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Category: Management

Deep learning: Why it matters for the future

Posted on by Imarticus Learning

The leaps and bounds that technology has taken since artificial intelligence’s inception in the 1950s are impressive. And it happened in the absence of the internet. Experts in the period between 1957 to 1974 derived the proof of principle for AI. They visualised a system that could recognise spoken language with the help of data processing. This technology has grown exponentially to reach the era of “big data.” This is where a large amount of data is processed in minutes. The future of AI looks promising. From translating languages in real-time to self-driven cars, it’s everywhere. A PG in Data Analytics helps you start a successful career in this promising field.

The following section will help you explore the scope of Deep Learning in the future.

Importance of Deep Learning for the Future through Examples

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Almost all companies in the world are leveraging AI and ML-based machines. So, knowing how this technology is being used is important. The examples listed below discuss Deep Learning implementation in real-world scenarios. We also explore the scope of future improvements in DL-based technologies. This will help you understand the need for a Deep Learning course in the future.

Machine Translation: Assistants like Siri, Google Assistant, and Alexa have changed our mundane routine around the globe. These assistants are trained using Deep Learning algorithms to generate the fastest and the most appropriate responses to human queries. They translate spoken language to machine language to process natural language instantly.

Additionally, assistants and software like Google Translate perform another type of machine translation. They efficiently translate the words spoken by a person in one language into another in real-time. In the future, this technology will improve multifold to translate more than one language as the speakers speak. If you are a technology enthusiast, get a PG in Data Analytics now! You can be a part of the ongoing technology revolution.

Mobile AI: Several AI-based applications are available in the market today. However, the lack of computing space and memory in mobile phones stops this technology from reaching larger audiences. In the future, most of us will leverage deep compression technology to reduce the size of applications. These applications could be developed for laptops, tablets, or phones.

Research: Deep Learning is not only for delivering comfort and luxury. It also delivers on bigger responsibilities like saving lives. Medical researchers have teamed up with IT professionals to create systems for detecting life-threatening cells (cancerous) in the human body. We have indeed used diagnostic systems assisted by computers for a long time. But the introduction of deep learning has changed the game in medicine.

Data processing is used for detecting hazardous cells in the human body. However, many challenges in this field are yet to be solved. Hundreds of medical imaging devices generate images based on varying principles. Technology has not developed at a pace to understand this level of variation. You can join the healthcare field with a PG in Data Analytics.

Employee Retention: Companies are bombarded with large volumes of employee data that require daily processing. To retain employees by providing the best satisfaction level, companies’ HR department must link their employee information with different categories. These categories are compensation management, onboarding procedures, and incentive management. In the future, Deep Learning can be paired with blockchain to get better results. Also, the process of training and employee reviews could be automated using Deep Learning. A Deep Learning course can help you understand the new HR-related tools. Most of them are AI and DL based.

Customer Support: Today, this technology is used to improve the performance of chatbots. They work with inputs from words, audio, images, and videos to generate the desired output. In the coming years, this technology will expand its scope to include various facets of customer support. You can check out the Data Analytics course with placement options. This would help you set foot in the field fast.

Defence: The Defence ministries of many countries are leveraging deep learning to detect hazardous and safe areas for their naval, air, and ground soldiers. In the coming years, these algorithms can diversify to automate the responses of missile systems against an incoming threat.

Kickstart your career with a Data Analytics course with Placement opportunities

The knowledge of Deep Learning or neural networks can help you explore a career in various IT roles. These include the positions of Data Engineer, Software Developer, Research Analyst, Bio Information, and Data Engineer. Generally, Machine Learning and Deep Learning come under the umbrella term of Artificial Intelligence (AI). According to the study by The International Data Corporation, the financial value of AI is expected to reach $7.8 billion. Imarticus Learning offers a job-ready Data Analytics Course with placement for fresh graduates and professionals with 0-5 years of experience.

Imarticus Learning offers a job-ready curriculum for fresh graduates and professionals early in their careers. You can learn more about the PG In Data Analytics program on the official website. Call their professional support, or use chatbots to resolve your queries.

Does your machine learning course offer these features?

Posted on by Imarticus Learning

Are you considering taking a machine learning course to level up your skillset? It is essential to make sure that the course you choose is comprehensive and provides tools that are easy to understand. 

Whether you want an introductory crash course or an in-depth, hands-on course with practical takeaways, there are certain features every good machine learning class should have. 

In this blog post, we’ll examine key elements of a quality machine learning course so you can make sure it fulfills all your educational needs.

The fundamentals of machine learning

artificial intelligence and machine learning courses

First, a great machine learning course should give students an understanding of ML algorithms and techniques fundamentals. This includes supervised and unsupervised learning, hyperparameter tuning, feature engineering, and more. It should also provide an overview of popular ML libraries such as TensorFlow, Scikit-Learn, Keras, PyTorch, and others. A solid foundation in ML basics will ensure students have the knowledge they need to become well-rounded practitioners. 

Projects & Applications 

A good ML course should also include hands-on projects that help students apply their knowledge of ML techniques in real-world scenarios. Projects can consist of image recognition applications using convolutional neural networks or natural language processing tasks using recurrent neural networks. By working through these projects, students can gain valuable experience that they cannot teach in the classroom alone. 

Tools & Resources 

A quality machine learning course should provide access to helpful tools and resources to aid their learning process. For example, many courses offer access to cloud computing platforms such as Google Cloud Platform or Amazon Web Services, which allow students to run experiments on powerful machines with minimal effort. Additionally, some courses even offer access to GPU instances so that students can significantly speed up their training time. All of these tools are invaluable when it comes to mastering ML concepts quickly and efficiently.    

Real-World Examples

An excellent machine learning course should have plenty of real-world examples so students can better understand how their learning concepts apply to everyday life. After all, what good is knowing the theory if you can’t use it in practice? Real-world examples also help students think more creatively about solving problems and give them the confidence to tackle new challenges. 

Interactive Exercises

An excellent machine-learning course should offer interactive exercises that allow students to test their knowledge and understand how different concepts interact. This helps build critical thinking skills and will enable students to learn from mistakes. Plus, these exercises are usually fun, making them more engaging for students than just reading textbooks or watching lectures. 

Course Support

 Students need to know they have support available if they get stuck on a problem or need help understanding something. An excellent machine learning course should provide online and offline access to resources such as tutorials, forums, mentors, and more so that students can get help when they need it most. 

With so many options for machine learning courses, it can take time to decide which is best for you. To make sure you get the most out of your educational experience, take the time to research each course carefully before making your decision. Make sure it covers all the basics and provides hands-on projects and access to helpful tools and resources—then you will know you’ve got yourself a winner!

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

Ready to take your career in a new technology-focused direction? Enroll at the E & ICT Academy for their comprehensive IIT AI ML course! Equip yourself with all you need for revolutionary leadership – don’t hesitate, start your journey now!

 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 certificate 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 these AI and machine learning courses 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.

A comprehensive guide on how to build a neural network: Python for beginners

Posted on by Imarticus Learning

Neural networks are systems that operate similarly to the human brain. Currently, neural networks are used daily – even without us realizing it. For example, when we use our mobile assistant to perform a search- such as Siri, Google, or Amazon Web- it is all neural-network driven. In computer games, neural networks are also utilized – such as how the system adjusts its players, maps applications, processes map images, and finds the shortest routes to reach the destination.

Neural networks can perform any of the following tasks:

Translate text

Control robots

Recognize speech

Read handwritten text

Identify faces

How does the Neural Network Function?

There are three different layers to neural networks:

Input layer: Picks up the input signals and passes them on to the subsequent layers.

Hidden layer: This layer does every feature extraction and calculation required. Often, we will find more than one hidden layer in a single neural network.

Output layer: This is the final layer and has the job of delivering the result

best data analytics course

To better understand these layers and their workings, let’s take a real-life example of how traffic cameras capture the license plates of vehicles speeding on the road. Pixels, which make up the picture, are fed into the input layer in arrays. Each neuron has activation, or an assigned number, ranging from 0 to 1. For the white pixel, it is 1, and for the black, it is 0. Next, the input layer passes these inputs to the hidden layer. All the interconnections have weights assigned to them randomly, which then is multiplied by the input signal. Lastly, a bias is added to all these weights.

The weighted addition of the inputs is then converted to the information of the activation function- this function decides which nodes should fire for the feature extraction.

Here we will take a look at the different types of activation functions that are there:

Threshold function: This function is deployed when we don’t want to worry about mid-level uncertainty.

Sigmoid function: The sigmoid function is useful when the model is required to predict probability.

Hyperbolic Tangent function: This function is very similar to the sigmoid function; however, it ranges from -1 to 1.

ReLU (rectified linear unit) function: This rectified linear unit (ReLU) function works in such a manner that it gives the function value, however, if the value is over 1, then it will just be one, and similarly, if the value is less than 0, it will be just 0. This ReLU function is the most commonly used among all.

Now that we know all the activation function types, let’s explore more neural networks. So finally, the model, after having applied a suitable application function to the output layer, will predict what the outcome is. The error rate in the output is minimized through the adjustment of weights, and the error itself is back-propagated through the network. The weights are constantly adjusted until they perfectly fit all the training models that it has been put in. Furthermore, the output is contrasted with the original result, and numerous iterations are done to attain maximum accuracy.

Neural Networks and their Types

There are mainly six different types of neural networks, as discussed below:

Feed-forward neural network: This is the example we just explored where data travels in a uni-directional manner- from input to output. It is also the simplest form of artificial neural network.

Radial basis function neural network: Here, the data point is classified based on distance from any central point. We often want to group things and contract a centre point when we don’t have any other training data. This network groups similar data points and has its application in power restoration systems.

Kohonen Self-organizing Neural Network: Discrete maps that are comprised of neurons have inputs that are vectors of random input. Applications of this network include recognizing patterns in data such as medical analysis.

Recurrent Neural Network: Here, the hidden layer saves the output such that it becomes part of the new output and can be used for future predictions.

Convolution Neural Network: Here, the input features are filtered and taken in batches, therefore, allowing the network to remember an image.

Modular Neural Network: This model consists of multiple neural networks which function together to get an output.

Keras is an efficient software that allows users to create artificial neural networks with the Python programming language. Many data scientists use Python because it is essential for the Keras software. If you are new to Python and data science, you can opt for a Data Science and Analytics course or data analyst training to learn how to use Python more efficiently.

Aspirants in this Data Science and Analytics domain can benefit significantly by enrolling in the Post-graduate Program in the Data science and analytics course of IMARTICUS Learning.

Data Scientist Careers: What You Should Know Now

Posted on by Imarticus Learning

Scientific usage of gathered and filtered information for extrapolating the outcome of an unknown (future) situation by using different tools and languages before presenting the data in a visually ready reckoning format for influencing business decisions is called data science. This process consists of two main tasks – exploratory data analysis and data engineering. Other related activities such as data visualisation, data analytics, model creation and model deployment complete the entire cycle and the entire gamut of activities which fall under data science.

Career Guidelines

data science course

The Data Scientist career is arguably the fastest-growing career in the software world and is not just meant for candidates who have a Bachelor’s degree in Software Engineering or Computer Science Engineering. Study on data science also includes artificial intelligence, machine learning and deep learning in the course. However, certain key skills and know-how are required before taking data science as a career option. Some basic guidelines for such aspiring candidates from the non-core or non-technical subjects or domains are as follows –

  • It may be noted that the activity of a Data Scientist on one platform is very different from that of another. So, a prospective candidate has to figure out the course that the candidate is interested in and is capable of handling. The candidate must learn the skill of data collection by reading articles or videos and develop the habit of recording information by taking notes.
  • Making friends in the data science community before taking a deep dive into this field is also another useful idea. Community discussions unravel many unknown facts and may help the aspiring Data Scientist to come across a solution. Besides, they may continually be aware of the best practices in the subject domain and come across leaders in this field, who have already tasted success, albeit from a different field.
  • A Data Scientist must be in the habit of upgrading his or her knowledge by studying the latest research papers or new tools, languages and algorithms that are being published from time to time. The constant inquisitiveness regarding the updated knowledge will help the candidate to scale more heights.

Career Options

Data science and machine learning courses in India result in highly rewarding careers. However, it may be noted that the application of data science is industry-specific and hence the knowledge of a Data Scientist is a bit incomplete until and unless he becomes a domain expert. The different jobs that a prospective candidate may hit upon after completing the course are as follows –

  1. Data Analyst – This is the beginner’s role that a candidate lands upon just after completing the course. The basic functions of this position are to gather raw data, clean them and apply algorithms to it by the usage of statistics and other tools. Besides, they have to be masters in PPT and other tools that are used for presentations. A business house takes decisions after they review its inputs.
  1. Data Scientist – The role of a Data Scientist is a consultant’s role and oversees the activities of a Data Analyst along with other important functions like model creation, implementation and testing. A Data Scientist, who is also an expert in domain knowledge, is an asset to the organisation. The models that he creates are more practical and expected to yield far more accurate future predictions. It is the role of a Data Scientist to derive the key performance indicators of an organisation.   
  1. Business Analyst – A Data Scientist with a strong interest in business development predictions and ample domain knowledge often chooses the career of a Business Analyst. However, in this case, it may be noted that he must also possess operational knowledge along with the past and present practical challenges faced by the business. It’s noteworthy to mention here that a Business Analyst can also extract the key performance indicators of a company.
  1. Statisticians – The role of the Statisticians is to collect and organise data in a way that a trend is visible through the interpretation of those data. The sole purpose is to make a business decision. They also play an immense role in advising on long-term organisational strategies. 
  1. Financial Analyst – A candidate coming from a finance background and foraying into the field of data science often chooses this portfolio. This position is of diverse nature and finds roles in banks, NBFCs and corporate, as well. We may find them as a Credit Analyst or a Financial Model Developer.
  1. Machine Learning Engineer – Machine Learning Engineers provide software solutions through clean data funnels. Strong statistics and programming skills coupled with software knowledge are their basic assets. They also run tests and experiments on the different models they create.

Conclusion

Data science has already picked up its growth trajectory in India, exponentially, and it has the potential to double the industry volume just within a couple of years. The IIT Roorkee data science and machine learning course at Imarticus is an online course. The duration of the course is 5 months. It is one of the best data science courses in India that will help you to achieve your dream. 

Is A Supply Chain Analytics Certification A Good Investment Of Your Time and Efforts?

Posted on by Imarticus Learning

Supply Chain Management is a rapidly growing field with tremendous potential for career growth. With the advancement of technology and globalisation, the demands on supply chain professionals have increased exponentially. A successful career in Supply Chain Management involves analysing data, understanding customer needs, and managing resources to ensure customer satisfaction.

Supply Chain professionals are highly sought after due to their ability to forecast market trends and respond quickly to changes in demand. They are expected to create efficient strategies for optimising inventory levels, manufacturing processes, distribution networks and transportation systems to reduce costs and improve customer service. Additionally, they must understand how each element of the supply chain affects one another to identify opportunities for improvement.

Those pursuing a career in Supply Chain Management should have strong analytical skills, make informed decisions quickly, and be comfortable working with various stakeholders. Supply Chain Managers must also possess excellent communication and problem-solving skills to collaborate with other departments and develop innovative solutions effectively.

How can a Supply Chain Analytics certification help gain industry insights?

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A Supply Chain Analytics certification provides professionals with the knowledge and skills necessary to identify trends and opportunities in the supply chain. Through a supply chain management course with analytics certification, individuals can improve their understanding of analytics tools, develop strategies for optimising performance, and gain insight into industry best practices. In addition, this certification can equip professionals with the ability to make data-driven decisions that will benefit their organisation.

Furthermore, Supply Chain Analysts can apply for certifications such as PMI’s SCMA (Supply Chain Management Analyst) or APICS’ Certified Supply Chain Professional (CSCP). These certifications provide comprehensive training on all aspects of supply chain management, from inventory control, logistics planning, demand forecasting and more. With these credentials, Supply Chain Analysts can demonstrate their expertise and add value to their organisation.

Pursuing a career in Supply Chain Management can open up numerous opportunities for professionals looking to build successful careers.

Certifications for Supply Chain professionals can provide them with the necessary knowledge and skills to develop efficient strategies that will benefit organisations in today’s competitive business environment.

The outlook for supply chain analysts in the industry is very positive over the next few years. According to the Bureau of Labor Statistics (BLS), employment for all logisticians, which includes supply chain analysts, is projected to grow 5% from 2019-2029, faster than the average for all occupations. A continued increase in global trade and rising customer expectations for quick delivery of goods drive growth in this field. Therefore, it’s likely that demand for experienced and knowledgeable supply chain analysts will remain high through 2023 and beyond.

What skills do you gain after a Supply Chain Analytics certification?

A Supply Chain Analytics certification can equip professionals with the ability to:

  • Analyse large amounts of data,
  • Interpret and conclude trends,
  • Implement changes that improve efficiency throughout the supply chain,
  • Identify areas for improvement in the organisation’s supply chain processes,
  • Collaboratively develop innovative solutions.

To become a supply chain analyst, the students, through the course, also learn how to use various analytics tools such as Big Data, Predictive Analytics, Machine Learning, and Artificial Intelligence (AI) to enhance decision-making capabilities.

Learn and Grow with Imarticus Learning:

The professional certification in supply chain management with Analytics from Imarticus Learning is a future-proof way to make a career with a new-age curriculum.

These certifications for the supply chain profession have been designed in collaboration with CEC, IIT Roorkee and industry leaders to provide a cutting-edge experience for candidates who wish to enter the operations and supply chain industry.

This course enables you to make data-driven decisions at strategic, operational, and tactical levels. With the current shortage of professionals with process and analytics talents, this certification prepares you for high-demand job roles such as Demand Planner, Supply Planner, and Supply and Operations Planner.

Course USPs: 

  • Premium education standards to be competitive
  • Unique pedagogy by IIT faculty & industry leaders.
  • The program prepares students to gain from trending opportunities.
  • Campus Immersion at IIT Roorkee

Learning and Training for Apache Hadoop Online

Posted on by Imarticus Learning

The amount of data generated online is increasing at a rapid pace. Organisations use raw data to derive meaningful insights to make decisions. Companies are adopting Big Data tools to process vast volumes of data from many sources. Apache Hadoop is a significant disruptor in Big Data. This technology has proved useful in capitalising on Big Data. For a career in Big Data and analytics, one must understand Apache Hadoop. A PG in Data Analytics course from a reliable and certified source is the way forward.

Let’s understand what Hadoop is all about.

What is Apache Hadoop?

Apache Hadoop is an open-source platform. It is used to store and process Big Data on a large scale. It offers reliable, scalable and distributed processing of massive amounts of data. Hadoop-based applications use data stored on clusters of commodity computers. These sysetms are inexpensive. Thus, Hadoop uses the computational power of an extensive network of clusters at a lower cost.

Doug Cutting and Mike Cafarella created the Big Data analytics platform in 2005. Many large businesses use Hadoop. It provides a cost-effective solution to store and process data. Big Data analytics is an emerging field used across many sectors. The good thing is that one can learn Hadoop online and prepare for a career in data analytics.

Applications of Apache Hadoop

Learning Hadoop can be excellent for a data analytics career. Let’s look at some of its primary uses in the real world.

Understanding customer requirements

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Companies in sectors like finance and telecom use Hadoop to get customer insights. For example, sales data determines when a specific product sells the most. Or why certain products sell more on particular hours or days. Hadoop can track social media data and online conversations about a business. Monitoring online data helps the business understand customers’ grievances and expectations. Hadoop’s insights can enhance the quality of service and improve customer satisfaction.

Security and law enforcement

Hadoop is also in use in the field of security. Many law enforcement agencies use it to become more efficient, proactive, and accountable. Hadoop enables them to detect suspicious activities and security breaches in real-time. The police department uses it to predict criminal activities and catch criminals. The national security agency of the USA uses it to prevent terrorist attacks.

Financial institutions also use Hadoop to detect any fraudulent transactions.

Healthcare intelligence

Healthcare providers and healthcare insurance companies are using Hadoop-based healthcare intelligence apps. These apps help process large data sets and devise smart business solutions at a lower cost. Hadoop’s scalable and inexpensive features can accommodate dynamic data and scale.

Financial trading and forecasting

Hadoop is quite popular in the field of financial trading. Its algorithm allows it to scan markets with predefined conditions and find trading opportunities. Most trading decisions are made using Hadoop in high-frequency trading.

Improving science and research

Hadoop plays a major role in the research field as well. A considerable amount of relevant data is extracted and analysed to conclude early. This saves time and effort and improves the quality of output.

Optimising business processes

The most common application of Hadoop is to optimise a company’s performance. It can improve various business processes. Workspaces are improved by monitoring the employees’ behaviour. Most business decisions can be taken with the help of insights derived using Hadoop.

Takeaway

Apache Hadoop has plenty of other applications as well. The good news is that one can easily learn Hadoop online. If you want a successful data science career, you need to build your knowledge base in the field. We at Imarticus Learning offer one of the best data science courses with placement. We have developed our Post graduate program in data analytics and machine learning after having an in-depth understanding of the data science job landscape. The data science course with placement has a cutting-edge curriculum that teaches all the in-demand data analytics tools and job-relevant skills. Applicants can also learn Hadoop online in this course.

This PG in data analytics course will transform learners into expert data scientists as the course includes real business projects, relevant case studies, and mentorship from industry experts. It guarantees interview opportunities for learners, as we have over 400 placement partners. Fresh graduates and early career professionals (0-5 years of experience) with a technical background are welcome to apply. Anyone interested in enrolling in the program can enquire about it or chat with us.

Data science is a highly rewarding career path with many job opportunities across several sectors. Our data science courses with placement will set anyone inclined to be a data analyst on the right path.

Your data analyst training course is incomplete without these features

Posted on by Imarticus Learning

Data analytics is one of the most trending careers in present times. Earlier, data was considered the new oil in the industry because of its requirement in almost every sector. These days its significance is more than oil, as none of the business sectors can survive without data and insights. 

Hence, there is an increasing need for professionals in this sector. And, you can find data analyst training in various educational institutes. But, the question is which one to choose amongst these courses. To help you solve this conundrum, the following is a list of features that a course for data analysis must contain.

5 must-have features of a data science and analytics course

As we have already mentioned, multiple data analytics courses are offered across various platforms. Some are made available online and others in colleges or universities. Some of them are free, some are paid, and some are based on the freemium model (a portion of the course is free and the rest is paid). But, which ones should you choose? This can be done with the help of a checklist which consists of some basic features. Without these, a data analyst training course is deemed incomplete. What features are these? Read on…

It should consist of placement assistance along with the training programme

 

Completing a data science and analytics course and securing a job right afterwards would help you secure a handsome salary package. Therefore, your data science course should have a placement programme and the partner companies should be some of the reputable names in the industry. Otherwise, you will not get the exposure you need to build a successful career in this domain.

The curriculum should be job oriented

If your course lacks the modules that are helpful for the job, then you are in for a rude shock, as none of the placement agencies like candidates who do not have skills that make them job-ready. By this, we mean that you should have knowledge of data science and analytics modules like Python, SQL, Power BI and Tableau. Without these courses in the curriculum, you will not even be considered for an interview. So, be on a sharp lookout while selecting the programme.

There should be live learning modules

The training methodology needs to be hands-on. The faculty should teach you all the basics of data science and analytics, as well as the prevalent practices in the industry. If you are able to grasp the whole mechanism, then you will be able to take on any role in your job in data analytics. However, if your programme does not have a live learning module, then you very well know what to do. 

The programme should contain some real-world projects

Without these, you are just another data analyst in the room. Live projects enable you to test your theoretical knowledge and check your progress as a professional. Moreover, you get a chance to experience the real-world application of what you are studying and how clients and companies work, along with the type of issues you are likely to face when working as a data analyst.

The programme should offer dedicated career services

To excel in a data analyst interview, you need to give your best. And to put your best foot forward, you need to participate in interview workshops, resume building sessions, profile building exercises and one-on-one mentorship. Your data analytics training should include proper career services so that you can easily land your dream job.   

A data science and analytics course enables access to one of the most prized job opportunities, which is highly rewarding if you grab it with both hands. So, it is extremely important that you find a course that fulfils your requirements and then gives you the professional advantage you need to advance in your career. In this regard, you can use the advice mentioned above to your advantage while researching for your PG in data analytics course.

And, if you are looking for a readymade solution, consider the postgraduate program in data analytics from Imarticus Learning. This course will get you placed in the top 500 companies, and you will get a chance to learn from a well-reputed faculty with decades of industry experience in data science and analytics.

Deep learning is easier than you think: Read on

Posted on by Imarticus Learning

Do you wonder how Google translates the entire paragraph into one language in seconds? Or about the recommendations we get on YouTube and Netflix? How do self-driven cars function? All these smart innovations are the product of Deep Learning.

Deep Learning is a part of ML and AI. It uses the multi-layered structure of the algorithm. This is known as a neural network.

Almost every technical item has AI integrated. So, a career in Data Analytics has a bright future. You can learn data analytics to start your career. This is an exciting and intriguing topic. But you need the right course and guidance to become an expert. A Deep Learning career will enable you to contribute to the futurist society.

 What is Deep Learning?

 DL falls under the subcategory of AI. It’s a general term for processing, enabling computers to function independently. In simple terms, it tries to mimic human behaviour to analyse and predict. DL algorithms analyse and conclude with logical structuring. In short, it has multiple layers of neural networks.

 What is the Difference between ML and DL?

 ML is the process where computers learn data by using algorithms to work independently. They don’t need external intervention. Whereas in DL, the performance is entirely end-to-end. Users give raw data as inputs. Now, the machine performs tasks like a human brain. Deep learning facilitates the processing of unstructured data. This includes images, texts, and documents.

One needs to understand DL and its algorithm scale with data. Only then can you enable it to perform machine learning.

 A Career in Deep Learning

Deep Learning Career is the most demanding and desirable course in this digital era. Everything is shifting to AI, and mastering this skill will offer us a better future, growth and good earning opportunities. The opening for a wide range of job profiles like software engineer, research analyst, data analyst, and data engineer is taking over huge packages.

Skills Required to Master Deep Learning 

To make a Deep Learning career and learn Data Analytics, we need specific skills as a student to exercise it rightly, such as software engineering skills, including a thorough understanding of Data Structures, Algorithms, Github, and the Software Development Life Cycle.

 Deep Learning Neural Network

● Convolutional networks

● RNNs

● LSTM

● Adam

● Dropout

● Batch Norm

● Xavier/He initialisation

 Probabilistic Methodology

● Distinctive and continuous distributions

● Cost functions with the highest likelihood

● Tasks and training data with hypotheses

● Maximum likelihood-based cost

● Cross-entropy

● Cost-forward networks for MSE

● Sigmoid units, MLP

● the influence of neuroscience

● Gradual ascent

● Chain rule with recursion

● Optimal bias-variance ratio

● Regularisation

 Practical

● Linear regression

● Softmax

● tanh

● REPLY

● Tensorflow

 Job For a Deep Learning Specialised Candidate

Deep Learning Career studies and courses are transforming the lives of professionals with ample opportunities on the way. Here are some common job roles which employ the used Deep Learning:

● Software Engineer

● Research Analyst

● Data Analyst

● Data Scientist

● Data Engineer

● Neuroinformatics

● Bioinformatician

● Image Recognition

● Software Developer

● Research Scientist

● Research Fellow

● Instructor for Deep Learning

● Applied Scientist

● Full Stack Web Developer for Deep Learning

● Lead Manager – Deep Learning

● Natural Language Process Engineer

 The question of how to become a Data Analyst lies in mastering Deep Learning. Once we get this specialisation, getting hired for such job profiles is easier.

 The average salary structure for the deep learning expert ranges from $77,562 per year for a Research Scientist to $60,255 per year for a Data Analyst engineer.

 Deep Learning Career Outlook 

Computer and Information Researchers are the most sought-after profile in Deep Learning. The correct blend to learn data analytics with a clear concept of deep learning is a lucrative career path for engineers.

It is a great idea to specialise in such a futuristic subject and excel in it. So, if you want to study Deep Learning, join Imarcitus and supercharge your career with us! Contact us online, or visit our training centers spread across top cities like Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, and Ahmedabad.

The nuances of operation planning supply chain management

Posted on by Imarticus Learning

Operation planning in supply chain management is a complex task. This is because the operation planning factors impact the production process. These factors include the availability of raw materials. They also include production facility capacity, demand for final product, and disruptions in the supply chain. Operation planning must also consider the company’s objectives. This blog post discusses the nuances of operation planning. It also shares how to overcome some challenges.

1. Forecasting

A challenge in operation planning is forecasting. Forecasting estimates future demand for a product or service. This is difficult because many factors impact demand. They are the economy, changes in consumer preferences and new competitors.

AI and ML are used to help forecast demand. They analyse data to see trends. This can then be used to predict more accurately.

2. Production Capacity

Another challenge is production capacity. It is the maximum amount of a product or service produced by a manufacturing process.

There are two types of production capacity. They are theoretical and practical. Theoretical capacity is the maximum output that can be achieved. That is if all the machines in a production system are used to their full potential. Practical capacity is the maximum output that can realistically be achieved.

Practical capacity is often lower than theoretical capacity. This is because of many factors. They are downtime, maintenance, and employee breaks.

To overcome this challenge, firms need to assess their production capacity accurately. They can do this by doing production capacity analysis. This is a process that assesses the capacity of the production system. This also identifies constraints.

Constraints need to be identified. Then, firms put try to improve production capacity. This could mean investing in new machinery. Training employees to use the existing machinery can be done too.

3. Supply Chain Disruptions

Another challenge is supply chain disruptions. It is an event that disrupts the flow of goods and materials in the supply chain.

Supply chain disruptions occur due to many reasons. They are natural disasters, political unrest, and transportation problems. They can also be caused by suppliers going out of business. Failing to meet delivery deadlines is another reason.

Supply chain disruptions have a major impact on businesses. They can cause production to stop entirely. They can also lead to shortages of raw materials and finished products.

Firms must address supply chain disruption risks. This could involve diversifying the supplier base. Having alternative sources of raw materials is there. Making contingency plans can also be done.

4. Miscellaneous Challenges

It is important to think of other challenges impacting operation planning. They are quality issues and regulatory changes.

Quality issues can occur when products do not meet the required standards. This could lead to customer returns, production delays, and financial losses.

Regulatory changes can impact operation planning if the products or services being produced are subject to government regulation. For example, if the government introduces new environmental regulations, this could impact the production process and the materials used.

New product development can also impact operation planning. This is because new products often require different production processes and materials. This can lead to supply chain disruptions and production capacity changes.

It is essential to consider all of these potential challenges when operation planning. By doing so, companies can be better prepared to deal with them if they do occur.

Conclusion

Operation planning is a complex process that must take into account many factors. The challenges discussed in this article are just some of the challenges that need to be considered.

Companies must overcome these challenges to ensure that their operation planning is successful. They can do this by conducting a production capacity analysis, implementing measures to mitigate the risks of supply chain disruptions, and considering all potential challenges.

If you want to learn more about operation planning, overcoming these challenges, and embark on a career in this field, consider enrolling in Professional Certification in Supply Chain Management & Analytics by the prestigious IIT Roorkee. This program will give you the skills and knowledge you need to become a successful supply chain analyst.

Some key features of the operation planning course in supply chain management include Certification by IIT Roorkee, hands-on learning with real-world SCM projects, and live sessions with IIT Roorkee faculty and industry experts.

If you need assistance regarding the program, contact us through chat support or drive to our training centres in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad. We will be happy to assist you.

Training in Excel as A Data Analyst? Read On

Posted on by Imarticus Learning

There are different opinions when it comes to training for a job in data analysis. Some people will tell you that getting a degree from a top university is the only way to succeed. Others will say that experience is more important than anything else. And still, others will tell you that specific skills, like the ability to use Excel, are what matters. So, who should you believe?

As it turns out, all three groups of people are partially right. You can gain entry with a degree from a prestigious university. And yes, experience is essential. But when it comes to landing a job as a data analyst, specific skills—like the ability to use Excel—can decide between getting an offer and passing over.

Why Excel Skills Matter for Data Analysts?

Excel is the most commonly used program in business today. That’s because it’s a potent tool for everything from keeping track of sales numbers to performing complex financial analyses. And while Excel might seem like a program that anyone can use, the fact is that most people only know how to do the basics.

 

As a data analyst, though, you need to be able to do more than just the basics. You need to be able to use Excel to its fullest potential to find trends, uncover relationships, and make predictions. In other words, you need to be an expert. And that’s why having Excel training can be necessary when looking for a job as a data analyst.

Excel Skills You’ll Need as A Data Analyst

You will learn analytical and visualization techniques in this course, which will help you improve your current work performance and your job prospects in the future. If you’re in business or data analysis and want to enhance your employability, learn advanced Excel and basic Power BI.

So what kind of Excel skills do you need to land a job as a data analyst? 

Here are just a few of the things you should be able to do:

  • Import and export data from different software programs 
  • Use basic formulas and functions 
  • Create charts and graphs 
  • Perform simple statistical analysis 
  • Write macros 
  • Use pivot tables 

As you can see, having training in Excel can give you a real leg up when you’re looking for a job as a data analyst. However, it’s important to note that simply taking an excel course is not enough—you also need to learn python online and have some experience working with data to excel in this field. Still, if you’re looking for a way to enhance your chances of landing a job as a data analyst, getting training in Excel is certainly worth considering.

Discover PG in data analytics with Imarticus Learning

 You might be able to find employment at Tableau with the aid of this data analyst training. Create complex models using data science to produce vital business forecasts and insights using what you’ve learned.

Course Benefits for Students:

  • Students will be familiar with the most common data science methodologies and tools and the fundamentals of data analytics and machine learning.
  • Students who complete 25 real-world projects and case studies under the guidance of business partners will receive a data analytics course with placement.
  • The Data Analytics and machine learning course is an intensive program that will teach you how to use data analytics to solve real-world problems.
  • One of the most in-demand skills on the market is the capacity to display data using a data analytics tool.

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

Get a free 1:1 Imarticus counselling
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