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Category: Management

What is deep learning and what does it do?

Posted on 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:

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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:

  1. 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.
  2. 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.
  3. 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 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 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. 

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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 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?

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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. 

7 ways through which you can learn full stack web development

Posted on by Imarticus Learning

A full-stack developer is someone who knows all aspects of a project. They can design, build and market everything from scratch. A full-stack developer can also work on multiple projects by dividing their responsibilities. This post will discuss seven ways to learn full-stack web development.

What is a Full-Stack Developer?

full stack developer course

A web programmer who builds websites and web applications from the ground up is known as a full-stack developer. These experts can specialize in both back-end and front-end web development, and to create a complete website, they need to be knowledgeable in programming tools and languages. 

Front-end development deals with creating a website’s aesthetically pleasing and usable elements, whereas back-end development deals with creating the databases and supporting infrastructure for a website.

How To Become A Full-Stack Developer?

  • Online Courses

An online course is the best way to learn full-stack web development. They allow you to learn new skills quickly. The best part about online courses is that they can be completed on your own time–no need for travel or a classroom setting! 

  • Bootcamps

Bootcamps are a great way to learn full-stack web development. They’re also a great way to learn to program quickly, and they can give you what you need to enter the industry or change your career path. Students will have access to knowledge and mentorship from experts in their field who have already built successful businesses. 

This allows you to get up-to-speed quickly on all things related to coding and gives you confidence and connections with people who can help you move forward once graduation has finished!

  • Study the basic programming languages.

Depending on the project’s needs, a full-stack developer may use different programming languages like JavaScript, SQL, and Python. You can build websites using a variety of patterns and concepts if you are familiar with these front-end applications. You can lay a solid foundation by being proficient in languages like HTML and CSS. You can learn these languages by registering for a beginner’s course, building programs using templates, and watching instructional videos on social media websites.

  • Self Learning

Self-learning is a good way to learn, and it’s also a great way to stay up-to-date on new technologies and languages. You can learn all of this on your own time!

  • Networking and Collaboration

The most important way to learn full-stack web development is by networking with other developers and getting their feedback on your work. You can also collaborate with other developers and try solving problems together. This will help you improve your skills as a developer because understanding others’ points of view is a significant aspect of being a good programmer.

  • Practice and Consistency

As you move along in your learning process, it’s important that each time you learn something new, you try to apply it as well. It’s easy for people who haven’t learned anything about the field to think that their skills are complete if they’ve learned how to build websites using HTML and CSS. 

But this isn’t true–there is no such thing as a full-stack developer! You have to keep learning new things so that when someone asks, “How do I solve this problem?” or “What framework should I use?” or even “What programming language should I learn next?” You can confidently answer those questions because now you know all about these topics.

  • Continual Learning and Growth

Continual learning is the key to success in any industry. It’s important for you to always be on the lookout for new opportunities and learn from them so that you can adapt to new situations or challenges. To keep growing as a developer, it’s also necessary that you learn from your mistakes–and other people’s mistakes as well! 

Learn data structures and algorithms with Imarticus Learning.

Our Full Stack web Development certification course will teach students database architecture and algorithms. During this six-month full stack developer online course, students will learn data structure algorithms and the technical facets of front-end and back-end programming.

Course Benefits for Learners:

  • As part of our career services, we offer resume writing, profile improvement, workshops to help students prepare for interviews, and one-on-one career counseling. 
  • While learning well-known tools like Java, Spring, MongoDB, JavaScript, React, Docker, and Jenkins,  lay a strong foundation in data structures. 
  • With the help of our community project Skillenza, students can now compete in coding challenges to solve complex business problems and stand out on resumes.

Top 8 data science roles in the field of data science

Posted on by Imarticus Learning

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

Big Data Analytics Course

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

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

Data scientist

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

Data Analyst

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

Machine Learning Engineer

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

Data Engineer

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

Business Intelligence Analyst

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

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

Data Architect

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

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

Data Visualization Engineer

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

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

Statistician 

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

Explore your Career in Data Science with Imarticus Learning!

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

Course Benefits for Students:

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

Visit our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad, or contact us via the chat support system.

How machine learning and analytics have evolved as a career?

Posted on by Imarticus Learning

Data science is the complete range of activities that encompasses artificial intelligence, machine learning, and deep learning. It applies mathematics, statistics and linear algebra to create algorithms that solve diverse business and operation issues in multinational organizations and start-ups alike.

Artificial intelligence is the ultimate goal to be achieved. The very basic purpose of it is to make machines think and act like humans. Artificial intelligence is achieved through machine learning and deep learning techniques. In present times, a career in data analytics and machine learning is seeing an upward trend. 

The Concept of Data Analytics

The foundation of data science is plenty of historical data. Data may be gathered from various sources. Sometimes the organizations provide their own past data along with the data of their competition, if available. Sometimes, the analyst has to gather the data from several resources such as websites and relevant social media or e-commerce platforms. These collected data are raw and need to be cleaned, filtered and segregated. The job of the analyst covers all these activities and then applying proper algorithms to the same.

The knowledge of a programming language like Python or R is essential at this stage. While Python has its own set of algorithms that may be directly applied and thus recommended for beginners, R is an advanced language which will enable the analyst to create his or her own algorithms to extract meaningful insights from the data. When all these activities are complete, the analyst then applies visualization tools like Power BI or Tableau to transform these data into easily understandable pie and bar charts. The sole purpose of all these activities is to enable the management to take important business decisions regarding its products, services and much more.

The Concept of Machine Learning

machine learning course

When we read a machine to respond to situations in a way that a human would have done under similar circumstances, we achieve the purpose of machine learning. Machine learning is generally of three main types – supervised learning, unsupervised learning and semi-supervised learning. 

  • Supervised learning is the process of feeding labelled data as inputs so that the machine may respond to similar situations as per the input conditions. The inputs may be text, images, videos etc. 
  • Unsupervised learning is the case where there will not be any labelled data, but the machine will be programmed to read and draw useful insights from the data they get. This technique is used in clustering group data. 
  • Semi-supervised learning is a mixture of the above two. Deep learning is an advanced form of machine learning where the machine is made to mimic a human brain. 

It is universally true that humans learn from the pages of history. History consists of past data. In earlier days, the quantity of this data was small, and it could be easily managed over manual accounting or, at a later stage, over a simple Excel sheet. Business and Operation Managers made the best use of these historical data to make future decisions. However, with the passage of time, the volume of data has changed, and so has the method of record keeping and analysing. Start-ups and big companies alike need data to predict their next business moves. They would like to know which products and services would remain relevant in business and which ones will fade out. They would also like to know the potential a particular business will have in the next financial year or further ahead. This demand has evolved analytics as a key career subject with the present-day young job searchers.

Similarly, machine learning also has its own application domain. We are privileged to the benefits of robotics. Machine learning has other applications in different services. For instance, a reputed spectacles merchant often uses this technique to enable its customers to understand which frame would best fit their face contour. A user of a social site is often recommended as per his or her earlier choices. 

Course Details of Machine Learning And Data Analytics

The contents of the Data Analyst training course are very similar to those that are covered during Machine Learning as well. The courses are available in both online and offline modes. However, it is important for an aspiring candidate to join a reputed institute with credibility among employers. Furthermore, students should choose those courses which give them ample opportunity to enhance their practical experience with projects. The following topics are generally covered in data analytics certification courses –  

  • Advanced Microsoft Excel, basic mathematics, statistics, and linear algebra. 
  • Data analysis and project cycle life.
  • Techniques of evaluation, exploration, and experimentation.
  • Segment analysis using clustering and method of prediction.  
  • Data visualization with Tableau or Power BI.
  • Analytics and recommender systems. 

Both of these subjects have evolved as very demanding careers amongst the present job-seekers. A prospective candidate can learn data analytics from the postgraduate program in data science and analytics course taught at Imarticus. The duration of the course is 6 months. This course will help you achieve your dream and establish a career in sync with present requirements. 

10 best tools that lead machine learning projects

Posted on by Imarticus Learning

The world of machine learning is always expanding and changing. As such, there are many tools to aid you in your quest for knowledge. 

Most likely, you already have some knowledge of machine learning and its potential to revolutionize industries. But when it comes down to building a successful project, there’s no escaping hard work, expertise—and picking the right tools.

Data Science Course

The size of the machine learning market has been rising steadily. The deep learning software category, expected to reach almost $1 billion by 2025, is the most significant subsegment of this market. According to recent machine learning market research, the demand for AI-enabled hardware and personal assistants is anticipated to grow rapidly.

The following list offers 10 of the best tools for machine learning projects. The selection is based on their usefulness and versatility in various contexts, including training models, deploying them at scale and analyzing data.

TensorFlow

Google Brain’s engineers and researchers initially created an open-source machine learning framework called TensorFlow. The library was initially created for ML and deep neural network research. 

Sklearn

One of Python’s most well-liked and reliable tools for carrying out machine learning-related tasks is sklearn (also known as scikit-learn), first created by David Cournapeau in the 2007 Google Summer of Code (GSoC) program. 

Shogun

Shogun is an open-source machine-learning framework built on C++. It offers a broad range of complete machine-learning algorithms that are both efficient and optimized. Support vector machines are among the kernel machines in Shogun that are used to address regression and classification problems.

Colaboratory

Google Colab, also known as Colaboratory by Google, is a free cloud computing platform for data science and machine learning. It eliminates any physical restrictions that might exist when using machine learning models. Run complex models and algorithms. 

Weka

Weka (Waikato Environment for Knowledge Analysis) is an open-source toolkit that can be used to create machine learning models and use them in practical data mining scenarios. It is available under the GNU GPL (General Public License) and includes tools for data preprocessing, the implementation of numerous ML algorithms, and visualization.

IBM Cloud

More than 170 products and cloud computing tools comprise the entire IBM cloud services stack for business-to-business (B2B) organizations. Like many other all-encompassing cloud computing services like AWS, Microsoft Azure, and Google Cloud, IBM Cloud includes all three of the primary service models (or varieties) of cloud computing. 

Google ML kit for Mobile

Google offers the ML Kit to mobile app developers with machine learning know-how and technology to build more reliable, optimized, customized apps. This toolkit can also be used for barcode scanning, landmark detection, face detection, and text recognition applications. It can also be used for offline work.

Apache Mahout

The Apache Software Foundation’s open-source project Apache Mahout is used to creating machine learning programs primarily focusing on linear algebra. With its distributed linear algebra framework and mathematically expressive Scala DSL, programmers can quickly implement their algorithms. 

Amazon Web Services

Amazon Web Services has a wide range of machine learning services. For companies and software engineers, AWS offers a wide range of tools and solutions that can be used in server farms across more than 190 nations. Government agencies, educational institutions, NGOs, and companies can all use the services. The end-users needs can be taken into account when tailoring its services.

Oryx2

Built on Apache Kafka and Apache Spark, it is a realization of the lambda architecture. For large-scale, real-time machine learning projects, it is frequently used. It also serves as a framework for creating apps, including complete packages for filtering, regression analysis, classification, and clustering. 

Learn Data Science and machine learning with Imarticus Learning. 

 Do you want to improve your machine-learning abilities? Certificate Program in Data Science and Machine Learning from IIT Roorkee is now available!

Start your journey with iHUB Divya Sampark from IIT Roorkee! As you build on the fundamentals, our esteemed faculty members will instruct you on crucial ideas like mining tools and how to apply insights to create practical solutions using Python programming.

 Course Benefits For Learners:

  • In this IIT Roorkee machine learning certification course, learn from renowned IIT faculty and gain a fascinating perspective on India’s thriving industry.
  • You will have the advantage you need to advance your career in the data science field with the help of our data scientist careers.
  • Learn the fundamentals of AI, data science, and machine learning to build skills that will be useful in the present and the future.
  • With the help of our IIT Roorkee data science online course, you can give yourself a career edge by learning about cutting-edge technology that will lead to amazing opportunities.

How SQL and Excel are right for tableau

Posted on by Imarticus Learning

SQL and Excel are powerful tools that complement Tableau’s data visualization capabilities. Tableau is a data visualization software or application that allows users to connect to and visualize data from various sources. You can integrate SQL and Excel with Tableau to enhance functionality and allow users to work efficiently with data. 

Reasons behind the growing hype of Data Visualization:

  • Data visualization tools and techniques allow for the quick and easy interpretation of complex data sets, helping businesses and individuals to identify trends, patterns, and outliers that you might otherwise miss.
  • Visualization tools have become more user-friendly, and cloud-based platforms have made it easier to share and collaborate on data visualization projects.
  • Infographics, charts, and interactive visualizations are more likely to be shared and engaged than traditional data reports or spreadsheets.
  • Finally, data visualization is an effective way to communicate data-driven insights to a broad audience. By presenting data visually appealing and engagingly, businesses and individuals can share complex information in a way that is easy to understand and memorable.

Here are eight points that explain how SQL and Excel are right for Tableau:

  • Data Preparation: Excel and SQL can help prepare data before feeding data into Tableau. Excel is an excellent tool for cleaning up and formatting data, such as removing duplicates or fixing formatting issues. SQL can filter and sort data and perform calculations before importing it into Tableau.
  • Data Connection: Both Excel and SQL can be used to connect to Tableau data sources. Excel can import data from various sources, such as spreadsheets, databases, or other files, which you can use in Tableau. Similarly, SQL can connect to multiple databases, and you can use SQL queries in Tableau.
  • Data Aggregation: SQL is a powerful data aggregation tool, allowing Tableau to work with large datasets more efficiently. SQL queries can summarize and group data, making it easier for Tableau to work with large datasets.
  • Data Blending: Tableau’s data blending feature allows users to combine data from multiple sources. Excel can blend data from multiple spreadsheets, while SQL can integrate data from various databases. This feature enables Tableau to create more complex visualizations incorporating data from different sources.
  • Data Analysis: Excel is a popular tool for data analysis and can be used in conjunction with Tableau. For example, Excel can calculate statistics or perform regression analysis, which can then be used in Tableau. SQL can also be used for data analysis, such as computing averages or creating complex queries you can visualize in Tableau.
  • Data Exploration: Tableau is designed to help users explore data visually. However, SQL and Excel can also explore data before visualizing it in Tableau. You can use SQL to query databases and explore data in a more structured way. You can use Excel to create pivot tables, which can help users explore data quickly and efficiently.
  • Data Validation: Excel and SQL can validate data before visualizing it in Tableau. You can use Excel to perform data validation checks, such as checking for missing data or identifying outliers. SQL can also validate data by checking for duplicates or identifying inconsistencies.
  • Data Maintenance: Excel and SQL can be used to maintain data integrity over time. Excel can create templates or dashboards updated regularly with new data. You can use SQL to create stored procedures that you can use to edit or delete data in a database. This functionality allows Tableau to work with more up-to-date and accurate data.

SQL and Excel are powerful tools that can enhance Tableau’s data visualization capabilities. You can use them for data preparation, connection, aggregation, blending, analysis, exploration, validation, and maintenance. By leveraging these tools, users can work with data more efficiently and create more sophisticated visualizations. 

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best big data analytics course

This Data Analytics Certification Course promises to help you realize the full potential of data analysis and create models with the most significant business impact possible. Whether you are a recent graduate seeking to make a career in data analytics and machine learning or an experienced professional looking to advance your career in analytics, this program provides a practical approach to understanding how analytics can drive actual results in a business setting.

Course Benefits for Students:

  • Students can benefit from data analyst training while staying in the comfort of their homes.
  • Learn by doing with Data Analytics codes.
  • Students who complete the PG in Data Analytics Certification Course will have a thorough theoretical and practical understanding of data analytics.

How life-changing lessons can be learned with deep learning

Posted on by Imarticus Learning

Are you prepared to witness the power of algorithms that can interpret massive amounts of data, identify patterns, and generate predictions that can resolve complex issues? In that case, you’ve found the right place. In this post, we’ll examine the mind-blowing potential of deep learning and how it might completely alter how we live and work.

Think of it this way. Deep learning algorithms are like a superpower for the 21st century, one that can turn big data into actionable insights, drive innovation, and improve the quality of life—the secret ingredient powers cutting-edge technologies like self-driving cars, virtual assistants, and predictive analytics. 

The potential of deep learning is vast and limitless, and it’s only a matter of time before it becomes an integral part of our daily lives. As we explore the incredible power of deep learning algorithms, buckle up and get ready to be inspired. The future is now, and deep learning is leading the way!

The power of deep learning algorithms

Welcome to the exciting and transformational world of deep learning! Imagine a world where computers can quickly process enormous amounts of data, identify intricate patterns, and make incredibly accurate predictions.

A world where artificial intelligence has the power to revolutionize industries, solve difficult problems, and enhance the quality of life for people all over the world. This world is not a distant future but a reality that we are living in today, thanks to the power of deep learning algorithms.

Artificial Intelligence and machine learning course

Deep learning algorithms are the backbone of artificial intelligence and are responsible for creating intelligent systems that can perform tasks that were once only possible for humans. These algorithms use a combination of mathematical models, data, and powerful computing power to learn, adapt and make predictions. The result is an algorithm that can process vast amounts of information, recognize patterns, and make accurate predictions that surpasses human capabilities.

The power of deep learning algorithms is immense, and the possibilities are endless. This technology is changing the world as we know it, and it’s up to us to harness its power for the greater good. So, embrace the future and embrace the power of deep learning algorithms. 

Personal growth through deep learning

Deep learning has the potential to impact personal growth in a profound and meaningful way. 

  • Improving critical thinking and problem-solving skills: Deep learning algorithms require individuals to think critically and solve complex problems. The process of training algorithms and making predictions requires individuals to think outside the box and come up with creative solutions. This, in turn, helps individuals develop better critical thinking and problem-solving skills.
  • Developing a growth mindset: Deep learning encourages individuals to embrace challenges and view failures as opportunities for growth. Learning and improving algorithms requires individuals to experiment, make mistakes, and learn from their failures. This fosters a growth mindset, where individuals see challenges as opportunities to grow and improve.
  • Enhancing creativity and innovation: Deep learning algorithms can process vast amounts of data, recognize patterns, and make predictions. This process encourages individuals to think creatively and come up with innovative solutions. Individuals can enhance their creativity and foster innovation by exploring new ideas and approaches.

Deep learning has the potential to help individuals grow and develop in ways that are essential for personal and professional success. By improving critical thinking and problem-solving skills, developing a growth mindset, and enhancing creativity and innovation, deep learning can help individuals reach their full potential and achieve their goals.

Discover IIT artificial intelligence and machine learning course with Imarticus Learning.

Are you prepared to advance your career in a new tech-focused field? Enroll at the E & ICT Academy for their comprehensive IIT AI ML course! You can get ready for the roles of Data Scientist, Data Analyst, Machine Learning Engineer, and AI Engineer with this intensive 9-month program. 

Through real-world projects from various industries, you will develop a solid foundation in data science concepts and work with industry experts to learn how to apply machine learning, deep learning, and AI techniques practically. 

Course Benefits For Learners:

  • Students work on 25 real-world projects to develop real-world business experience and prepare for a rewarding data science career.
  • With a deep learning certification recognized by the E & ICT Academy, IIT Guwahati, and a credential supported by Imarticus Learning, students can attract employers’ attention and demonstrate their skills.
  • Students who complete this IIT artificial intelligence course land lucrative jobs in the machine learning and artificial intelligence sectors.

Visit our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad, or get in touch via the chat support system.

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