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What is data visualisation with tableau? A beginner’s guide

Posted on March 16, 2023April 5, 2024 by Imarticus Learning

Data visualisation is a powerful tool that helps businesses and individuals make sense of complex data. Tableau is one of the leading data visualisation software in the market today. It provides a user friendly interface that allows users to create interactive and visually appealing visualisations. Tableau is one of the most popular data visualisation tools, used by businesses, researchers and data analysts around the world. Read more to know everything beginners need to understand about data visualisation with Tableau.

What is data visualisation?

Data visualisation is the process of presenting data in a visual format, such as charts, graphs and maps. This allows people to easily understand and analyse the data. It helps to identify patterns and trends that may not be immediately obvious from raw data.

Why use Tableau for data visualisation?

Tableau is a powerful data visualisation tool that allows you to create interactive and dynamic visualisations. It is easy to use and provides a wide range of features and capabilities, making it a popular choice for businesses and researchers alike.

Getting started with Tableau

To get started with Tableau, you need to download and install the software on your computer. One can download a free trial from the Tableau website or purchase a license for the full version. Once installed, you can begin to explore the different features and tools that are available. The Tableau interface is designed to be intuitive and user friendly, with a drag and drop interface that makes it easy to create and modify visualisations.

Creating a basic visualisation

To create a basic visualisation in Tableau, you need to import some data. Tableau supports a wide range of data sources, including spreadsheets, databases and cloud services. Once you have imported your data, you can begin to create a visualisation. To do this, you will need to select the type of visualisation you want to create, such as a bar chart, line graph or scatter plot. Then, you can drag and drop data onto the visualisation and customise it to suit your needs.

Adding interactivity

One of the key features of Tableau is its ability to create interactive visualisations. This means that you can add filters, sliders and other controls that allow us to interact with the data and explore it in more detail. For example, you can create a visualisation that shows sales data for different regions and periods. You could then add filters that allow you to select specific regions or periods and see how the sales data changes in response.

Customising your visualisations

Tableau provides a wide range of customisation options that allow you to create visualisations that are tailored to your needs. We can customise everything from the colours and fonts used in our visualisations to the way that data is aggregated and displayed. For example, you might want to create a visualisation that shows the sales data for different products over time. You could customise the visualisation to show the data as a line graph with different colours for each product.

Sharing your visualisations

Once you have created a visualisation in Tableau, you can share it with others in a variety of ways. You can export the visualisation as an image or PDF file, embed it on a website or blog or share it as a Tableau workbook. Tableau also provides a range of sharing and collaboration tools that allow us to work with others on our visualisations.

Career as Data Scientist

We see a career as a Data Scientist as a rapidly growing profession. As businesses and organizations generate massive amounts of data, Data Scientists are increasingly in demand to analyze and interpret it to inform decision-making. They are responsible for collecting, cleaning and organizing data, using statistical and machine-learning techniques to extract insights and predictions.

A career in data science requires a strong foundation in mathematics, statistics, computer science and business as well as excellent communication and problem-solving skills. With a high demand for skilled Data Scientists across a wide range of industries, we see it as a promising career choice for those interested in working with data.

Certificate Program In Data Science And Machine Learning

Imarticus Learning is a platform that helps young professionals upgrade their skills. Here, students or young professionals can find several certificate courses. If you are looking for a career in Data Science then, you can enrol in Imarticus Learning’s Certificate Program In Data Science and Machine Learning. The program is designed to provide a comprehensive understanding of data science and machine learning concepts and tools. Enrol in Certificate Program In Data Science And Machine Learning today to launch your career as a Data Scientist!

Takeaway

We believe mastering data visualisation with Tableau is a valuable skill for any aspiring Data Scientist. By investing time and effort into learning Tableau, aspiring Data Scientists can enhance their data analysis skills, create more compelling presentations and stand out in a competitive job market.

How to learn machine learning – A complete guide for 2023

Posted on March 16, 2023April 2, 2024 by Imarticus Learning

Machine learning is a rapidly growing field that has become increasingly important in many industries in recent years. It involves the use of algorithms and statistical models to enable computers to learn from data and make predictions or decisions without being explicitly programmed to do so. Therefore, jobs in the field of machine learning and data analytics are continuously rising. If you want to enter this growing field and learn machine learning, then machine learning certification courses are one of the best ways to do so.

If you’re interested in building a career in data analytics in 2023, this guide will provide you with all the information you need to get started.

Here is how you can learn machine learning.

Understand the basics

Before diving into the world of machine learning, it’s important to have a good understanding of the basics. This includes understanding the different types of machine learning, such as supervised learning, unsupervised learning and reinforcement learning. This also includes algorithms and models used in each type.

Learn the programming languages

To learn machine learning, you’ll need to have a solid foundation in programming. Some of the most popular programming languages used in machine learning include Python, R and Java. It’s recommended to start with Python as it’s widely used and has a large community that offers plenty of resources and support.

Choose your tools

In addition to programming languages, you’ll also need to choose the right tools for your machine-learning projects. This includes frameworks like TensorFlow and PyTorch. This provides pre-built models and algorithms that you can use in your projects. Other tools like Jupyter Notebook, Pandas and NumPy are also essential for data manipulation and analysis. If you want to build a career in data analytics, then these tools can be really helpful.

Build your knowledge

To become proficient in machine learning, it’s important to build your knowledge through a combination of theory and practice. There are plenty of resources available online, including courses, books and tutorials. One of the best machine learning certification courses to get started is the Postgraduate Program in Data Analytics and Machine Learning.

Get hands-on experience

While learning the theory is important, getting hands-on experience is essential to becoming a skilled machine learning practitioner. This can be achieved through personal projects or by working on real-world problems. The Postgraduate Program in Data Analytics and Machine Learning by Imarticus focuses on practical knowledge and a job-oriented curriculum.

Join the community

Joining the machine learning community can help you learn faster and stay up-to-date with the latest developments in the field. There are plenty of online communities, including forums, slack groups and social media groups, that provide opportunities to connect with other machine learning practitioners and ask questions.

Keep learning

Machine learning as a concept is rapidly evolving. Hence, it’s critical to keep up with discoveries and updates in this field. This can be achieved through reading research papers, attending conferences and workshops and taking advanced courses.

Postgraduate program in data analytics and machine learning

Imarticus is a leading course platform that offers job-oriented certifications to help young professionals learn new skills like data analysis, machine learning and more. The Post Graduate Program in Data Science and Analytics is a 6-month job-oriented certification course offered to young professionals to help them learn machine learning and build a career in data analytics.

The course helps students build analytical models to solve real-world problems and improve business decisions. The machine learning certification course also offers a job interview guarantee that allows students to work with companies like Cognizant, IBM, Goldman Sachs, Standard Chartered and more. Following are the various benefits of completing the Post Graduate Program in Data Science and Analytics by Imarticus:

Young professionals are provided with a completion certificate by KPMG to boost their CVs.

The course also offers career services like interview workshops, mentorship, skill development workshops, career counselling, profile development and more.

The Post Graduate Program in Data Analytics and Machine Learning also provides students with a chance to participate in national-level competitions like KPMG India-organised hackathons.

Final thoughts

Learning machine learning requires a combination of theory, practice and community involvement. By following the steps outlined in this guide, you’ll be well on your way to becoming a skilled data analyst and machine learning practitioner in 2023. Enrol in the machine learning certification course by Imarticus to learn essential skills and take advantage of dedicated career services.

Remember to stay curious and keep learning as the field continues to evolve.

Which languages are used in python programming?

Posted on March 14, 2023April 2, 2024 by Imarticus Learning

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.

What is the importance of logistics in supply chain management?

Posted on March 13, 2023April 6, 2024 by Imarticus Learning

Why is logistics important in supply chain management?

Supply chain management is the management of all the processes involved in producing goods and services. The larger the chain is, the more complex all this gets. And this is where logistics comes into the picture. It’s the glue that holds it all together—making sure everything is delivered to the proper place at the proper time and in the proper condition.

But what exactly is logistics?

Logistics is everything involved in managing the resources of a project. A logistics manager ensures that each step of the way is complete and efficient. They ensure that resources are being used properly and play an active role in improving the production cycle.

Let’s explore some of their functions a bit more closely:

1. Customer satisfaction

Meeting client requests and ensuring the timely delivery of high-quality products are key logistics functions. This is especially important for companies where late delivery is a dealbreaker. Logistics managers can often act as the bridge to ensure everything is on track and customers are happy.

2. Cost savings

Lowering overall costs is also a really important logistics function. Streamlining supply chains, decreasing inventory costs, and improving transportation routes are great ways to cut costs. Another popular strategy is using technology and data analytics to improve the production process. This also helps the company increase its profit margin!

3. Inventory management

Inventory management involves the logistics of transporting products and raw materials from suppliers to producers and distributors. With effective supply chain management, you can improve your stock management processes and reduce inventory holding expenses.

4. Risk management

Logistics managers also recognize potential issues in the supply chain and put mitigation plans in place. This includes evaluating risks involved in handling, storing, and transporting commodities. It also means taking necessary action to reduce the effects of any supply chain disruptions.

Why logistics matters in today’s business

If you want your business to thrive in the global economy, you must pay attention to logistics. Investing in logistics gives your business a competitive edge and puts you on a path to success. Here’s why it matters for today’s global business:

1. You can scale up and expand

It paves the way for you to scale up your company’s operations across countries and regions. A robust logistics model will help you effectively manage the difficulties of cross-border trade, including customs rules, import/export procedures, compliance challenges, and other aspects of international trade.

2. Your business can be more sustainable

Logistics can also help your company be more sustainable. This can be done by setting up sustainable processes that lessen environmental impacts. Using energy-efficient transportation, cutting waste, and recycling products are all examples of sustainable logistics techniques.

Recent trends in supply chain management

As the business landscape becomes more global and technology continues to evolve, supply chain management grows as well. The field is actively responding to the world around it, and this trend-driven transformation will only continue.

Here are some recent trends in the supply chain management world:

1. Automation and digitalisation

Digital technologies like blockchain, artificial intelligence (AI), and the Internet of Things (IoT) are game changers in supply chain management. These tools offer real-time visibility and insights into operations, meaning you can quickly intervene and improve processes.

2. Sustainability

More and more companies are realizing the importance of sustainability and social responsibility. Through logistics, they take active steps to reduce their carbon footprint, recycle waste, and find ethical sourcing methods. These steps play an important role in improving the overall production process.

3. Risk management and resilience

Calamities like the COVID-19 pandemic and climate change highlight the need for supply networks that are flexible yet robust. In this new world, logistics managers have to design systems that account for business challenges, disasters, societal tensions, and other such circumstances.

Businesses that embrace innovation and adapt to these changes are more likely to prosper in a global market that is becoming more complex and competitive.

Sort out your logistics, keep learning, and transform your business

Setting up a logistics model is just the beginning. Many curveballs are coming your way as you set up processes that are perfect for you.

Here’s how you stay on top: constantly educate yourself. Keep up with trends in the industry and assess competitors’ logistics models.

For more in-depth knowledge, also consider taking an academic route and enrolling yourself in some courses. A great option is Imarticus Learning and IIM Raipur’s Supply Chain Management Course. It is a fantastic balance of academic instruction and hands-on activities.

We hope this short introduction has given you a headstart on starting your career in supply chain analytics and management. Follow Imarticus Learning for more insights on developing your professional skills.

Learning guide for data mining: 2023 guide

Posted on March 13, 2023April 6, 2024 by Imarticus Learning

Data mining is the process of discovering patterns and relationships in large datasets. It involves analysing data to find useful insights that can be used to improve business operations, predict future trends and make informed decisions. If you are new to data mining, here is your help learn what data mining is, how it works and its applications in various industries.

What is Data Mining?

Data mining is a process that involves analysing large datasets to discover patterns and relationships. It is a type of statistical analysis that is used to uncover hidden patterns in data that can help businesses make better decisions. The main goal of data mining is to identify patterns that are not immediately apparent by simply looking at the data. Data mining uses various techniques such as machine learning, clustering, classification, regression and association rule mining.

How does Data Mining work?

The process of data mining typically involves several stages:

Data collection

It is the first stage where data is gathered from various sources, such as databases, social media and customer feedback forms. Once the data is collected, it is stored in a data warehouse, where it can be easily accessed and analysed.

Data preprocessing

This is the second stage where the raw data is cleaned, transformed and formatted so that it can be used for analysis. This stage involves removing duplicates, handling missing data and ensuring that the data is in a consistent format.

Data modelling

This is the third stage where various algorithms are applied to the data to uncover patterns and relationships. This stage involves using techniques such as clustering, classification, regression and association rule mining.

Interpretation and evaluation

In the final stage, the insights gained from the analysis are interpreted and evaluated to determine their usefulness and relevance. This stage involves visualising the data in meaningful ways, such as graphs, charts and tables and using these visualizations to communicate the insights to the audience.

Applications of Data Mining

Data mining has many applications across different industries. In finance, data mining is used to detect fraud, predict customer behaviour and optimise investment strategies. In healthcare, data mining is used to analyse patient records, identify disease patterns and develop new treatments. In retail, data mining is used to analyse sales data, predict customer preferences and optimise pricing strategies. In marketing, data mining is used to analyse customer data, segment customers and personalise marketing campaigns.

Tools and technologies used in Data Mining

There are several tools and technologies used in data mining, including machine learning algorithms, data visualization software and big data analytics platforms. Machine learning algorithms are used to identify patterns and relationships in the data, while data visualisation software is used to create visualisations that can be used to communicate insights to decision makers. Big data analytics platforms are used to handle large datasets and perform complex analysis tasks.

A career in Data Mining

A career in data mining can be both challenging and rewarding. The role of a data mining professional can vary depending on the industry, but typically involves working with large amounts of data to identify patterns and trends and using this information to inform business decisions. This requires a strong background in statistics, mathematics and computer science, as well as proficiency in data analysis tools. With the increasing amount of data being generated by organisations across industries, there is a growing demand for skilled data mining professionals.

In terms of job opportunities, there are many companies that hire data mining professionals, including tech companies, financial institutions and consulting firms. Some job titles we believe young professionals may be interested in pursuing include data analyst, data scientist or machine learning engineer.

Learn Data Mining

You can learn Data Mining by taking programmes that can help give your career a boost. Several Institutes and learning platforms provide certification in data mining and machine learning. Young professionals can opt for IIT Roorkee Data Science Certification. We also suggest checking Imarticus Learning’s Machine Learning Certification Course.

Imarticus Learning is a leading platform for learning programmes for young professionals. Our Machine Learning Certification Course will definitely help you if you are interested in learning data mining. To launch your Data Mining Career, we suggest you enrol today!

Takeaway

To conclude, Data Mining is a rapidly growing field. With the right skills and training, we believe you can find excellent career opportunities. Pursuing a course in data mining can help one acquire the necessary skills and knowledge to succeed in this field. We suggest keeping up with the latest trends and technologies in data mining to remain competitive in the job market.

What is the process of sales and operation planning?

Posted on March 13, 2023April 5, 2024 by Imarticus Learning

Introduction

Sales and Operations Planning (S&OP) is a process to align the sales and operations activities of the organization to achieve strategic objectives. Operation planning in the supply chain involves a joint effort between sales, marketing, production and supply chain teams. This helps develop a unified plan to balance demand and supply.

For instance, a mobile phone-making company can use operation planning in the supply chain. It can, thereby, plan its production and sales activities. Here, the sales team provides a sales forecast based on historical data, market trends and customer demand. The production team uses this forecast to plan the production capacity, inventory levels and raw material needs. The supply chain team ensures that the required components and raw materials are available in time for production.

Benefits of Sales and Operations Planning

Sales and operations planning is a necessary process. Operation planning in the supply chain helps organisations to improve their performance. It also helps them to stay competitive in the market. Some of the benefits of S&OP are:

It allows organisations to develop a unified plan that balances demand and supply.

It involves different teams in the process. This helps improve forecast accuracy and reduce the risks due to demand and supply mismatches.

It helps to reduce lead times by aligning operations. It also enhances production and delivery times. This further helps in better customer service and customer satisfaction.

It helps to optimize inventory levels. It balances the demand and supply and reduces the risks of excess or shortage.

Stages of Sales and Operations Planning

Let’s take an example of a manufacturing company to understand the operation planning in the supply chain. Consider that a company produces bicycles. The different stages of the S&OP process here will be:

Data Gathering

The company gathers data on sales, production, inventory and supply chain activities for the last year.

Demand Planning

The company predicts the demand for bicycles for the upcoming year. This prediction is based on historical sales data and market trends.

Supply Planning

The company then plans its production capacity. This includes planning inventory levels and raw material requirements to meet the expected demand.

Pre-S&OP

Once the plan is ready, the company reviews the demand and supply plans. This is vital to make adjustments as per market changes and the company’s strategic objectives.

Executive S&OP

Once the plan is reviewed, the senior management further reviews the demand and supply plans. They make decisions on the production capacity to meet the expected demand.

Supply Chain S&OP

Finally, the supply chain team reviews the demand and supply plans. The team ensures that the required items and raw materials are available in time for production.

This is how the bicycle manufacturing company can optimise production to match the anticipated demand. This can help the company to:

Reduce inventory costs

Meet customer demands on time

Improve its financial performance

Implementing Sales and Operations Planning

The implementation of operation planning in the supply chain requires a standard approach. This involves planning, communication and collaboration across the organisation. Here are the critical steps in implementing S&OP:

S&OP requires the active support of senior management. This is to ensure that it is implemented effectively.

The implementation team should include members from different departments. They can be from sales, operations, finance and supply chain. This ensures easy and smooth functioning across departments.

Before execution, the scope of the S&OP process should be defined. It includes the time horizon, frequency and other relevant detail.

Accurate data is critical to the success of S&OP. Hence, the organisation needs to establish reliable data sources.

The process should focus on aligning sales and operations activities. This helps an organisation to achieve its strategic objectives.

Before implementing, it is ideal to train the team on the S&OP process along with the tools and techniques.

The S&OP process should be piloted. It is crucial to monitor and evaluate the process. This helps to test its potency and identify areas for improvement.

It is worth noting that S&OP is an ongoing process. Therefore, it needs to be evaluated regularly. After all, successful implementation of S&OP results in:

Improved forecast accuracy,

Reduced lead times,

Improved customer service levels,

Reduced inventory levels and

Enhanced financial execution.

Learn Supply Chain Management Course With Analytics

Whether you are a graduate or a working professional, you can learn supply chain management. From designing to managing supply chains, the Certified IIT Roorkee Supply Chain Management Course through Imarticus Learning, a learning platform for professionals, covers everything. Enrol today and create a better future.

What is deep learning and what does it do?

Posted on March 13, 2023April 4, 2024 by Imarticus Learning

Introduction about how Deep Learning differs from traditional Machine Learning

Deep learning is a subset of machine learning in which artificial neural networks, algorithms inspired by the brain, learn from large amounts of data. Deep learning is used to recognize patterns in data, such as images or spoken words, and make predictions based on those patterns.

Traditional machine learning algorithms are designed to work with a small amount of data and require humans to hand-label that data. Deep learning algorithms can automatically label data by finding patterns in the data itself. This allows deep learning to work with much larger datasets and achieve better results than traditional machine learning.

Key concepts and components of Deep Learning:

Deep learning is a subset of machine learning that uses algorithms to model high-level abstractions in data. In deep learning, a set of algorithms attempt to learn representations of data that are more complex than the input data.

The key concepts and components of deep learning are:

Algorithms: Deep learning algorithms are designed to learn high-level data representations. These algorithms are usually based on neural networks composed of many interconnected processing nodes or neurons.
Neural networks: Neural networks are the building blocks of deep learning architectures. They are composed of many interconnected processing nodes or neurons that can learn to recognize input data patterns.
Representations: Deep learning algorithms learn complex representations of data that are more informative than the raw input data. These representations can be used for classification, prediction, and estimation tasks.

Types of Deep Neural Networks used in Deep Learning and Applications of Deep Learning.

Many different types of neural networks are used in deep learning, each with advantages and applications. The most popular types of neural networks used in deep understanding are convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

CNNs are well-suited for image classification and recognition tasks, as they can learn to extract features from images and identify patterns. RNNs are good at sequential tasks such as text generation and machine translation, as they can remember information from previous inputs.

Deep learning can be applied to many tasks, such as image classification, object detection, speech recognition, machine translation, and natural language processing.

Why Make a career in Deep Learning and Data Analytics?

There are many reasons to make a career in deep learning and data analytics. With the ever-growing amount of data being generated, the need for experts who can analyze and make sense of it will only increase. Deep learning and data analytics offer a unique set of skills that can be used to uncover hidden patterns and trends, which can then be used to make better decisions or predictions.

The ability to work with large amounts of data and extract useful information from it is highly valued in today’s business world. Deep learning and data analytics provide the tools and techniques to do this effectively. As more organizations strive to become data-driven, the demand for deep learning and data analytics experts will only grow.

So, if you’re looking for a challenging and rewarding career that offers the opportunity to make a real impact, deep learning, and data analytics is worth considering.

Learn and Grow with Imarticus Learning:

Imarticus Learning is offering a job interview guarantee program for recent graduates and professionals who want to develop a successful data science and analytics career. This program will take you through the practical aspects of data science and analytics, allowing you to gain practical knowledge about the implications of data science in real-world businesses, preparing you to work as a data science professional in an emerging field of data science and analytics.

During the data analytics course, you will build a data science project from scratch, applying the learnings from our boot camps. This is how you learn how to plan and implement a project successfully, get valuable feedback from the evaluators, and add it to your GitHub portfolio.

We offer various career services to help you find the right job. Our resume development, profile enhancement, mentorship, and interview preparation workshops are designed to help you land your dream job.

Professional Scope: What Can I Become after the Data Analytics certification course?

Data Scientist
Data Analyst
Business Analyst
Business Intelligence Specialist
Business Analytics Professional
Analytics Manager
Data Science Consultant
Machine Learning Engineer

Course USPs:

Job interview Guarantee
Job-Specific Curriculum
Live Learning Module
Real-world Projects
Dedicated Career Services
KPMG India COE Organised Hackathons

What is a supply chain design and how does it work?

Posted on March 11, 2023March 14, 2023 by Imarticus Learning

A supply chain design refers to creating and managing a system of operations that delivers products or services to customers. It involves almost everything from the supplier to the end customer. Professionals who become supply chain analysts can improve efficiency, reduce costs and enhance customer satisfaction. That’s why leading companies prefer professionals having supply chain management courses with analytics.

Suppose a coffee company designs its supply chain. It will start by sourcing high-quality coffee beans from farmers. The company works closely with its suppliers to ensure the beans are produced and stored sustainably. Once harvested, beans are transported for roasting to create the desired flavour profile. Beans are then packaged and distributed to coffee shops and retail partners.

Stages of a Supply Chain

Let’s take another example of a clothing company to understand different stages.

Planning

The clothing company plans its supply chain by forecasting the demand and creating a production plan. This includes the quantity needed and the production schedule for each item.

Sourcing

The company then sources raw materials, such as fabric and thread, from suppliers. Moreover, it negotiates prices, establishes contracts and manages supplier relationships.

Production

The raw materials are then transformed into finished products in a production facility. It can be shirts, pants, jeans, or any other piece of clothing. This stage includes –

Managing the production process

Monitoring quality control

Ensuring that production schedules are met

Inventory management

Once produced, the inventory is stored and managed in the warehouse. This includes –

Monitoring inventory levels,

Tracking shipments and

Ensuring timely delivery

Transportation

The finished products are transported to stores using various transportation modes. This stage involves –

Managing logistics,

Identifying transportation modes and

Tracking shipments.

Retail

Once the products reach the stores, they are available for customers to buy. This stage includes –

Managing distribution channels,

Coordinating with retailers and

Ensuring timely delivery in a cost-effective manner

Thus, by managing each stage, the clothing company ensures high-quality products with cost and resource optimisation.

How Supply Chain Design Works

The working of supply chain design is quite simple. Here is how it works –

The first step is to define the objective of the supply chain design. It could be for cost optimisation, to improve customer service or to reduce lead times.

The next step is to collect data on the current supply chain. Data can be collected from suppliers, transport agents and inventory management teams.

Once the data is collected, it is analysed to identify potential areas for improvement.

Based on the analysis, an improved design is developed. The new supply chain design aligns with the objective.

The new model is then tested to see how it performs under different conditions. For instance, changes in demand or supply disruptions.

Once functional, it is then implemented. But it can include some modifications as well if needed.

The final step is to track the success of the new supply chain design. In case of deviations, adjustments are made to meet the objectives.

Suppose a consumer electronics company wants to optimise the cost. So, the company uses supply chain design to identify –

The ideal number of warehouses,

Location of warehouses,

Production facilities and

Transportation routes

The company will collect and analyse data based on the current supply chain design. Based on the analysis, a new design will be proposed and tested for different scenarios. Once the new design meets the expectation, it is implemented. Finally, the new supply chain design will be monitored to ensure it meets the cost optimisation objectives. But, in case of a discrepancy, it will be adjusted as per the goal.

Why should a company understand its supply chain?

Understanding the supply chain is important for a company for several reasons. These are –

It allows the company to identify areas where efficiency can be improved. This ultimately leads to saving costs.

The company can identify potential bottlenecks. It helps in planning steps to address them to ensure production runs smoothly.

It also helps the company to manage risk. The company can mitigate risks with supply disruptions or quality issues by knowing the supply chain.

It also helps the company to meet product demands more effectively. This eventually helps in –

Managing customer expectations,

Enhancing lead times and

Focusing on quality and price

Make your supply chain design and become a supply chain analyst

Whether you are just a graduate or a working professional, everyone can learn to create supply chain designs. From creating supply chain design to managing supply chain uncertainty, this Certified IIT Roorkee Supply Chain Analytics Course through Imarticus Learning, a learning platform for professionals, covers everything. Enrol today and become a supply chain analyst tomorrow.

SQL for Data Science: Why Is It Important?

Posted on March 10, 2023January 20, 2024 by Imarticus Learning

Learning SQL or Standard Query Language is a mandate for anyone looking to build a career in data science. It is used for interacting with and extracting data from relational databases. Most modern systems today capture data stored in one or multiple databases like Oracle, MySQL, SQL Server and Redshift. Hence, it is important to have an in-depth understanding of SQL to glean data from these systems and use them efficiently.

Apart from writing queries and handling data, it aids in communicating with people, visualising results and building models. It is also an essential element in machine learning. Despite being a powerful tool, it is easy to learn, easily shareable, familiar and relevant worldwide.

What is SQL?

SQL or Standard Query Language is a declarative language for controlling and acquiring data. Data scientists use it to develop, decipher, control (insert, update, delete) and combine tables. Furthermore, it is used for filtered results with ORDER BY statements, WHERE clauses and the like.

SQL helps data scientists access data and work directly with a database without using a different programming language. It makes running complex queries easier because one can do so with SQL syntax and without writing code, making extracting anything from a database easy.

5 Reasons Why SQL is Important in the Field of Data Science

SQL is essential for Relational Database Management which plays a major part in data science. Here are 5 main reasons why SQL is important in data science:

1. It is a powerful language

SQL programming is used for manipulating data, creating new tables, inserting data into tables and retrieving results of queries. SQL syntax is similar to the SQL programming language, which makes it easy to learn. Developers familiar with Standard Query Language also find it easier to learn Python objects and programming. With SQL, it is possible to:-

Query the database and acquire results comprehensibly without manually going through every row with tools like R scripts or Excel.
Quickly acquire the necessary answers you need without the need to write code or try multiple algorithms.

2. It is globally recognised

Being familiar with data science tools like R, Spark and Python makes it easier to learn SQL. More importantly, it is a mandatory skill recognised globally to help manipulate and interact with data stored in databases. Knowing how to write queries in SQL can be used in all database applications and tools without any in-depth knowledge of statistics.

3. SQL is sharable

SQL is also widely used for sharing data and helping data scientists communicate with other non-technical members of an organisation who might require the same information. For instance, if the marketing team of a company requires understandable information from a raw dataset, then it is the duty of the data scientist to glean, process, clean and provide it. This helps enhance flexibility and work efficiency among teams.

4. It is a common tool

Data experts and business users widely use SQL for querying databases like data lakes and warehouses. Aside from being another tool that helps access Spark and Hadoop, it is also used by primary data analysis tools like Tableau to query relational databases.

5. It is relevant

SQL is commonly used in multiple data science tasks like:-

Exploring data and understanding it better
Cleaning up data
Prepare data for analysis
Building models on the prepared data set
Visualising results and reporting on them.

Why is Learning SQL a Mandate for Becoming a Data Scientist?

We have consolidated a list of reasons why learning SQL is a mandate for pursuing a career in data science:-

It helps handle structured data: SQL is required to work with structured data stowed in relational databases and raise a query in said databases.
Big data platforms provide useful extensions: Platforms like Hadoop offer extensions for raising SQL command queries to manipulate data efficiently in HiveQL.
It helps experiment with data: SQL is a standard tool that provides data scientists with the opportunity to experiment with data by creating test environments.
It helps in analysing data: SQL skills are integral in data analytics. It helps work with data stored in relational databases such as MySQL, Microsoft SQL and Oracle.
Helps in preparing data: SQL helps in data wrangling, which removes errors and combines complicated datasets. It is essential for working with numerous big data tools because it aids in preparing data and making it more accessible.

Conclusion

SQL skills are mandatory for data scientists. It helps comprehend data efficiently to facilitate effective decision-making. Therefore, a career in data science is the most lucrative choice you can opt for because of the golden opportunities that lie in its wake.

Large corporations are constantly looking for data scientists who can glean and analyse data from large data sets to help make better decisions that will facilitate the ultimate growth of an organisation. To upskill yourself and further your career in this field, you can sign up for the comprehensive Postgraduate Program in Data Science and Analytics offered by Imarticus Learning.

A Beginner’s Guide to learn Hadoop Online in 2023

Posted on March 10, 2023April 6, 2024 by Imarticus Learning

The popularity of Hadoop among big companies is growing immensely. The market of Hadoop has also witnessed the largest transition in recent years. This transition will have no downfall anytime soon. Hadoop allows companies to store data, make financial plans, track down information and formulate personalised recommendations.

You can learn Hadoop online to grab all the lucrative opportunities this field offers. An online course will help you change your career easily.

What is Hadoop?

Apache had created Hadoop, an open-source framework. It can easily process and store big-volume data. The entire framework has been coded with the assistance of JavaScript. Hadoop is unique as it can be amplified by just adding more nodes to its clusters. Big companies like LinkedIn, Meta, and Twitter are already using Hadoop.

What are the four fundamental modules of Hadoop?

Hadoop consists of four fundamental modules. Let’s learn more about these modules:

Hadoop Distributed File System (HDFS)

HDFS generally uses low–end or standard hardware and is more efficient than traditional systems. It can store big data better than the traditional system. Besides, HDFS also has an outstanding fault-tolerating system.

Yarn

The full form of Yarn is Yet Another Resource Negotiator. The main task of this is to regulate and supervise various clusters and nodes. It also organises tasks.

Map Reduce

This is a framework designed to allow programs to compute big data in parallel. The Map Reduce framework converts the input data to evaluate it using the Key value pair.

Hadoop Common

Hadoop Common is the main JavaScript library. This is mainly used to launch the Hadoop framework and other modules.

Advantages of using Hadoop

Large companies are opting for Hadoop because of the various advantages it offers. It has escalated the revenue of many large companies. Here we have enlisted a few benefits of Hadoop:

Scalable: Hadoop can be upgraded at any point by adding more modules to its clusters.

Rapid: The traditional systems can not distribute data among clusters as fast as Hadoop can. Most of the data analysis tools of Hadoop are situated on the same servers. Therefore, the processing time is reduced significantly. Due to Hadoop, terabytes and petabytes of data can be processed within a few minutes and hours, respectively.

Economical: Hadoop is more economical than traditional systems as it is an open source and usually stores big data in commodity hardware.

Failure Resistant: Hadoop can resist data failure easily. It has a feature which enables it to copy and replicate data. This replicated data is used when any network collapses. Data in Hadoop can be copied thrice.

Various tools from Hadoop Ecosystem

The Hadoop ecosystem consists of various tools as well as applications. These tools are used to store, manage and analyse huge data. Here is a list of a few Hadoop tools:

Spark- An open-source tool, it is mainly used for huge data loads. It also encompasses within itself batch processing, graph database, streaming analytics and Machine Learning.

Hive- This tool assists the users in using Hadoop along with SQL. On a massive scale, Hive can enable and distribute data analytics. It can also work on the fault tolerance feature of Hadoop.

HBase- This non-relational open source runs on Hadoop Distributed File System (HDFS). HBase is a Hadoop tool that can be upgraded at any given point. Apart from that, it can circulate huge data among various in-built stores. It also has access to rows and columns in real-time.

Presto- This is an open-source tool which supports the ANSI SQL. Presto also includes complicated inquiries as well as window functions. It can work on more than one data source that includes HDFS as well.

Zeppelin- This tool is a type of notebook that permits interactive data.

What jobs are available as a Hadoop professional?

There is a huge demand for Hadoop professionals as more companies are adopting it. Therefore, companies are offering lucrative salaries so that individuals can change their career options to become Hadoop specialists. Here is a list of job roles of a Hadoop Professional:

Hadoop Admin
Data Engineer
Data Scientist
Software Engineer
Big Data Architect
Data Analyst
Hadoop Developer

Conclusion

If you are wondering how to learn Hadoop, then enrol yourself on the online course by Imarticus Learning. This course is handled by professionals who arm you with the requisite knowledge and skills. The data analytics course will help you grasp the nitty-gritty of Hadoop and open doors for lucrative job opportunities.