Contact Us
×

Category: Analytics

Why Pursuing a BBA in Business Analytics is a Smart Career Move

Posted on by Imarticus

Are you trying to find a job with stability and room for growth? The only option is to study for a Bachelor of Business Administration (BBA) in Business Analytics. 

Businesses continue to rely on data-driven decision-making, driving up demand for experts in this area. The median salary for operations research analysts in 2021 was $82,360. That year, the top 25% earned $119,240, while the bottom 25% earned $61,940.

This post will discuss the benefits of earning a BBA in Business Analytics and how it may lead to various fascinating work options. A BBA in Business Analytics can also provide you with the necessary information and skills to thrive in today’s data-driven business environment, whether you’re a graduate or an experienced professional wishing to change fields.

Importance of Business Analytics in the job market

The Future of BBA in Business Analytics looks promising. Almost 2.5 quintillion bytes of information are produced daily, quickly increasing. These figures demonstrate the increasing demand for specialists in this field for many more years to come. As a result, graduates with a BBA in Business Analytics will always be in demand and have a promising future in the global market.

Today, businesses are all about data. Business analytics come into play in this situation. Making wise business judgments involves looking at, evaluating, and interpreting data. And let’s face it, a company’s ability to make the correct choices may make or break it. 

Business analytics aids in trend identification, consumer behavior forecasting, and operational optimization to boost profitability. It works like a crystal ball that informs you what steps to take next. So it’s critical to have a strong background in business analytics if you want to excel in the corporate sector. 

Data-driven business choices are guided by the BBA-Business Analytics program, which keeps businesses flexible and competitive. This course combines computer programming with data analysis and business intelligence. 

Reputable companies worldwide are looking for business analysts and data analysts who can extract workable and useful insights from the data gathered using particular tools and approaches. This process proceeds from defining a business problem to data collection, relationship creation, and data cleansing in preparation for analysis and modeling. 

Three categories of analytics are further used to categorize this process:

Descriptive Analytics

Predictive Analytics

Prescriptive Analytics

What are the job prospects for BBA in Business Analytics graduates?

  • Retail Sales Analyst
  • Big Data Analyst
  • Data Business Analyst
  • Business Analyst Manager or Consultant
  • Machine Learning Engineer
  • Business Analyst Specialist
  • Financial/Marketing Analyst
  • Data Scientist
  • Quantitative Analyst/Modeller
  • Project manager
  • Data mining manager
  • Business Intelligence and Analytics Consultant

Why is a BBA in Business Analytics a smart career move?

Navigating the Business World with Business Intelligence

  • Knowledge and in-demand skills

Business analytics is one of the industries with the highest global growth. This implies that people with the abilities and expertise to evaluate data and deliver insights will have more possibilities and earn more money. 

A BBA in Business Analytics will teach you how to gather, handle, and understand data from multiple sources using various tools and techniques, including statistics, machine learning, data visualization, and programming. Additionally, you’ll discover effective and convincing ways to present your findings to diverse stakeholders.

  • An advantage in the job market

Your BBA in Business Analytics will provide you with a special blend of business knowledge and technical proficiency that will set you apart from the competition. You’ll be able to use your analytical abilities to tackle issues and situations in the real world that are pertinent to your field and business. 

You will also showcase your originality and ingenuity by coming up with fresh approaches to using data to enhance procedures, goods, services, and client happiness. You may become a business analyst, data analyst, data scientist, business intelligence analyst, and more with a BBA in business analytics.

  • Flexible and diverse career path

When it comes to picking your professional path, having a lot of alternatives and freedom is one of the best aspects of obtaining a BBA in Business Analytics. You can work in any field that appeals to you, including manufacturing, retail, finance, healthcare, education, and the arts. 

Additionally, you can work for various businesses, including startups, large enterprises, NGOs, or governmental entities. You may even launch your own company or work as a consultant or freelancer. There are countless options!

  • A positive impact on society

Earning a BBA in Business Analytics will enable you to constructively contribute to society by using data for good. You may utilize your expertise and abilities to contribute to the resolution of the most important problems and difficulties we are now facing, such as poverty, climate change, health care, and education. By giving people and communities knowledge and insights to enhance their lives and well-being, you can use data to empower individuals and groups.

The Final Words

As you can see, pursuing a BBA in Business Analytics is a smart career move that will open up many doors and opportunities for you. You will gain valuable skills and knowledge in high demand and a fulfilling and meaningful career that can make a difference. So what are you waiting for? 

The dynamic partnership between Imarticus Learning and Geeta University has created a wonderful opportunity. The BBA program in business Analytics has been cleverly designed to meet the needs of ambitious students ready to fully immerse in analytics and use it to develop unmatched company strategies. You will become skilled at analyzing data, revealing insights, and implementing tactical plans to guide your company to unparalleled success with this program.

Enroll in a BBA in Business Analytics program today and start your journey towards success!

The Role of Data in Business Strategies

Posted on by Imarticus

Businesses of all sizes now rely on data to gain a competitive edge in today’s data-driven world. By analysing market trends, consumer behaviour, and sales data, businesses can optimise operations, improve efficiency, and develop more effective marketing campaigns. These strategies can lead to a huge growth in revenue for businesses employing such business management techniques. 

Data has revolutionised the way businesses operate, and it is now a critical component of business management. Leveraging this information helps businesses gain insights, make informed decisions, and stay ahead of their contemporaries. A solid data strategy is essential for organisations to achieve their business objectives and multiply revenue. 

What is Data in Businesses?

Data in business is the summation of information relating to operations, sales, customer reviews, and other essential information about the business. All businesses carefully analyse these data to further formulate effective business strategies and improve operations. This task involves statistical analysis of data sets and their interpretation. 

What Are Business Strategies?

Business strategies are actions that lead to a company fulfilling its aims and objectives. Formulating a business strategy involves a structured process of analysing the internal and external environment, setting clear and specific goals, developing a strategy to achieve those goals, implementing the strategy, and evaluating and adjusting the strategy according to the needs of the company. 

The process requires careful planning, analysis, and execution to ensure that the strategy is effective in achieving the company’s objectives. Through the use of appropriate key performance indicators (KPIs) and tools, companies can mitigate biases and make effective managerial decisions that are in line with their overall strategies. A BBA in Business Analytics can help you become a master of business strategies.

How is Data Useful for Businesses?

No business decision can be made without accurately analysing data. Data helps companies do away with redundant strategies and employ more useful ones to maximise business benefits. Businesses can remain competitive, innovate, and drive growth with the help of data.

Data plays a crucial role in business as it enables leaders to make informed decisions, which can improve overall business performance, streamline operations, and strengthen customer relationships. With data-driven insights, businesses can identify patterns, trends, and potential opportunities or risks that might not be visible without correctly analysing and interpreting data.

How Does Data Help in The Growth of The company?

Data can help companies in numerous ways. Here are some of the ways businesses can leverage data for better outcomes:

Risk Management

Data analytics provides valuable insights into a business’s environment, enabling them to identify areas that may pose risks to its long-term strategy. A thorough insight into a business can expose a number of factors that could impact its operations in the future and handle them proportionately. 

Serve Customers Better

With the help of customer reviews and buying habits of your customers, you will be able to improve your products and services. These insights will in turn help you make better profits. Remember that it is always important for businesses of all sizes to take note of what their customers want. 

Increase Revenue

Effective utilisation of data can enable businesses to enhance their revenue and reach out to more customers. Companies can identify potential growth areas and devise appropriate strategies to implement changes. 

By strengthening their understanding of market trends businesses can gain a competitive advantage while creating more opportunities for generating revenue.

Cost Cutting

When you analyse such a huge chunk of data, you are bound to be exposed to areas that are contributing to unnecessary costs. By evaluating such areas efficiently you will be able to significantly cut costs and increase profits.

Facilitate Business Growth

Making decisions based on data facilitates companies to identify crucial insights across various functions, departments, and operations. Consistently making informed decisions can help companies establish measurable benchmarks that can lead to tremendous business growth. 

How Can Businesses Use Data to Build Business Strategies?

Here are a few steps businesses can follow to develop business strategies:

  • Gather data that is relevant to your business goals and objectives. This could include data from internal sources such as sales, customer feedback, or financial statements, as well as external sources like market trends, competitor analysis, and industry reports. 
  • Use data analytics to analyse the data that you gather from your businesses. You could use data visualisation, statistical analysis, and other methods to analyse the data you collected. 
  • Based on your analysis, identify the key insights that will help you achieve your business goals. These insights should be actionable and relevant to your business. 
  • Monitor and evaluate the performance of your business strategy from time to time so that you can understand whether the strategy is effective. Use data to measure the success of your strategy and make adjustments as needed. 
  • Use feedback from customers, employees, and other stakeholders to refine and improve your business strategy over time. This will involve collecting new data and adjusting your approach based on the insights you gain. 

Conclusion

Analysing data to drive business growth can have an added advantage for your business. By leveraging the ability to observe and analyse historical and current data, companies can extract valuable insights. This can help companies in the effective implementation of policies and strategies, ultimately contributing to the growth and success of the organisation. 

The field of Business Analytics is a highly sought-after specialisation as it employs cutting-edge data science methods to anticipate and prepare for future business results. This degree is especially helpful for aspiring BBA candidates in today’s increasingly data-driven world. Enrol in the BBA in Business Analytics by Imarticus Learning to master Business Analytics and business management.

5 ways to enhance the supply chain management strategies

Posted on by Imarticus

With increased globalization, supply chain management has become crucial for businesses. The supply chain involves an intricate system of suppliers, manufacturers, distributors, and retailers collaborating to deliver customer products and services. 

global chief supply chain management course

Implementing effective supply chain management strategies can aid companies in cutting costs, boosting efficiency, and improving customer satisfaction. In other words, a supply chain is a key component of modern business operations, and mastering it can lead to significant benefits for companies. 

Over the previous five years, U.S. warehouses have grown 68%. Therefore, this is evident in need for warehouse space. Fewer establishments serve as warehouses and retail sites since more consumers and e-commerce transactions exist. The digital model now gives the warehouse priority.

What is Supply Chain Management?

Supply chain management involves a system of logistics, marketing, operations management, and procurement to transform raw resources into completed goods and deliver them to the final consumer. All the processes involved in acquiring, manufacturing, and delivering goods and services must be coordinated and integrated.

A responsive supply chain’s goals are as follows:

  • Satisfying client demands (delivering on time and at the expected cost)
  • Having the ability to scale up or down when demand forecasting fails
  • Handling expenses in uncertain times
  • Adjusting procedures and workflows as needed to meet demands and circumstances at hand
  • Lowering risk via preparation, diversification, and formation of the appropriate relationships

5 Ways to Enhance Supply Chain Management Strategies.

  • Embrace Technology: Using technology can significantly improve the efficiency and effectiveness of supply chain management. Implementing technology solutions such as supply chain management software, automation, and data analytics can help you optimize your supply chain. 
  • Foster Collaboration: Collaboration is crucial in supply chain management. Establishing strong relationships with suppliers, manufacturers, and distributors can help to streamline the supply chain and improve performance. Collaborating with partners can help you identify cost savings opportunities, reduce lead times, and improve product quality. 
  • Focus on Customer Needs: Supply chain management is not just about moving products from one place to another. It’s about meeting the needs of customers. Understanding customer needs essential in developing a supply chain strategy that is customer-focused. 
  • Optimize Inventory Management: Inventory management is integral to supply chain management. Effective inventory management can help you reduce costs and improve customer service. By using forecasting tools and demand planning techniques, you can optimize inventory levels and reduce the risk of stockouts. 
  • Monitor Performance: Monitoring supply chain performance is essential in identifying areas for improvement. By setting key performance indicators (KPIs) and monitoring them regularly, you can measure your supply chain’s performance and identify improvement areas. KPIs can include delivery times, inventory turnover, and order accuracy.

How can supply chain management courses advance your career?

Supply chain management courses can advance your career in several ways:

Develop specialized skills: Supply chain management courses provide specialized skills and knowledge in logistics, procurement, inventory management, and transportation. These skills can help make you unique from other professionals and advance your career.

Expand your network: Supply chain management courses provide opportunities to connect with other supply chain professionals in the industry. This can help you build a network of contacts who can provide job leads, mentorship, and advice.

Stay current with industry trends: Courses may assist you with staying current with trends, best practices, and technology in the supply chain business, which is an area that is continuously evolving. You may use this to increase your effectiveness in your present position and become ready for new possibilities.

Increase earning potential: By developing specialized skills and knowledge, you can increase your value to employers and earn a higher salary. Supply chain management roles are in high demand and often offer competitive salaries.

Advance leadership positions: Supply chain management courses can provide you with the necessary knowledge to advance to leadership positions. With leadership roles, you can have a greater impact on the organization and potentially earn a higher salary.

Gain a competitive edge: Supply chain management courses can give you an edge in the job market. With specialized skills and knowledge, you can stand out from other candidates and enhance your chances of being hired for roles in supply chain management.

The Final Note

Businesses must have a well-planned and efficient supply chain to reduce costs, improve productivity, and satisfy customers’ needs. This post has discussed five important ways to enhance your supply chain management strategies. By focusing on these areas, businesses can enhance their supply chain operations and stay competitive in the ever-changing global market.

So, if you want to learn supply chain in detail, then Imarticus learning Supply chain course is your one-stop destination. Their comprehensive courses cover all aspects of supply chain management, from logistics and inventory management to procurement and distribution. In addition, they offer leadership programs that can help you develop the necessary skills to manage teams and drive change within organizations.

Data Engineering and Building Scalable Data Pipelines

Posted on by Imarticus

The significance of data engineering and scalable data pipelines cannot be emphasised enough as organisations of all kinds continue to amass massive volumes of data. In order to derive insights and make educated choices, businesses need reliable means of storing, processing, and analysing their data.

The development of machine learning and the explosion of available data has made the creation of scalable data pipelines an essential part of data engineering. This blog will go into the basics of data engineering, revealing helpful tips for constructing scalable and reliable data pipelines to fuel machine learning in Python.

So, if you’re interested in learning how to handle data and unleash the full potential of data engineering, keep reading.

What is data engineering?

Data engineering is the process of planning, constructing, and maintaining the infrastructure and systems required to store, process, and analyse massive quantities of data. Data engineering’s purpose is to provide quick, trustworthy, and scalable data access for the purpose of transforming raw data into actionable insights that fuel business value.

When it comes to making decisions based on data, no company can do without the solid groundwork that data analysis and data engineering provides.

Distributed computing systems, data storage and retrieval systems, and data pipelines are just a few examples of the solutions that must be developed in order to handle big data.

What is a data pipeline?

The term “data pipeline” refers to a series of operations that gather information from diverse resources, alter it as needed, and then transfer it to another system for processing. In data engineering, data pipelines are often used to automate the gathering, processing, and integration of huge amounts of data from a variety of sources.

Often, data pipelines consist of numerous stages or components that collaborate to transfer data from source systems to destination systems. These steps may involve data intake, data preparation, data transformation, data validation, data loading, and data storage. The components used at each pipeline level rely on the use case’s unique needs.

How to build a scalable data pipeline?

Collect and store the data:

First, you need to find the data you want to analyse and then save it somewhere. As a result, it may be necessary to gather information from a variety of databases, application programming interfaces (APIs), or even human data input. After the data sources have been located, the data must be consolidated into a single repository for easy access by the pipeline. Data warehouses, data lakes, and even flat files are all common places to save information.

Extract and process the data:

After the data has been gathered in one place, it must be extracted and processed before it can be used to build the pipeline. It might entail cleaning, filtering, summing, or merging data from many sources. When data is extracted, it must be converted into a format that the pipeline can use.

Data is mainly processed using two different techniques:

  • Stream processing: A data processing approach that includes continually processing data as it enters, without storing it beforehand. This method is often used for real-time applications that need data to be handled as soon as it is created. In stream processing, data is processed in micro-batches, or small increments, allowing for real-time data analysis.

  • Batch processing: Refers to a method of data processing in which huge amounts of data are processed simultaneously, at predetermined intervals. Applications that need to analyse huge amounts of data over time but do not need real-time analysis might benefit from batch processing. The data in a batch processing job is often processed by a group of computers concurrently, which results in short processing times.

Load the data:

After data extraction and transformation, the data must be loaded into the pipeline. To do this, the data may need to be loaded into a memory cache or a distributed computing framework like Apache Spark. The information has to be easily accessible so that it may be analysed.

Designing the data pipeline architecture:

Lay up a plan for the data pipeline’s architecture before you start with the development process. Data processing pipelines have an architecture that specifies its parts such as a source, collector, processing engine, scheduler and more. These parts determine how information moves in the pipeline, and how that information is handled.

To guarantee the pipeline is scalable, resilient to errors, and straightforward to maintain, its architecture must be thoroughly thought out.

Developing the data pipeline:

Developing the data pipeline is the next stage after deciding on the pipeline’s design. Executing this requires setting up the data processing logic, integrating the pipeline parts, and implementing the pipeline components. At this stage, testing the pipeline is also performed to guarantee it operates as planned.

Monitor and optimise performance:

After the pipeline is up and running, it’s time to start keeping tabs on how well it’s doing. Checking for problems, such as bottlenecks or slowdowns, is part of pipeline monitoring.

Improving pipeline throughput may be achieved by making small changes to individual components, modifying the data processing algorithm, or replacing hardware. In order to maintain peak pipeline performance and maximise data processing efficiency, it is essential to constantly monitor and tune the pipeline.

Conclusion

Data engineering and building scalable data pipelines are crucial components of data analysis and decision-making in today’s business landscape. As data continues to grow, it becomes increasingly important to have the skills and knowledge to handle it efficiently.

If you’re keen on pursuing a career in this field, consider enrolling in Imarticus’s Certificate Program in Data Science and Machine Learning, created with iHUB DivyaSampark at IIT Roorkee. This programme will teach you everything you need to advance in the fields of data science and machine learning.

Take advantage of the opportunity to get knowledge from seasoned professionals in your field while also earning a certification from a prominent university such as IIT. Sign up for the IIT data science course right now and take the first step towards a successful and satisfying career in data engineering.

Inventory Management, Warehousing and Order Management: Importance and Best Practices

Posted on by Imarticus

Inventory management, warehousing, and order management are crucial to running a successful business. Businesses may simplify processes and increase customer satisfaction by properly managing these areas. These supply chain management strategies help companies address cash flow challenges and optimise the flow of materials, leading to improved efficiency and cost reduction.

This blog will discuss the importance of inventory management, warehousing, order management, and best practices.

First, let us understand what the 3 procedures are from the following table:

Aspect Inventory Management Warehousing Order Management
Definition The process of managing the flow and storage of goods, materials, and finished products. The process of storing, managing, and moving goods within a warehouse. The process of receiving, processing, and fulfilling customer orders.
Objectives Optimise inventory levels, minimise carrying costs, and meet customer demand. Maximise storage capacity, optimise the layout and improve operational efficiency. Streamline order processing, minimise errors, and improve customer satisfaction.
Key Activities Forecasting, planning, purchasing, receiving, storing, picking, packing, and shipping. Receiving, putaway, picking, replenishment, packing, and shipping. Receiving orders, processing orders, picking products, packing orders, and shipping orders.
Key Metrics Inventory turnover, stockouts, order fill rate, and carrying cost. Capacity utilisation, order cycle time, order accuracy, and labour productivity. Order processing time, order accuracy, on-time delivery, and customer satisfaction.
Technologies Inventory management software, barcode scanners, RFID, and automated inventory systems. Warehouse management systems (WMS), conveyor systems, automated storage and retrieval systems (AS/RS), and mobile robots. Order management software, customer relationship management (CRM) system, and enterprise resource planning (ERP) system.
Challenges Maintaining optimal inventory levels, reducing excess inventory, and managing stockouts. Maximising space utilisation, improving order accuracy, and reducing operational costs. Managing complex order fulfilment processes, ensuring timely delivery, and minimising errors.
Benefits Improved inventory accuracy, increased productivity, and enhanced customer satisfaction. Optimised space utilisation, reduced labour costs, and improved operational efficiency. Faster order processing, increased order accuracy, and improved customer satisfaction.

Importance and Best Practices of Inventory Management

Importance

Some importance of inventory management are:

  • Ensures fulfilment of incoming or open orders
  • Automates the process of ordering, storing, and optimising goods
  • Prevents the accumulation of dead stocks
  • Saves money by understanding stock trends and using stock efficiently
  • Manages the flow of raw materials from procurement to finished products
  • Enhances product quality
  • Minimises overstocks and improves efficiency
  • Enables businesses to monitor market trends and adjust product offerings accordingly
  • Accurately forecasts how much inventory is needed based on sales activity, preventing overstock and stockout
  • Helps businesses make smart decisions and adjust their product line

Best Practices

Here are some best practices for inventory management:

  • Accurately forecast demand to ensure the right stock levels are maintained
  • Track inventory from purchase to sale to identify trends and ensure adequate stock levels
  • Optimise the entire inventory process, from initial orders to restocking, receiving, storage, and shipping
  • Ensure the right stock is available in the right place, at the right time, and at the right cost
  • Implement a system for tracking inventory turnover and identifying slow-moving items
  • Design the warehouse for efficiency by placing top-selling items in easily accessible locations
  • Conduct regular audits to ensure inventory accuracy and identify discrepancies
  • Train employees on inventory management best practices

Importance and Best Practices of Warehousing

Importance

Some importance of warehousing are:

  • It helps in inventory management, which involves tracking and organising products and goods shipments
  • Warehousing is essential for the efficient storage and management of goods
  • Helps in improving customer service by ensuring that products are available when needed and delivered on time
  • Proper warehouse setup, receiving goods efficiently, storing temperature-sensitive products correctly, picking and packing are some of the functions of warehousing
  • Helps in reducing transportation costs by consolidating shipments and reducing the number of trips
  • It provides a safe and secure environment for storing goods, protecting them from damage, theft, and other risks

Best Practice

Here are some best practices for warehousing:

  • Reduce touch points to streamline operations
  • Control inbound and outbound freight to reduce costs
  • Use automatic data collection to track inventory and avoid manual data entry processes
  • Efficient labour is key to successful warehousing
  • Implement minimal touch of goods, one-way flow, and triadic warehousing principles for warehouse design and operation
  • Use a warehouse management system to maintain a system of record for all inventory
  • Use warehouse management software to help with daily tasks and increase efficiency
  • Better storage allocation helps to optimise warehouse layout and item placement
  • Benchmarking is important to determine best-in-class pick rates and compare performance against industry standards
  • Inventory tracking is essential to warehouse management

Importance and Best Practices of Order Management

Importance

Here are some points explaining the importance of order management:

  • Allows businesses to coordinate the entire fulfilment process
  • Helps to manage inventory levels, ensuring that products are available when customers place orders
  • It helps to ensure that orders are processed accurately and efficiently, reducing the possibility of human error
  • It can help businesses to reduce costs by optimising and reducing waste
  • Helps to improve customer satisfaction by ensuring that orders are fulfilled on time and accurately
  • Allows businesses to track orders from start to finish, providing visibility into the entire fulfilment process

Best Practices

Best practices of order management include:

  • Real-time synchronisation of information to ensure accuracy and avoid errors
  • Integrating with other systems, such as inventory management and shipping, to streamline the process
  • Centralising all orders in one system to keep track of everything in one place
  • Accepting and fulfilling orders, managing inventory, and engaging in post-sales interactions
  • Developing a request for proposal for an order management system to ensure that the system meets the company’s business requirements
  • Mapping order management processes to the supply chain to ensure that the system fits into the order of things and does not disrupt other IT systems
  • Transforming ERP systems to optimise order management processes

Proper inventory management, warehousing, and order management are crucial for businesses that deal with physical products. By implementing these supply chain strategies, businesses can streamline their operations, reduce costs, and improve customer satisfaction.

Conclusion

The Advanced Certification Programme in Digital Supply Chain Management by Imarticus Learning is designed for graduates and diploma holders who wish to learn digital supply chain management.

The programme consists of seven modules that cover essential aspects of digital supply chain strategy and management. These modules include supply chain fundamentals, technology in inventory management, supply chain analytics, supply chain risk management, technology in logistics management, implementation of digital supply chain strategy in a supply chain, and more.

The programme aims to equip students with the required knowledge and expertise to succeed in the fast-growing field of digital supply chain management.

Python in Data Science: Real World Applications (Spotify, Netflix, Uber etc.)

Posted on by Imarticus

Talk of the leading global tech companies and you will see they use Python programming as an integral part of their technology stack. Created in 1991, Python has become one of the most popular programming languages worldwide. The simplicity, shorter learning curve, reduced development time and effortless coding experience has made Python a coveted choice with many developers. 

Let us see how Python is used in Data Science projects and web development along with some real-world applications. 

Using Python in Data Science projects and web development

The universal, high-level programming language, Python is used extensively in various web development and Data Science projects. 

  • Web development

Flask and Django are Python frameworks, which are famous for web development. Python also has extensive modules and libraries, which speed up development time considerably. 

  • Web scraping applications 

Python facilitates extracting huge volumes of data quickly from sources for price comparisons, research and development and email address gathering. With the logistic regression classification technique, Python solves classification problems. The simple-to-code programming language has a lucid syntax and a great collection of useful libraries like Pandas, Numpy and Matlplotlib. 

  • Data Science

Python helps in quickly analysing and manipulating data. The programming language has graphing libraries which support data visualisation. Moreover, you will find a vibrant and active Python Data Science community. 

  • Game development

Python libraries like Pygame are great for building games and prototypes. Popular games like Battlefield 2, EVE Online and World of Tanks are built with Python. 

  • Python application development

Since Python is a general-purpose language, it is used for developing desktop GUIs, file directories and APIs. 

Real-world applications of Python programming

There are thousands of Python websites and apps running on the internet successfully. Let us take a look at some of the real-world applications using Python. 

Uber

Uber, the well-known mobility-as-a-service company, had doubts about choosing between Ruby and Python while selecting a programming language. They chose Python for the backend and frontend functions. 

The Uber platform needs to make many calculations. Uber’s backend predicts traffic, demand and supply, arrival times, approximate reaching time to the destination, etc. Python is also great for mathematical calculations at big data levels. 

Reddit

Do you know the internet’s popular source of cat videos or dank memes? You guessed it right – Reddit. Self-acclaimed ‘internet’s front page’, Reddit is also a great source of community interactions. 

Reddit uses Python as the programming language because of its easy readability and writeability. Moreover, Python has diverse arrays of ready-to-use libraries. Along with Python, Reddit also uses Javascript and Go. 

Spotify

As a music enthusiast, you do not need to go anywhere else other than Spotify to listen to the kind of music that you love. Spotify has developed as a huge podcast and music streaming platform with more than 489 million active users monthly worldwide. 

You don’t need to look for MP3s, torrent links, or other websites to listen to your favourite music. Spotify developers have used Python for building infrastructure to run user forecasts. 

Instagram

Python is the main programming language used in Instagram. There have been many changes in Instagram’s tech stack, but the app wouldn’t be born without Python. Instagram was built using Django, which is a Python web framework. The viral video and image-sharing platform has almost 1.35 billion users globally in 2023, which is expected to reach 1.44 billion by 2025.  

With an increasing number of users, Instagram developers are creating static sort checkers using Python for server investigation. The server has millions of lines of Python code. 

Netflix

Netflix began its business as a DVD-by-mail service. Today it has become a leading video streaming platform with millions of paid subscribers globally. One of the reasons for the popularity of Netflix is its powerful analytics and recommendation engine. The company offers suggestions to users by understanding the kind of content that they watch. The recommendation and analytics engine is based on Python. 

Extremely intuitive, Python programming language helps in solving complicated networking problems. The content lifecycle of Netflix uses Python including security tools and Machine Learning recommendation algorithms. For statistical analysis, developers use Python libraries. Python is also used for automation tasks, data cleaning and exploration and data visualisation. 

YouTube

YouTube is not only a video-streaming platform on the internet, but it is also the second-largest search engine after Google. YouTube has billions of logged-in users monthly. Along with being a search engine, YouTube is also a popular social media platform. 

YouTube is written exclusively in Python. The interactive experience that users enjoy is due to the various libraries and features of the Python language. The coding of the platform is done in a manner so that the process of downloading, uploading and sharing videos becomes easy. 

Quora

Quora is a question-and-answer platform mainly targeted at professionals who seek answers to various queries on different subjects. Quora has almost 300 million users. Along with sharing answers, professionals also share their experiences on various subjects on Quora. 

Developers of Quora tried using various programming languages for the development of the platform. Python suited them best more so because of the amazing development speed of the programming language. 

Conclusion

Python programming language is a favourite among all leading global technology leaders for its robust, reliable and engineering enterprise-level applications. The majority of websites and apps use Python for their development as the coding is simple and easy. Becoming a Python developer will help in making a great career with a lucrative pay package. 

Imarticus Education offers a Postgraduate Programme in Data Science and Analytics through classroom teaching and live online training modes. You can build your career with this Data Science course with placements. Enhance business outcomes with real-world application of Data Science. 

The course curriculum includes fundamentals and complex concepts of Data Science and Analytics. Certain subjects that the module covers include the basics of Excel for Data Science, SQL programming, Python programming, Statistics for Data Science, Machine Learning, Data Visualization with Power BI and Tableau and many more. 

Enrol for the course right away!

What is Performance Analytics? How does it help businesses?

Posted on by Imarticus

All industries big or small, largely focus on analysing their performance to drive better business outcomes and accelerate growth through a method called performance analytics. Business performance evaluation involves assessing a company’s ability to achieve its goals and generate profits from the resources it utilises.

 

The evaluation of business performance is crucial for companies to remain competitive and successful. By regularly monitoring and analysing KPIs, businesses can identify potential issues before they become major problems and take corrective actions to improve their performance. Performance analytics is thus an integral part of business management and students who take up a BBA in Business Analytics have a thorough understanding of it.

There are various tools that professionals use to do performance analysis of businesses. One can seek employment in several capacities such as a business analyst, data analyst, or information security analyst if one wants to take up performance analytics. 

What is Performance Analytics?

All businesses must analyse the performance of their companies, whether it is positive or negative. This analysis is usually done by taking into account some indicators. Decision-makers, with the help of these indicators, are able to identify areas of deficiency in performance and make informed decisions on how to enhance those specific areas.

One of the key benefits of performance analytics is that it enables businesses to focus on the things that matter most. By providing insights into the most critical KPIs, businesses can prioritise their efforts and resources toward achieving their goals. Performance analytics also connects people across different departments and levels of the organisation and enables them to work together towards a common objective.

How Does Performance Analytics Help Businesses?

There are numerous ways performance analytics helps in business management. Here is how:

  • Businesses can improve their operations by using performance metrics to identify areas where they are not meeting their desired performance levels. After identifying such gaps they design better ways to address those problems that keep them from achieving the desired results.

  • Through performance analytics, businesses can detect bottlenecks in their processes and streamline their operations, improving efficiency.

  • Organisational growth is powered by HR and performance metrics, as structured HR data and analytical reports facilitate data-driven decision-making that informs future growth strategies. By leveraging project-wise performance analytics, businesses can identify their talent strength across projects and develop plans for the future and allocate responsibilities accordingly.

  • By analysing the market and consumer behaviour over time, businesses can determine if their product is meeting consumer demands and identify new opportunities for growth. It is important to track all the changes that have made an impact on the consumer base, economic conditions, market demand or supply adjustments, and any modifications made to the marketing plan.

  • By analysing performance metrics, businesses can identify areas where resources are being underutilised or over-utilised and adjust accordingly, leading to better resource allocation and cost savings.

What are the types of performance analytics?

There are mainly 5 types of performance analytics methods that are usually used by businesses. Here is a brief account of each of them:

Prescriptive Analysis

Prescriptive analysis is a kind of analytics that makes use of machine learning techniques, statistical algorithms and data to provide recommendations on what actions should be taken to achieve a specific outcome.  

Prescriptive analytics not only predicts what is likely to happen but also suggests specific actions to optimise the outcome. This type of analysis is particularly useful in complex business scenarios where there are many variables and also numerous possible outcomes. 

Diagnostic Analysis 

Diagnostic analysis is a type of analytics that aims at diagnosing the root cause of a problem. It involves examining data and analysing it to uncover the reasons behind a particular trend or behaviour. 

By utilising techniques such as drill-downs, data mining, data discovery, and correlations, businesses can gain a better understanding of the factors that are driving a particular trend or behaviour.

Companies often use diagnostic analysis in quality control and risk management so as to maintain high standards and prevent errors or failures.

Predictive Analysis

Predictive analytical tools aid in making future predictions by utilising historical data to identify patterns and assess their likelihood of recurring. The process typically involves gathering and analysing large datasets, developing predictive models, and using those models to make informed predictions about future events or behaviours.

Predictive analysis is used in a variety of fields, including business, healthcare, finance, and marketing, to forecast customer behaviour, identify potential risks or opportunities, and optimise decision-making. 

Cognitive Analysis

A cognitive analysis is a process of using artificial intelligence and machine learning techniques to simulate human thought processes and understand complex data. It involves using natural language processing, image recognition, and other AI technologies to analyse unstructured data. 

Cognitive analytics can help anticipate potential outcomes and suggest ways to manage them if conditions change. This can aid in decision-making and enable businesses to be better prepared for various scenarios. 

Descriptive analysis

Descriptive analysis is a statistical analysis technique that involves the summarisation and description of the characteristics of a dataset. Descriptive analysis can identify patterns and trends in the data, which can help to uncover insights and inform decision-making.

Conclusion

If you want to set your foot in the field of Business Analytics, you should consider taking up the BBA in Business Analytics program by Imarticus Learning. The BBA in Business Analytics is an ideal program for individuals who wish to develop a comprehensive understanding of the practical application of analytics in developing effective business strategies. 

This course is designed to equip students with the necessary skills and knowledge to excel in the contemporary data-driven business landscape. It focuses on enabling students to leverage the right combination of data analytics, insights, and strategies to achieve superior business outcomes.

What is Material Procurement and Why is it One of the Most Important Aspects of SCM?

Posted on by Imarticus

Supply Chain Management (SCM) is a critical component of any business, as it affects everything from production and manufacturing to delivery and customer satisfaction.

Material procurement is one of the most noteworthy aspects of supply chain management strategy, as it directly affects a company’s ability to meet customer demand and maintain a competitive advantage in the market.

In this blog, we will explore why material procurement is so important and what businesses need to know in order to ensure the success of their procurement process management. 

What is Material Procurement?

global chief supply chain management course

Material procurement refers to the process of acquiring raw materials, supplies, and components needed for production and manufacturing.

 It’s a critical function that ensures that a business has the resources it needs to operate efficiently and effectively. In this blog, we’ll discuss the various stages involved in material procurement and what businesses need to know to get the best results from their procurement processes.

 The first stage of material procurement is the identification of the raw materials, supplies, and components required for production.

 This stage involves determining the quantity, quality, and specifications of the materials needed, as well as the time frame for delivery. It is crucial to consider the long-term demand for the materials, as well as the potential for fluctuations in the market.

 The second stage of material procurement is the sourcing of the materials. Procurement process management involves researching potential suppliers, evaluating their offers, and selecting the best option based on a number of factors, such as price, quality, delivery time, and reliability.

 Businesses can choose to source materials directly from suppliers or through intermediaries such as wholesalers or distributors. In some cases, businesses may even choose to produce the materials in-house.

 The third stage of material procurement is the negotiation of the terms and conditions of the agreement with the supplier.

 This stage involves establishing the price, delivery terms, payment terms, and any other relevant conditions.

It’s important to ensure that the agreement is legally binding and that all parties understand the terms of the agreement.

 The fourth stage of material procurement is the placement of the order and the delivery of the materials.

 This stage involves communicating the order to the supplier, arranging for the delivery of the materials, and receiving and storing the materials in a secure and controlled manner.

 It’s important to have a system in place for tracking the delivery of materials, as well as for tracking the inventory of materials once they have been received.

 One of the key factors in successful material procurement process management is having accurate and up-to-date information about the materials being procured.

 This includes information about the materials themselves, such as their specifications and quality, as well as information about the suppliers, such as their financial stability and delivery times.

 Having access to this information allows businesses to make informed decisions about which materials to purchase and from whom.

 Another important factor in material procurement has a system in place for tracking and managing the procurement process.

 This can include using software or other technology to manage the process, as well as having clear procedures and processes in place for tracking and monitoring the delivery of materials and the payment of suppliers.

 This can help to ensure that the procurement process runs smoothly and that the materials are delivered on time and within budget. 

Why is Material Procurement one of the most important aspects of SCM?

Supply chain management strategy is important for businesses to have a reliable and efficient procurement process in place in order to ensure that they have access to the required materials when they need them.

 Without effective material procurement, a business may face challenges such as stock shortages, production disruptions, and late deliveries, all of which can negatively impact the company’s bottom line.

 One of the key reasons why material procurement is so important is that it directly affects a company’s ability to meet customer demand. If a company is unable to secure the materials it needs to produce its products, it will not be able to meet customer demand and may lose market share to its competitors.

 Additionally, if the procurement process is not efficient and well-organised, the company may struggle to keep up with customer demand, leading to long lead times and frustrated customers.

 Another reason why material procurement is so important is that it affects the cost of production. The cost of raw materials can have a significant impact on a company’s overall production costs and, therefore, its profitability. By optimising the procurement process and securing the best possible prices for raw materials, companies can reduce their production costs and improve their bottom line.

 Additionally, by having a well-managed procurement process in place, businesses can avoid overstocking or understocking materials, both of which can be costly and disruptive.

 Material procurement also plays a key role in risk management. The procurement process involves sourcing materials from suppliers, and it is essential for businesses to choose suppliers that are reliable and financially stable.

 If a supplier goes bankrupt or experiences financial difficulties, it can impact the delivery of materials and disrupt the production process. By carefully selecting suppliers and regularly monitoring their financial stability, businesses can minimise the risk of supply chain disruptions and ensure that they have access to the materials they need to meet customer demand.

Conclusion

In conclusion, material procurement is a critical function for businesses in a variety of industries. It involves several stages, including the identification of materials, sourcing, negotiation, and delivery.

To be successful, businesses need to have a supply chain management strategy which holds accurate information about the materials being procured, a system for tracking and managing the procurement process, and an awareness of the environmental and social implications of the procurement process.

By taking these factors into account, if your ambition is to become a chief supply chain officer, you can opt for Imarticus’ IIM supply chain management course. This programme offers all the resources that you need to learn to operate effectively and efficiently and to contribute to a sustainable future.

Statistics in Analytics: Essential Statistical Techniques

Posted on by Imarticus

In today’s data-driven world, businesses are increasingly relying on analytics to gain insights and make informed decisions. One of the fundamental pillars of analytics is statistics, which involves using mathematical methods to collect, analyse, and interpret data. From predictive modelling to hypothesis testing, statistics play a crucial role in uncovering meaningful patterns and trends in data.

In addition to mathematical techniques, data visualisation is also a key component of statistical analysis, as it allows us to present complex data in a way that is easy to understand and visually appealing.

Let us explore essential statistical techniques that are commonly used in analytics and how they can enhance our understanding and interpretation of statistical results.

Essential Statistical Techniques Used in Analysis

There are a plethora of statistical techniques you can employ in order to get valuable insights from the data you possess. Down below are listed a few of them with real-world examples for each:

Descriptive Statistics

Descriptive statistics as defined by the word itself ‘describes’ the essential features that one can gather from a dataset. Let’s take a small example to understand it better.

Imagine a hypothetical scenario where a business is tasked with analysing the sales data for a product over the past year. The dataset they have at their disposal includes a wide range of variables such as the number of units sold each month, the average price per unit, and the total revenue generated.

To gain a more granular understanding of the data, the business could employ descriptive statistics techniques. This would allow them to summarise and describe key features of the dataset in a more intuitive manner.

The main techniques employed in descriptive statistics:

  • Mean
  • Median 
  • Mode
  • Variance
  • Standard Deviation

Inferential Statistics

Inferential statistical techniques are employed to ‘infer’ the differences amongst groups of data and then make an assumption about the vast population pertaining to the insights gained from the inference.

Let’s say that you own a healthcare company and want to determine whether a new medication is effective in reducing blood pressure. You conduct a randomised controlled trial where you randomly assign patients to receive either the new medication or a placebo. After the trial, you collect data on the blood pressure readings for both groups. 

To draw insightful inferences about the effectiveness of the new medication, you could leverage inferential statistics techniques to analyse the data. By calculating the difference in the mean blood pressure readings between the two groups, you can gain a deeper understanding of the impact of the medication on blood pressure levels using data visualisation.

Following the inference, you could employ a hypothesis test to determine if the difference in blood pressure readings between the two groups is statistically significant. This would allow you to draw strong conclusions about the effectiveness of the new medication in reducing blood pressure.

The main techniques employed in inferential statistics:

  • Hypothesis Testing
  • Confidence Intervals

Correlation Analysis

Correlation Analysis is a statistical technique used to determine whether or not there is a link between two variables/datasets and the strength of that relationship.

Let’s say a company wants to investigate the relationship between advertising spend and sales revenue. They have collected an extensive dataset that contains information on the amount of money spent on advertising and the corresponding sales revenue for each month over the past year.

To unravel the intricacies of the relationship between advertising spend and sales revenue, the company could use correlation analysis techniques. This would involve calculating the correlation coefficient, which is a numerical measure that reveals the strength and direction of the linear relationship between two variables.

In the case mentioned above, the variables would be advertising spend and sales revenue. The most widely used correlation coefficients are ‘Spearman’s Rank Correlation Coefficient’ and the ‘Pearson Product-Moment Coefficient’.

If the correlation coefficient is positively skewed, it would indicate a strong positive relationship between advertising spend and sales revenue, signifying that as advertising spending increases, sales revenue also tends to increase. Conversely, if the correlation coefficient is negatively skewed, it would indicate a negative relationship, implying that as advertising spending increases, sales revenue tends to decrease.

Regression Analysis

Picture a world where you’re looking to understand the relationship between two or more variables. In walks regression analysis, a statistical method that helps you do just that. This technique is heavily utilised in an array of fields, including economics, finance, marketing, and social sciences.

It aims to pinpoint a mathematical equation that can predict the value of one variable based on the values of other variables. The variable being predicted is known as the dependent variable, while the variables that are used to predict it are known as independent variables or predictors.

Let’s say a car manufacturer wants to predict the fuel efficiency of its vehicles based on various factors such as engine size, weight, and transmission type. To achieve this, they conduct a regression analysis to identify the most significant predictors of fuel efficiency.

The manufacturer compiles data on fuel efficiency, engine size, weight, and transmission type for each of their car models. They then utilise regression analysis to construct a mathematical equation that optimally foresees fuel efficiency grounded on these variables.

Upon scrutinising the data, the regression model reveals that engine size and weight wield significant influence on fuel efficiency, whereas transmission type has no substantial impact on fuel efficiency. The car manufacturer can exploit this knowledge to reconfigure its production methods and make adjustments to the design of their cars to fine-tune fuel efficiency.

The dependent variable in the scenario above is the fuel efficiency of the vehicles. This is the variable that the manufacturer is trying to predict based on the values of the independent variables.

The independent variables are the engine size, weight, and transmission type of the vehicles. These are the variables that are used to predict the fuel efficiency of cars.

The most widely used techniques in regression analysis are:

  • Linear Regression
  • Logistic Regression
    NumPy, a popular Python library, is used for regression because it provides a fast and efficient array operation, mathematical functions, linear algebra operations, and interoperability with other libraries. These features of NumPy make it an ideal tool for handling the computations involved in different types of machine learning including regression modelling.

Conclusion

As you can understand from the aforementioned, statistical techniques play a crucial role in insights for businesses to function optimally. Though we discussed a few of the plethora of techniques, it is important to remember that these techniques are used in most types of machine learning.

If you’re interested to know more about techniques such as cluster analysis, time series analysis, and many more, then you should check out the Postgraduate Programme in Data Science and Analytics offered by Imarticus Learning. With expert instructors, hands-on projects, and a industry-relevant curriculum, this programme can help you launch your career in the dynamic field of data science. Don’t wait, click now to learn more and enrol today!

What are the 4 types of machine learning with examples

Posted on by Imarticus

Welcome to the world of machine learning! It’s no secret that machines are taking over more human life as technology develops. Machine learning has become the most crucial area in computer science with the development of artificial intelligence. Since it is engaging, several experts and computer enthusiasts are interested in this topic.

The practice of teaching machines to recognize patterns in data and take actions without explicit programming is known as machine learning. In other words, a computer system can use data to enhance performance on a particular job over time.

According to IDC, the market for AI software in India would increase at a CAGR of 18.1% from USD 2,767.5 million in 2020 to USD 6,358.8 million in 2025. 

Machine learning is therefore being embraced quickly due to its enormous potential to impact businesses all over India. With thousands of new opportunities being created daily, there is a tremendous demand for workers who can learn data science. We’ll discuss the four types of machine learning in this blog and give examples of each.

What is Machine Learning?

Artificial intelligence, known as “machine learning,” enables computer systems to learn from their past performance and advance. In other words, machine learning algorithms are trained to learn and develop independently instead of being programmed to do a certain task.

Types of Machine Learning

Supervised, unsupervised, semi-supervised, and reinforcement learning are the four primary categories of machine learning.

Let’s examine each category in more detail and give instances of their use.

  • Supervised learning

The most popular kind of machine learning is supervised learning. The method is trained on a labeled dataset in supervised learning. Each dataset’s data point includes a label indicating the desired result. The algorithm learns how to map inputs to outputs based on the labeled samples given during training. 

Supervised learning examples:

  • Image classification: The algorithm guesses the item in an image given only the picture. This is often applied in projects like medical image analysis, self-driving automobiles, and facial recognition.
  • Spam detection: It uses an algorithm to determine whether a given email is spam. Email filtering systems frequently employ something like this.
  • Predictive Maintainance: Using information about a machine, an algorithm may forecast when the machine is most likely to break down. In manufacturing and industrial applications, this is frequently utilized.

  • Unsupervised Learning

The method is taught on an unlabeled dataset in unsupervised learning. This indicates that the data lacks labels or categories. The algorithm learns to detect patterns or structures without understanding what the data represents.

Unsupervised learning examples include:

  • Clustering: When given data points, the algorithm clusters them according to similarity. Market segmentation, social network analysis, and picture segmentation frequently employ this.
  • Automatic detection: The method discovers the data points that are noticeably distinct from the rest of the data when given a batch of data points. This is frequently applied to medical diagnostics, network intrusion, and fraud detection.

  • Semi-Supervised Learning

The system is trained on a dataset that includes labeled and unlabeled data in semi-supervised learning. The algorithm learns to predict outcomes for the unlabeled cases using the labeled examples.

Semi-supervised learning examples include:

  • Language Translation: The system can translate new, unheard sentences given a small set of identified utterances. Applications for machine translation frequently use this.
  • Sentiment Analysis: The program can forecast the sentiment of new, upcoming reviews given a small number of tagged reviews. This is frequently used in consumer feedback analysis and social media monitoring.

  • Reinforcement Learning

A kind of machine learning called reinforcement learning teaches an algorithm to make decisions depending on input from its surroundings. The algorithm is taught to maximize a reward signal by choosing behaviors that produce the greatest reward.

Reinforcement Machine Learning examples: 

  • Video games: Real-time learning algorithms are widely used in game applications. It is utilized to perform at a superhuman level. The video games AlphaGO and AlphaGO Zero are examples of well-known RL algorithms.
  • Resource Management: To reduce average job slowness, the “Resource Management with Deep Reinforcement Learning” study demonstrated how to automatically utilize RL in computers to train and arrange resources to wait for various workloads.
  • Robotics: Several applications of RL are found in robotics. Robots are deployed in the industrial and manufacturing sectors, and reinforcement learning increases their power. 

Ending Note

Powerful technology like machine learning can completely change a variety of sectors. Understanding the many forms of machine learning is essential for companies and people wishing to use new technology. 

Each of the four categories of machine learning—supervised, unsupervised, semi-supervised, and reinforcement learning—has distinct properties and uses. Understanding the advantages and disadvantages of each form of machine learning will help you select the one that will work best for your needs and produce the best results. 

To develop the Certificate Program in Data Science and Machine Learning, Imarticus Learning collaborated with iHUB DivyaSampark @IIT Roorkee. Students interested in learning data science and machine learning course should start with this program.