Tag: best data analytics certification course

What is Data Analytics?

“Data Analytics” is a crucial instrument for understanding and dealing with dynamic business trends. The role of data analytics is to act as a helping hand in giving clients or businesses personalised data-driven advice. A career in Data Analytics is a great lookout in today’s technologically advancing world because nothing can be accomplished without the use of data.

The evolution of Data Analytics has witnessed adaptation in various forms, gradually through the phases of technological advancement. It has offered multiple advantages to organisations and individuals involved in data analytics. Data Analytics is an ever-expanding discipline.

Become a Data Analyst

The field of data is one of the top-notch areas that is growing in the corporate world. It would be very profitable for individuals to have a career in data science, as technology is scaling every day. A data science course with placement can be a huge game changer when a young professional is trying to understand what is data analytics and looking to start a career in a related discipline.

History and evolution of Data Analytics

Read on to learn about the evolution of Data Analytics and how the four eras of analytics have grown over the years. 

Relational and non-relational databases 

Edgar F. Codd created relational databases in the 1970s, and they gained significant popularity in the 1980s. SQL queries were very famous at that point in time, and users used to derive data from several databases using RDBMS. Relational databases and SQL are still widely used and accepted, but technologies like MySQL are not that prevalent. 

Relational databases like MySQL were intended to convert unstructured data and are typically highly strict. Soon, the internet had a surge in popularity, and the relational database couldn’t keep up. That is when NoSQL platforms like MongoDB were created to combat the shortcomings of RDBMS. 

Business intelligence

Business Intelligence refers to the process of improving business trends and choices by seeking, aggregating, and evaluating the accumulated data preserved by an organisation. Power BI cannot be underestimated, as it was one of the most innovative and futuristic concepts in the 1990s. 

Power BI is the ability to make decisions that are related to data technologies. Business intelligence was initially in the form of systematic consumer data but later took part in the decision-making process of large businesses.

Data visualisation

One can have a great career in Data Analytics if one understands the deep-rooted concepts of data visualisation. Visualisation means the representation of data in graphical form using representative tools like charts, graphs, diagrams, etc. With the evolution of data analytics in the big data era, the tools and technologies of data visualisation have been crucial for analysing a vast volume of data and making data-driven decisions.

The transition from Florence Nightingale’s diagram of mortality in the Army in the East to Tableau has been historical and remarkable. Florence Nightingale was a pioneer in the field of data visualisation who changed the ways of representing data for good. Such formats of statistical charts and graphs are now used in modern software like Tableau.

Big data

Big data emerged in early 2000 and Roger Magoulus first coined this term. The technology of big data emerged when handling immense amounts of data was not possible with only Power BI tools. Hadoop emerged as a big platform for processing large amounts of data.

The evolution of data analytics and big data in the four eras of analytics has become a very important part of the world of technology. It allowed the pooling of a large amount of data. The open-source platform gave its users the additional advantage of versatility. Many other open-source platforms developed after the success of big data, such as Apache Spark, Apache Cassandra, and so on.

Data Analytics in Cloud

Cloud computing tools and services have become more important as businesses have become more complex over time, allowing firms to stay up-to-date. The evolution of data analytics from traditional hardware to Cloud-based infrastructures is a good example. 

In 1997, a famous professor, Ramnath Chellappa, defined Cloud Computing as a new type of computing paradigm where economic considerations dictate the ways of computing, only focusing on technical constraints. 

Using Cloud and supporting programming technologies like Python, C++, Java, etc. has been most convenient for software professionals. The role of data analytics tools like Cloud is to offer more accessible storage and user facilities than ever before.

Applications of Data Analytics

Understanding the ways in which Data Analytics is used in the daily course of events is important. The fact is that Data Analytics is used in numerous business sectors. Some of the major applications of Data Analytics in multiple business sectors are enumerated as follows:

Manufacturing: Today, the manufacturing sector uses Data Analytics in its everyday operations. Data Analytics helps with supply chain management and solves complex supply chain problems. It also helps in resolving problems of labour shortages and looking out for any equipment breakdown.

Retail: It is mostly focused on tracking and understanding the buying patterns of customers. Data Analytics helps in analysing customer habits and tracking their patterns so that businesses can boost their sales by launching new products as per the taste and preferences of the customers.

Healthcare: by analysing the types of diseases, pharmaceutical drugs are created. The evolution of Data Analytics has helped to create novel medications and life-saving drugs for the treatment of patients. It results in better diagnosis and treatment of the patients.

Logistics: The main application of data analytics in logistics is to develop new business models. The industry is dynamic and requires changing models and techniques for effective results.

Education: The makers of the curriculum use data analytics to see the past results of the curriculum and the progress in the recent one. It allows policymakers to improve the learning process and inculcate essential skills in students that were missing earlier.

Banking: Banking is a complex process and requires the constant keeping of records. With the help of Data Analytics and its storage facility, it can be easily identified who the defaulters are in the banking system. It can help to better identify frauds and defaulters who have not paid the money they owe to the bank yet.

Conclusion

The evolution of Data Analytics has led to the rise of active fields in research and development today. Organisations are making a lot of investments in this area. If you want to learn data analytics and want to have a successful career, you can consider signing up for the Postgraduate Program in Data Science and Analytics at Imarticus. 

A Data Analytics course and certification can always act as additional support other than your academic degree. It can showcase you as a more skilled person when compared to your contemporaries. 

Companies must be well-versed in data analytics, including its various forms and applications, in order to fully benefit from these technologies. Some of the popular Data Analytics tools like, Python, Tableau, Qlik Sense, etc. have been vastly used by organisations in recent times.

Image recognition is a result of the incredible fusion between artificial intelligence and computer vision that has led to the emergence of this technology. Image recognition software or applications take the help of camera technology and various AI models.

Image recognition technologies can distinguish and identify objects, people, texts and so on by extracting information from the images it captures. The emergence of this technology has revolutionised the industrial platform, be it pharma companies or retail shops and made brilliant opportunities for building a career in data analytics.

How does computer vision help in image recognition?

The role of computer vision is very pivotal in image recognition. It incorporates various processes like Optical Character Recognition (OCR) with the help of which it extracts textual data from images it captures. Two technologies are employed in this case: convolutional neural networks (CNN) and deep learning which generally use Python programming.

With the aid of machine learning, computers can distinguish between images without the necessity of detailed programming. This is done by breaking down images into minute pixels and then making predictions based on that. By undergoing repetitive iterations, the predictions are made more accurate which is almost parallel to human perception.

Using CNN for image recognition

The Convolutional Neural Network inculcates a strong algorithm which it uses for image processing. It uses three different layers for analysing the images namely, Convolutional Layer, Pooling Layer and Fully-Connected Layer. 

In the Convolutional Layer, a small portion of input neurons is connected to hidden neurons. The dimensionality of the feature map is reduced by the Pooling Layer which follows the Convolutional Layer. In the end, the Fully-Connected Layer assesses the input data from the previous two layers and helps make assessments through predictions based on memory.

How does image recognition work?

There are a series of steps that are followed to convert images into textual data. These steps include:

• Acquisition of image: this is the first step where the image is retrieved from an external source
• Enhancement of image: this step involves changing the picture quality for better assessment
• Restoration of image: this utilises certain mathematical tools for improving the quality of the image
• Multiresolution processing: here the image is divided into smaller wavelets for the compression of data
• Morphology-based processing and segmentation: analysis of images is done based on their shapes and then subdivided into smaller individual components
• Description and representation: each component is analysed and quantitative information is derived

At the end of these steps, image recognition is made possible where the objects are tagged with a label centred on their characteristic features.

This entire process includes pre-determined signal processing methods which are employed to derive the information from the captured images. These methods include object visualisation, recognition, pattern measurement and so on.

Challenges faced in image recognition

The evolution in image recognition has brought with it several technological advancements. However, the advancements are augmented with various challenges and limitations which need to be overcome. The challenges are as follows:

• Model generalisation improvement: the challenge here is to ensure that the system can run well in real-world scenarios that can differ from training and test sets. One finds varying distributions in real-world scenarios like different viewing angles, size of the objects and camera features.
• Failure to read small and huge data sets: here the challenge is to enable the system to learn new data by introducing it to small and limited datasets in the beginning and utilising deep learning and machine learning to learn new information and ultimately recognise new objects. Similarly, another challenge here is that the current models lack the efficiency to read huge datasets to perform critical tasks.
• Limitations to cognitive understanding: the challenge here is the inefficiency to go beyond just object recognition and achieving a cognitive understanding of objects to interpret inter-relationships between objects like humans to humans, humans to cars and so on.
• Limitations to automate engineering of networks: the challenge here is that, instead of focusing on some specific features, the efforts are now to build novel network architectures. However, this is quite a difficult task involving myriads of parameters and choices.

Applications of image recognition

Some of the major arenas where image recognition is used are as follows:

• Face Recognition: This is used in surveillance and security works. 
• Remote Sensing: various sensors are used to extract information about a distant object. This is used in ships, aircraft and satellites to name a few.
• Medical sectors: image recognition is being used in image diagnosis of a disease in medical sectors. It is also used in augmenting Computational Tomography (CT) scans and Magnetic Resonance Imaging (MRI).
• Processing of Video: it is used to process visual data in television sets and other visual electronic systems.

Conclusion

A massive revolution in the industrial sector has been brought about by the advancements in technologies supporting image recognition and computer vision. Utilising deep learning and machine learning integrated neural connecting systems have been developed which aim at getting much better in the coming days. 

However, it has yet to overcome a number of challenges to attain its maximum potential. To gain expertise in such technological backgrounds you can check out Postgraduate Program In Data Science And Analytics provided by Imarticus. This 6-month long program will help data science aspirants with a better chance of securing a career in data analytics with a machine learning certification. 

An essential component of contemporary corporate operations is data analytics. It entails utilizing various methods and technologies to analyze data and provide insights that may guide decision-making. 

Given the daily increase in data production, organizations must have the necessary training and understanding to utilize data analytics fully. Determining which data analytics approach fits your company’s objectives might be difficult because many different sorts are accessible. 

In this post, we will explore the four main types of data analytics. We will also look at techniques for analyzing different types of data in real-life scenarios.

What is Data Analytics?

 

The phrase “data analytics” is broad and covers various data analysis techniques. Data analytics techniques may be used to any information to get insight that can be utilized to make things better. 

With an average yearly income of $65,745 in the United States, the anticipated total compensation for a data analyst is $70,268.

Techniques for data analytics can make patterns and indicators visible that might otherwise be lost in the sea of data. The efficiency of a firm or system may then be improved by using this knowledge to optimize procedures.

What are the steps involved in Data Analysis?

Let’s look at the steps that make up this fascinating procedure!

Step 1: Define the issue 

Any data analysis effort should start by defining the issue that needs to be resolved. What is it that you desire to learn? What inquiries do you wish to address? You’ll be able to maintain focus throughout the analytical process if you define the problem up front.

Step 2: Collect the data 

This step entails accessing databases, conducting surveys, or scraping website data. Regardless of your approach, ensure you’re gathering the appropriate data for your problem.

Step 3. Clean up the Data

It’s time to tidy up the data once you’ve acquired it. This includes eliminating duplicates, correcting mistakes, and handling missing data. Although cleaning the data might take some time, it is essential to ensure the accuracy of your study.

Step 4: Analyze the data 

The enjoyable part is finally here! You may now examine the data and find new insights. You can use visualizations, statistical analysis, or machine learning techniques to make sense of the data.

Step 5: Analyze the Results

It’s time to evaluate the findings after data analysis. What do the graphs and numbers represent? What new information have you discovered? You can start drawing inferences at this point.

Step 6: Communicate the results

It’s now time to present your results to others. This might entail writing reports, speeches, or publishing academic articles. Whatever approach you decide to use, communicate your findings straightforwardly and understandably.

4 Types Of Data Analytics

• Descriptive Analytics

The simplest and most popular sort of data analytics is descriptive analytics. Using graphs, tables, charts, and dashboards to summarize historical data responds to the query “What happened?” We can analyze what has previously occurred and spot trends and patterns in the data by using descriptive analytics.

For instance, a company may utilize descriptive analytics to monitor its sales performance, evaluate it against its rivals, and pinpoint its top-selling goods and services. Teachers may use descriptive analytics to track their pupils’ attendance and grades and pinpoint areas for improvement.

• Diagnostic Analytics

Diagnostic analytics goes further than descriptive analytics by addressing the “why did it happen?” issue. Identifying the underlying reasons and variables that impacted the results entails delving deeper into the data. Techniques including data mining, drill-down, slice-and-dice, and correlation analysis are frequently used in diagnostic analytics.

A company, for instance, can utilize diagnostic analytics to look into why sales declined in a certain month by looking at elements like consumer behavior, marketing initiatives, product quality, and price. A teacher can utilize diagnostic analytics to investigate the reasons behind some students’ low test performance by looking at their study habits, learning preferences, and feedback. 

• Predictive Analytics

The next level of data analytics, predictive analytics, responds to the question, “What will happen?” It forecasts upcoming events or results using past and present data. Techniques including machine learning, artificial intelligence, statistical modeling, and forecasting are frequently used in predictive analytics.

A company may use examples of predictive analytics in business to project its sales income for the upcoming quarter based on variables like market trends, client demand, and seasonality. Based on their prior marks and development, a teacher may utilize predictive analytics to forecast how well their pupils will perform on a test in the future. 

• Prescriptive Analytics 

The most sophisticated and intricate data analytics, prescriptive analytics, responds to the question, “What should we do?” Using data, it makes recommendations and offers ideas for the best courses of action or choices. Techniques including optimization, simulation, and decision analysis are frequently used in prescriptive analytics.

Prescriptive analytics, for instance, may be used by a company to improve its inventory levels, pricing schemes, and marketing efforts following its objectives and limitations. Teachers can utilize prescriptive analytics to provide tailored learning strategies and interventions based on each student’s requirements and preferences. 

The Final Words

Businesses and organizations may use data analytics to their advantage to learn more about their operations, clients, and sector. Businesses may make data-driven decisions that improve outcomes and success by investigating the many forms of data analytics, including descriptive, diagnostic, predictive, and prescriptive analytics.

There is a rising need for qualified data analysts who can efficiently comprehend and analyze data in today’s data-driven environment. Consider enrolling in a course at Imarticus Learning if you wish to pursue a career in data analytics. You may acquire the knowledge and expertise required to be successful in this sector through hands-on training and real-world projects.

Introduction

Data science is an excellent way to build a successful career. A data scientist combines scientific principles and analytical techniques to build predictive models. These models are then used to plan strategies and decision-making that help boost any business.

The market size for data science in 2021 was USD 95.39 billion. This figure is expected to grow in 2026 to USD 322.9 billion. So if you are looking to make a career in analytics and machine learning, Imarticus offers an excellent PG in data analytics programme. Want to know how to make the most of your data analyst training? Read on!

Why take up PG in data analytics?

Data analyst training makes candidates future-ready. Here are some benefits of getting a PG in data analytics:

A higher income. Since the skill is in demand, you can earn more than your counterparts.

Be tech-savvy. This course gives you the opportunity to learn different computer languages and methodologies. The course prepares you to help organisations collect, manage, and analyse data.

Data science is an ever-evolving field. A data science and analytics course will add to your career ascent. It will help you harness the ability to think strategically and take the organisation to a new high.

Why take up PG in data analytics with Imarticus Learning

Imarticus’ PG in data analytics is the only course you need to build your data science and analytics career. The six-month course offers more than 300 hours of learning. You can learn about more than 10 analytical tools.

More than 1,500 students have taken this course. And the highest salary package offered to them was INR 22.5 lakh per annum. The average salary hike offered to our students is 52%. In addition, they get an opportunity to interact with more than 400 hiring partners. What’s more? The course comes with a job interview guarantee programme! Here are some other benefits to consider:

The curriculum is job-specific to help students stay updated with the practical applications of data science. The data science and analytics course offers training in subjects such as tableau, data analytics, SQL, Python, and Power BI.

Our faculty uses a live learning module to prepare course takers to operate in various data science roles. The interactive method allows us to provide students with hands-on training.

PG in data analytics with Imarticus is the only data analytics course you need as it also provides a dedicated career service to learners. We focus on career mentorship along with one-on-one counselling. We also offer interview preparation workshops, profile enhancement, and resume development.

We also offer a chance to boost resumes with coding exercises such as KPMG India COE Organised Hackathons. These activities are all a part of the course. It gives candidates a chance to compete in national-level competitions.

Make yourself career-ready with this analytics and machine learning course

The journey of a data analyst training enthusiast is divided into two tracks: the core track and the specialisation track. On the core track, we focus on building a foundation through programming training and boot camps on data visualisation and machine learning (ML). At the end of core training, we conduct a pre-placement assessment. Here we assess the candidate for the specialisation in a vertical.

The next step is the boot camp. Here we focus on two specialisations. These are the advanced ML track and data analytics and ML track. We then provide students with learning tools such as Capstone, project completion, and hackathons. Our resources are dedicated to placement preparation and creating placement drives.

On successful completion of training, learners receive certificates from Imarticus Learning. These certificates are backed by the National Skill Development Corporation and Skill India. We prepare you to choose a career as a data scientist, business analyst, data analyst, analytics manager, business intelligence specialist, or machine learning consultant.

Final Thoughts

We at Imarticus offer a future-ready course called PG in data analytics. With the blend of practical knowledge and theory, this data science and analytics course is designed to empower you to achieve your desired career dream.

We offer students an unparalleled learning experience. How do we know? Well, we were awarded the ET Best Education Brand in 2022, the Indian Education Awards in 2022, and the certification of recognition for ‘Best E-learning Company of the Year’ by Entrepreneur India. These accolades are a reflection of our excellence and commitment as an online education provider.

Data analytics is growing, and the demand for professionals with expertise is at an all-time high. A career in data analytics is lucrative and provides opportunities to make a real impact on organizations by helping them gain insights from their data. 

One of the key tools used in this field is Microsoft Power BI, which has become the go-to business analytics and intelligence tool. 

In this blog, we’ll explore why Power BI is the ultimate tool for this growing field and how it can help you kickstart or enhance your career in data analytics. 

What Is Power BI?

Microsoft’s Power BI is a service for business analytics that offers interactive visualizations and business intelligence capabilities with a simple interface for users to build their dashboards and reports.

Excel spreadsheets, SQL databases, CSV files, and other data sources can all be integrated with Power BI. 

It also offers APIs for integrating with Python and other programmes and platforms. Thanks to Python integration, users can perform data analysis, transformations, and visualisations using Python scripts within Power BI.

Advantages of Utilising Power BI for Your Business Analytics Needs

Here are some reasons why you might consider using Power BI:

• Data visualisation: Power BI offers a variety of data visualisation options, such as bar charts, pie charts, line graphs, and more, making it simple to present data.
• Integration with other data sources: Data from various sources are combinable into a single repository thanks to Power BI’s integration with Excel, CSV files, and databases.
• User-friendly interface: Power BI is accessible to many users thanks to its user-friendly interface, which enables even non-technical users to create and customise their reports and dashboards.
• Real-time updates: Real-time data updates from Power BI provide information essential for making wise business decisions.

Common Applications of Power BI in Business and Industry

Power BI is a business intelligence and data visualisation tool developed by Microsoft. Organisations use it to analyse, visualise and share data insights with stakeholders. 

Some everyday use cases for Power BI are:

Data Visualisation: It offers a variety of interactive visualisations, including charts, graphs, maps, and dashboards, to assist businesses in presenting data.

Data Insights: It offers sophisticated analytics features to assist businesses in deriving value from their data, including data exploration, modelling, and data insights generation.

Report Creation: Power BI is a well-liked tool for report creation and data storytelling because it enables users to create reports based on data from various sources, such as Excel spreadsheets, SQL databases, and cloud-based data sources.

Collaboration: Organisations can collaborate on data analysis projects using Power BI’s cloud-based platform for sharing data insights and reports with stakeholders.

User Base of Power BI in Different Industries

Power BI is used by many organisations, from small businesses to large enterprises. Some of the industries that use Power BI include:

• Healthcare: Power BI is used by healthcare organisations to analyse patient data, track clinical outcomes, and check the performance of healthcare facilities.
• Retail: Retailers use Power BI to track sales and customer behaviour, track supply chain performance, and analyse market trends.
• Manufacturing: Manufacturers use Power BI to track supply chain effectiveness, track production performance, and study market trends.
• Education: Power BI is used in educational institutions to monitor faculty productivity, track student performance, and test research findings.

Notable Features of Power BI

Power BI is a powerful business intelligence and data visualisation tool that provides a range of features to help organisations analyse, visualise, and share data insights. 

Some key features of Power BI include:

Data Connectivity

Power BI makes it simple to import and analyse data from various sources by allowing you to connect to a wide range of data sources, including cloud-based data sources, on-premises databases, and Excel spreadsheets.

Data Visualisation

Power BI provides a wide range of interactive visualisations, such as charts, graphs, maps, and dashboards, to help you present data meaningfully.

Data Insights

To help you understand your data and make wise decisions, Power BI offers advanced analytics features like data exploration, modeling, and data insights generation.

Report Creation

Power BI allows you to create reports based on data from many sources, including Excel spreadsheets, SQL databases, and cloud-based data sources, making it a popular tool for report creation and data storytelling.

Collaboration 

You can collaborate on data analysis projects using Power BI’s cloud-based platform for sharing data insights and reports with stakeholders. 

Real-time Data

You can build real-time, interactive dashboards with Power BI that give decision-makers access to key performance indicators, data insights, and other crucial pieces of information.

AI Insights

With the help of features like automated insights, natural language queries, and predictive analytics, Power BI enables you to take advantage of machine learning and artificial intelligence to extract insights from your data.

Popular BI Components

Power BI is a business intelligence and data visualisation tool that provides various components to help users turn data into insights and make informed decisions. 

Some of the main components of Power BI include:

Power BI Desktop: A Windows-based desktop application that provides advanced data modelling and visualisation capabilities.

Power BI Report Server: An on-premises report server that enables organisations to share Power BI reports within their organisation.

Power BI Service: A cloud-based service that allows users to access and share Power BI reports and dashboards from anywhere with an internet connection.

Power BI Report: A visual representation of data that can include charts, tables, and graphs.

Conclusion

Power BI is an influential tool organisations use to analyse, visualise, and share data insights. Its wide range of features and ability to connect to many data sources make it a popular choice for organisations looking to gain insights from their data.

If you want to learn more about power BI and Analytics, consider Imarticus Learning’s Postgraduate Program In Data Science And Analytics course.  

This comprehensive data science course with placement offers hands-on experience with real-world data and covers the latest tools and techniques in data analytics. 

The program is designed for professionals looking to develop their skills in data analytics. It provides a data analytics certification course to give you a competitive edge in the job market. 

Whether you’re looking to learn data analytics for personal or professional reasons, Imarticus Learning’s Postgraduate Program In Data Science And Analytics course is a great place to start.

Which Languages Are Used In Python Programming?

Python is a high-level programming language that has gained immense popularity in recent years. It is used in a wide range of applications, from web development to scientific computing. One of the reasons for its popularity is its simplicity and ease of use, making it an ideal language for beginners. Python is also open-source, meaning that it is free to use, distribute, and modify. But what are the languages used in Python programming? Let’s dive in and find out.

In this article, we will explore the various languages which are used in Python programming and methods in which Python is used for data visualisation.

Basic Language Used In Python

Python itself is written in C, a low-level programming language that is used to create operating systems, drivers, and other system-level software. C provides Python with the necessary speed and efficiency, making it a powerful language for various applications. Most of Python’s standard library is also written in C.

Other Languages Python Can Interface With

One of the benefits of Python is its ability to interface with other programming languages. This is made possible through language bindings, which are essentially a bridge between Python and another language. Some popular language bindings for Python include:

C/C++: Python can interface with C and C++ using the Python/C API, making it possible to write Python modules in C/C++.

Java: Jython is an implementation of Python that runs on the Java Virtual Machine (JVM).

NET: IronPython is an implementation of Python that runs on the NET Framework.

Rust: RustPython is a project that aims to implement Python in Rust, a systems programming language.

Python For Data visualisation And Data Analytics

Data analytics is a field that involves using data to make informed decisions. Python is a popular language for data visualisation, but it is often used in conjunction with other languages, such as:

SQL: SQL is used to query and manipulate data stored in relational databases. Python can interface with SQL databases using libraries such as SQLAlchemy and PyODBC.

R: R is a language specifically designed for data analysis and statistics. Python can interface with R using the rpy2 library.

MATLAB: MATLAB is a language commonly used in scientific computing. Python can interface with MATLAB using the MATLAB Engine API for Python.

There are many resources available for learning Python online like the Postgraduate Program in Data Analytics and Machine Learning. If you are interested in pursuing a career in data analytics, then enrolling in a data analytics course with placement is among the best ways.

Postgraduate Program in Data Analytics and Machine Learning

Young professionals may learn Python online and other new skills with the aid of Imarticus, a top learning platform that offers courses that are focused on the workplace. Students can enrol in the 6-month Postgraduate Program in Data Analytics and Machine Learning, a job-focused certification programme, to master machine learning and learn Python for data visualisation.

Students learn to create analytical models to solve practical issues and enhance corporate decisions. Together with a job interview guarantee, the Postgraduate Program in Data Analytics and Machine Learning enables students to work with organisations like Cognizant, IBM, Goldman Sachs, Standard Chartered, and others. The following are some advantages of completing Imarticus’ Postgraduate Program in Data Analytics and Machine Learning:

KPMG provides students with a completion certificate to add to their resumes.

Additionally, the course provides career services including profile development, interview training, mentorship, skill development workshops, placement opportunities and more.

The data analytics course follows a job-oriented curriculum which ensures that professionals learn what is necessary for their job. This also makes them ready for top management roles in organisations.

Students who enrol in the Postgraduate Program in Data Analytics and Machine Learning have the opportunity to compete in national-level events like hackathons hosted by KPMG India.

Final Thoughts

Python is a versatile language that can be used in a wide range of applications, from web development to data analytics. Its simplicity and ease of use make it an ideal language for beginners, while its power and flexibility make it a favourite among professionals. Python can interface with other languages, which allows it to be used in conjunction with other tools and techniques. If you’re interested in learning Python online, there are many resources available, including courses by KPMG and Imarticus.

Whether you’re a beginner or an experienced programmer, Python is a language that is worth exploring. If you want to advance your learnings, enrol for the Postgraduate Program in Data Analytics and Machine Learning. It helps you learn Python for data visualisation and data analytics.

Data science is one of the hottest topics to talk about these days, evident by the number of job boards that mention data science as one of the top skills hiring managers seek. As such, there’s a lot of demand for data scientists, so you’ll find many roles across different industries and companies. 

A data scientist can keep your business one step ahead by spotting trends, forecasting outcomes, and making data-driven decisions that boost performance. Hire a data scientist right away to take advantage of the opportunity to harness the power of data!

Let’s start by investigating the top 8 Data Science roles!

Data scientist

A data scientist examines data to uncover useful information. One example of a specific task is finding the data-analytics issues that present the organization with the most opportunities—choosing the appropriate variables and data sets.

Data Analyst

Data analysts use tools for data analysis to examine information, and they work with their teams to generate insights and develop business strategies. You will need proficiency with tools for data analytics and data visualization, as well as math, statistics, communication, and working with data. 

Machine Learning Engineer

Machine learning involves training computers to make decisions independently. It’s a crucial part of the field of data science and can be used to solve many problems in business, science, and engineering. Even though machine learning has been around for a while, its significance is only growing as more businesses start incorporating it into their goods and services.

Data Engineer

Data engineers are a crucial part of the data science team. They design, implement and maintain the infrastructure that enables data analysis. They usually work closely with their colleagues in other departments, such as software development or analytics teams, to ensure that all processes run smoothly.

Business Intelligence Analyst

Data science is a field that encompasses many different types of roles and disciplines. For example, business intelligence analysts focus on doing this effectively; they must understand what makes up a given dataset before working with it.

They might also be called BI developers or BI architects if they are responsible for building new features within their company’s existing reporting systems using SQL databases and NoSQL databases. These experts will also work closely with their team members to ensure that all their processes are aligned with the company’s goals–and since these professionals are often in high demand across industries like retail & hospitality services too!

Data Architect

A data architect is responsible for designing, developing, and maintaining the data architecture of a business. The job of a data architect is to design a system that can efficiently handle large amounts of information. They create a structure of databases, tables, and fields to store your company’s data to perform analyses on it. 

Data architects also ensure that they have good quality control measures in place so they don’t end up with incorrect information in their systems when trying to use them for analysis purposes later down the line.

Data Visualization Engineer

Data visualization is a significant part of data science. It’s transforming raw, unstructured data into information that people can easily understand. Data visualization is also used to create and present information in a way that makes it easy for people to understand.

Data scientists are expected to create visualizations from their research findings and use them to communicate with colleagues and clients alike. Data engineers help create these visuals by building tools like graphs or charts that display relevant data points from different sources.

Statistician 

A statistician is someone who, as the name implies, is well-versed in statistical concepts and methods. In addition to extracting and providing priceless insights from the data clusters, they also aid in developing new methodologies that engineers can use.

Explore your Career in Data Science with Imarticus Learning!

This Data Science certification Course promises to assist you in realizing the full potential of data science and creating models with the greatest possible business impact. This program offers a practical approach to understanding how analytics can drive real results in a business setting, whether you are a recent graduate or an experienced professional looking to advance your career in analytics.  

Course Benefits for Students:

• Students can study the data science course without ever leaving the comfort of their homes.
• Gain hands-on experience with data science and analytics codes.
• PG in Data Analytics Certification provides students with a thorough theoretical and practical understanding of data analytics.

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