decisions and speculation about the stock market.

Big Data influences online trading in 3 primary ways:

Levels the playing field to stabilize online trade

Algorithmic trading is the current trend in the financial world and machine learning helps computers to analyze at a rapid speed. The real-time picture that big data analytics provides gives the potential to improve investment opportunities for individuals and trading firms.

Estimation of outcomes and returns

Access to big data helps to mitigate probable risks in online trading and make precise predictions. Financial analytics helps to tie up principles that affect trends, pricing and price behaviour.

Improves machine learning and delivers accurate predictions

Big data can be used in combination with machine learning and this helps in making a decision based on logic than estimates and guesses. The data can be reviewed and applications can be developed to update information regularly for making accurate predictions.

In a nutshell, large financial firms to small-time investors can leverage big data to make positive changes to their investment decisions. Information is bought to the fingertips in an accessible format to execute trading decisions.

If you are a trader, you will benefit from a Big Data Analytics course to help you increase your chances of making decisions. It is highly beneficial for those involved in quant trading as it can be used extensively to identify patterns, and trends and predict the outcome of events. Volume, Velocity, and Variety are the pillars of Big Data that aid financial organizations and traders in deriving information for trading decisions.

Tag: analytics and artificial intelligence course