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Category: Analytics

Here’s how to build a multi-layered neural network in Python

Posted on by Imarticus Learning

As businesses are recognizing the importance of neural networks, there is an increasing demand for the appropriate implementation of the same. Therefore, you can opt for a neural network tutorial. If you find the prospect of building and implementing neural networks to be exciting, then you can choose Imarticus Learning’s AIML program.  

How to Develop a Neural Network with Multiple Layers in Python

A master’s in artificial intelligence is essential to have a career in neural networking. However, you can start with the basics like building a network with Python code. Take a look at the following steps to learn more. 

Step 1: Prepare the Functions and Variables

To prepare all the functions and variables, you need to use the NumPy library. It is easier to do the calculations with this tool. Once your calculations are complete, you can move on to function activation. For this, you need to use the logistic sigmoid function.

Once you have all the values, you can decide on the learning rate, the input layer dimensionality, and the hidden layer dimensionality. This is crucial for a multi-layer neural network. You also need to determine the epoch count. 

Next, you need to fill the weight matrices with the np. random.uniform() function. Keep in mind that the values will be between -1 and +1. When this is complete, you need to set the empty arrays which are necessary for the values of preactivation and post-activation which are found in the hidden layer. 

Step 2: Import the Training Data

Use the Pandas library to import training data that is stored in Excel. Once you import it, you will need to convert that data to the NumPy matrix. 

Step 3: Initiate Feedforward Processing

Feedforward is a part of the neural network that is present within the computations that lead to the output. In the first loop, you will find more than one epoch value. You can calculate the output from each epoch value. The third loop is where you will need to check each hidden node separately and use the dot product to get the preactivation value. This will help to generate the post-activation signal. 

Finally, you can calculate the value for the output node. To do this, you need to calculate the pre-activation signals with the help of the dot product, and then use the activation function to get the post-activation signal. To know the final error, you will have to subtract the target from the post-activation signal of the output node.  

Step 4: Back-Propagation of the Output

You need to reverse the direction once the feedforward processing is complete. You need to first shift from the output node to the hidden-to-output weights and then to the input-to-hidden weights. This back-propagation will help to provide the error data that you can use to train the neural network.

Choose Imarticus Learning’s Course for Neural Networking

If you wish to learn about neural networks and pursue a career in that discipline, you need a degree from a reputable institute. At Imarticus Learning we offer certification in Artificial Intelligence and Machine Learning which is in collaboration with IIT Guwahati and the E&ICT Academy.

Industry experts have assisted our academicians in creating a curriculum that includes artificial intelligence, machine learning, AI deep learning, and data science. You will be learning through live lectures, hands-on project work, and assignments. 

Imarticus Learning prepares students for rewarding jobs as data scientists or machine learning engineers. The certification in Artificial Intelligence and Machine Learning is open to candidates with a Bachelor’s or Master’s in mathematics, computer science, engineering, statistics or science. If you have 50% in your graduation, enroll in our program and land the best job in the industry.

What no one will tell you about data analytics job applications

Posted on by Imarticus Learning

Do you know what the data analytics job roles are? At Imarticus we look at the keys to this professional profile, what their work consists of and the main requirements to start a career as a data analyst. We also tell you all you should know about data analytics jobs.

We are surrounded by data that, while it may not mean much in its raw form, can give significant value to many businesses and organizations when analyzed and turned into information. It’s not about who has the most, but who gets the most out of it at the end of the day.

The data analyst is a specialist who converts data into information so that they may make better-informed judgments. To that goal, these experts complete the following tasks:

In the discipline of data engineering, consider the following:

– Data acquisition: 

  • Dataset identification: data may be found in a variety of places (e.g. databases, social networks, etc.).
  • Acquisition: strategies for retrieving data for data analysis and processing.
  • Review of the information gathered (structure).

– Preparation: 

  • Exploration: using strategies to gain a better understanding of the data through preliminary analysis and a study of its nature (correlation, trends…).
  • Data cleansing (incoherent, duplicated, incorrect values, etc. ), transformation, and packaging into useful/manageable structures for processing.

In the subject of computational data science, there are a few things to keep in mind:

– Analyze: by deciding on the best strategies and creating processing models (predictive models, classification, clustering, etc.).

– Dissemination of data analysis/processing outcomes.

– Using the model’s conclusions in real-world situations, such as decision-making.

Data analyst profile

Due to the incipient process of digital transformation that many firms and organizations that already have a huge quantity of data but don’t know how to use it to gain commercial benefits have begun to handle, the data analyst’s profile is one of the most in-demand today.

With the rise of new occupations coming from technology demand, such as data analysts, the necessary training to perform the activities of this profile may be obtained in a variety of methods. STEM (Science, Technology, Engineering, and Mathematics) degrees are the ideal place to start if you want to learn the fundamentals of this field.

There are also many postgraduate and master’s degrees available to become an expert in this sector, such as a master’s degree in Big Data Analysis and Visualisation / Visual Analytics & Big Data.

Requirements to be a good data analyst

– Communication skills: describing the outcomes of the task to company or organization managers and directors who do not have a technical background.

– Dashboard design and implementation experience, particularly in the area of business intelligence.

– Familiarity with distributed storage systems

– Technological and “Machine Learning” foundation: algorithm creation, programming languages and databases management, and so on.

– Computer science, mathematics, and statistics knowledge: these profiles must be able to analyze databases, construct models, and forecast statistics, among other things.

– The capacity to evaluate data and draw judgments based on it is critical.

– The capacity to synthesize data in order to derive meaningful and relevant information.

– Analytical and creative skills: methodical, systematic, and creative workers do their tasks carefully, analyzing and processing data to develop answers to issues or company demands.

– Business acumen: understanding of the industry and the activities of the firm for which you work, as well as the ability to apply that knowledge to identify problems that can be solved through data analysis and processing.

Conclusion

If you want to find out what data analytics job roles entail, at Imarticus, we look at the most important aspects of this profession, what they do, and what it takes to get started in your career as a data analyst. We also cover all you need to know about data analytics jobs.

Careers in artificial intelligence: A smashing tool of Omnichannel

Posted on by Imarticus Learning

Artificial intelligence and machine learning can be implemented in various industries, including cybersecurity, healthcare, manufacturing, finance, and marketing. This is why there is an increase in the demand for artificial intelligence and machine learning courses. If you wish to have a successful career, you can choose our AIML program. Imarticus Learning offers the best course curriculum and learning sessions to prepare you for a steady career. 

Why Choose a Career in Artificial Intelligence and Machine Learning? 

A career in AI and machine learning can be rewarding in many aspects. Such a career is an excellent choice for those with a knack for technology. Following are some reasons you should become an expert in artificial intelligence and machine learning. 

  • Scope for Career Growth

Artificial intelligence and machine learning are beginning to become essential for businesses across industries. Therefore, there is endless scope for growth. If you start a career today, you will likely find opportunities in top managerial and research positions. Suppose you have a degree in artificial intelligence and machine learning that corresponds to current industry requirements. In that case, your career will continue to improve. 

  • Opportunity to Learn

If you enjoy learning new technological skills, then this is the best career for you. Artificial intelligence and machine learning are the new domain, dominating almost every sector. Therefore, there is a lot to learn. As it evolves, you will be learning the implementation of different technological solutions to generate the best outcomes for businesses. 

  • Competitive Salary

As artificial intelligence and machine learning are still evolving, there is a rising demand for professionals with the necessary skills. The industry is yet to become mainstream but is proving itself crucial for futuristic business processes. Therefore, you can ask for competitive salary packages from your potential employers. Companies are offering lucrative packages for artificial intelligence and machine learning experts. 

  • Jobs in Various Disciplines

If you have a degree in artificial intelligence and machine learning, you can pursue jobs in different fields. Most courses that teach artificial intelligence and machine learning include specialization in various disciplines. So, you can switch your career to that of a data scientist or even a deep learning engineer. These jobs are rewarding, and there is a demand for experts in such specialized fields. 

  • Challenging Work

A career in artificial intelligence and machine learning requires focusing on real-world business challenges and overcoming them with technological solutions. Such work is ideal if you enjoy data analysis and the use of technological tools for improving productivity. Since artificial intelligence and machine learning is evolving at every step of the way, there is no scope for stagnation. You will continue to enjoy the most challenging work in the field. 

How Can a Degree From Imarticus Learning Assist in Ensuring a Rewarding Career? 

Suppose you wish to pursue a career in artificial intelligence and machine learning. In that case, you need to choose one of the best AI ML courses. At Imarticus Learning, we offer certification in Artificial Intelligence and Machine Learning. This course is in collaboration with the E&ICT Academy and IIT Guwahati. Therefore, you will get the opportunity to interact with and learn from academicians and industry professionals.

The course is for nine months and will prepare you for a career in artificial intelligence engineering, machine learning engineering, data science, as well as data analytics. We offer real-world projects that provide you with hands-on experience in the field of artificial intelligence and machine learning. At Imarticus Learning, we also organize live lectures so that you can interact with your teachers and your peers. 

The course from Imarticus Learning is the best if you are considering a career as a data scientist or a data analyst. You can also become a specialist in artificial intelligence, machine learning, and deep learning and land the best jobs in the industry. 

Why Supply Chain Courses are in-demand and how to choose the best course

Posted on by Imarticus Learning

With the world becoming ever-more interconnected, supply chain courses are in high demand. Supply chains are not just for manufacturing companies anymore. More and more companies are starting to see how important it is to have a streamlined process from factory gate to customer delivery.

With this growth comes increased competition for quality training programs – but don’t worry! This blog post will teach you what you should look out for when researching your options so that you can choose the best course available to suit your needs.

What is supply chain Management?

Supply chain management is the process of planning, organizing, and controlling the flow of goods and services from suppliers to customers. It involves forecasting demand, sourcing materials, and production, arranging transportation and storage, and maintaining quality control. Many companies consider their supply chains to be their competitive advantage.

Why are Supply Chain courses in demand?

Supply chain courses are in demand because they can help students and professionals gain skills to be at the forefront of change. These are the following factors why SCM course is high in demand:

Increasing globalization.

As global markets continue to grow, there is a high need for people with expertise in supply chains to manage international activities smoothly and ensure that everyone will be satisfied with their products or services.

Technological advancements.

The advancement of technology has made supply chains more complex than ever before. With so many different aspects of managing, it is essential to have well-versed in supply chain management principles and strategies.

How To Choose The Best Course for your need?

Many supply chain courses are available in the market, but not all of them are perfect for your need. Here are a few factors to consider before enrolling in an SCM course.

  • Certified Material: All supply chain courses are imperfect because many use non-certified material. You must ensure that your chosen system has a certified instructor and use only certifiable resources in their syllabus.
  • Education Level: The next factor is your education level. For instance, if your current professional designation doesn’t require supply chain courses, then it will be difficult for you to learn advanced concepts of any supply chain course.
  • Training Duration: The duration of the training is another vital factor that you need to consider before signing up for a supply chain certificate program. 
  • Job Outlook: Many supply chain courses are available in the market, but not all of them will provide you with job opportunities. So before choosing a system, make sure that it gives enough knowledge to work with various organizations and provides ample career opportunities after completing the program.

Explore More and Make a Career with Imarticus Learning

IIT Supply Chain management course allows professionals to make data-driven strategic, operational, and tactical choices. There is a scarcity of experts with process and analytical skills in the Supply Chain Management area. This supply chain analytics certification prepares students for jobs such as Demand Planner, Data Scientist, Supply Planner, and Supply and Operations Planner, which are in great demand.

 Some course USP:

  • The E-Learning Centre IIT Roorkee provides the best supply chain analytics certification.
  • Impress employers & showcase skills with a career as a supply chain professional recognized by India’s prestigious academic collaborations.
  • This course provides students experience in hands-on learning with real-world SCM projects.

Contact us via the chat support system, or drive to one of our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad.

5 beginner friendly steps to learn neural network tutorial

Posted on by Imarticus Learning

A neural network mimics the human brain. The system architecture is made of artificial neurons and such a network can perform multiple functions in different industries. If you consider a career in the field of machine learning and neural networks, then a neural network tutorial is a must. You can start with a beginner-friendly tutorial and then move on to advanced topics of study. The AIML from Imarticus Learning is ideal for those interested in becoming specialists in the field. 

A Guide to Neural Network in 5 Steps

To understand a neural network, you need to understand the workings of such a network. If you opt for a Masters’s in artificial intelligence that includes a specialization in neural networks, it will be easier for you to grasp the concept and become an expert. 

A neural network has three distinct layers: the input layer, the hidden layer, and the output layer. Before we get into the details of the neural network tutorial, you need to understand how each of these layers functions. Now each layer is comprised of nodes and there can be more than one hidden layer.

As the name suggests, the input layer is responsible for recognizing and taking inputs, before transferring the signals to the next layer. Now, the hidden layers are where the back-end calculations occur. Once the results are obtained, the output layer transmits them. 

Now that you know the workings of each layer, it is important to take a look at how the network functions. Here are 5 steps that are involved in the working of a neural network. 

Step 1: Information Enters the Input Layer and Assignment of Weights

The data or the information is fed into the input layer. This then passes on to the hidden layer. At this interconnection, weights are assigned to every input. 

Step 2: Addition of Bias

The weights will multiply with each individual input. Once that happens, a bias is added to every input. 

Step 3: Transfer of Weighted Sum and Activation Function

The weighted sum, once obtained transfers onto the activation function. It is the activation function that decides which of the nodes can be used for the extraction of specific features. 

Step 4: Application Function

For the output layer to deliver, the deployment of an application function is necessary. It prompts the output layer to generate the output metrics. 

Step 5: Back-Propagation of Output

The weights need to be adjusted and then the output result is back-propagated. This helps to reduce errors. 

Using the above 5 steps, you can implement neural networks to approximate multiple functions accurately. To learn more about neural networks and move beyond the beginner level, you can opt for a course from Imarticus Learning. 

Learn Neural Networking from Imarticus Learning

Imarticus Learning offers certification in Artificial Intelligence and Machine Learning. We have designed this particular program with academicians and industry experts from the E&ICT Academy and IIT Guwahati. If you have a Bachelor’s or a Master’s degree in computer science, statistics, mathematics, economics or science and engineering with at least 50% in your graduation, then you are eligible for this course.

Our Artificial Intelligence and Machine Learning program include specialized topics like AI deep learning, machine learning, data science, and data analytics. Once you complete the course you will be able to seek job opportunities in all of these disciplines.

The mode of training for this course is online and we organize live lectures every week. You will spend 8 hours every week learning from the best academicians and professionals. We encourage students to interact and build networks during these sessions. At Imarticus Learning, we also provide hands-on training through 25 real-world business projects and more than 100 assignments. 

If you are interested in the current implementation of neural networks and wish to build a career in it, our certificate program is one of the best options. You can choose Imarticus Learning to gain excellent experience and engage with industry experts.

Develop the skills to become a supply chain analyst

Posted on by Imarticus Learning

Supply chain analysts are a key part of any business hoping to reach excellence in their operations. They develop efficient logistical methods by analyzing and supervising all the processes related to the supply chain management of a company.

Be it demand, cost, transportation, or even productivity, they have their thumbs into everything. This is why supply chain analysts are very high in demand. And, it needs specific requirements or eligibility to become supply chain analysts. In fact, if studies are to be believed the positions are only going to grow in numbers in the coming year. 

Considering this, it is the perfect time to do a course in supply chain management if you are looking for potential career scopes. There are lots of institutes in India that offer comprehensive courses in this domain. And, Imarticus Learnings has a cover-all, supply chain management course that will help you to learn all the desired skills you need to ace your career. You will also get excellent exposure which will put you in a league of your own.

Now, let us take a look at what this job entails and the skills that are required for a career as a supply chain analyst. 

Supply Chain Analyst: Job Description, Estimated Salary, and Prospects

The job market is supposed to grow about 5%, opening about 8,400 job opportunities across the United States. On a global scale, this number increases a lot more. As a starting salary, people can expect about $45,800. With years of experience, it can grow up to about $116,600 and the average annual salary stands at about $74,400.

As we stated before, this profile of supply chain analysts is high in demand and demanding as well. There are lots of things you need to do to fit into the job description. However, there are three major things that you need to be able to do. And, these are as follows:

 

  • Data analysis: Your job is to look for where the inefficiencies in the chain system lie and address them proficiently. The underperforming areas can be anything including productivity issues, changes in demands, delivery issues, weather problems, and so on. You need to be capable enough to find quick solutions for these problems. A lot of experts use professional tools like relational database management systems to analyze data in a faster and smoother way.

 

 

  • Improvement suggestions: This is one of the most important aspects of the job. After recognizing and addressing the concerns, you also need to put in suggestions that will help improve the plans and the system as a whole. During monthly sales and operations meetings, you will need to offer suggestions to the executives for a smoother experience all around.

 

 

  • Collaborate with different units: You will need to interact with employees across different units to come up with a plan that works for everyone. Based on the plan, the interactions then pan out to the vendors and even customers to negotiate proper deals and address their concerns. 

 

Skills you Need to Ace

Here is a list of skills that enhances your eligibility to become a supply chain analyst. 

  • Mathematical analytics
  • Analyzing big data samples quickly
  • Inventory management
  • Making educated predictions and plans
  • IT skills
  • Interpersonal connection skills

This is a tremendous career option for people who are looking for stable careers. Check out Imarticus Learnings’  supply chain management course by IIT Roorkee to gear up for your career as a supply chain analyst.

Supply chain management and analytics digitization: Cloud computing and predictive analysis

Posted on by Imarticus Learning

We are currently living in the era of digitization. From our daily life needs to education to retail to corporate, everything is taking to it pretty fast. Supply chain management too is a sector that has recently started integrating its essential business aspects with technology. The main reason for that is how easy it makes the process of collecting big data and silo breakdown in the supply chain. 

This is why it might be the best time to pursue a course on supply chain management if you are thinking of career prospects for a better future. As always, Imarticus Learnings has come through with its Supply Chain Management online course. It will not only provide you with amazing exposure but also put you, leagues, beyond your peers. 

Here, in this article, we are going to elaborate further on what is digitalization in supply chain management? What is predictive supply chain analytics? and how it can reinvent the whole process for a better outcome. Please go through the whole article to learn more.

How cloud computing and predictive analytics can change the supply change management process?

Service and product life cycles are getting shorter and shorter. This is what basically is pushing businesses towards cloud computing and predictive analytical solutions across industries. Cloud computing provides immediate insights and better responsiveness and predictive analytics helps to predict future issues and any changes that can come into the supply or demand cycle. This essentially answers, what is digitalization in supply chain management and what is predictive supply chain analytics. Now, we take a look at the benefits that come with it.

The benefits of cloud computing

There are obviously some major benefits to this process which as of now are not found anywhere else. The benefits are, as follows:

  • Cloud computing provides immediate insights into all the issues that can affect the chain directly. This, in turn, helps the managers to address the issues quickly and effectively. It also improves collaboration across all the sectors that come into play in the process. 
  • Once all the aspects are tied in the loop, predictive analytics will help the decision-makers with quick insights. With the help of ML and advanced statistical algorithms, it can help process big data to predict a likely outcome for the future. This includes all kinds of variables that have been gathered from different sources. 
  • Cloud computing also enables you to seamlessly add in different parties as needed at a moment’s notice. You can also adjust the provisions in this system immediately to fit the constant changes in demands or market trends. 
  • Digitalization will help to plan and execute things faster. This is supported by in-memory computing and big data analytics. This way you can improve your responsiveness to things that need immediate attention.

Best suited supply chain processes for cloud computing

There are some supply chain processes that are best suited for the cloud format, such as:

  • Sourcing and procurement: SaaS tools are best suited to create a collaborative format. 
  • Logistics: from inventory management, replenishment planning to world trade compliance, every process here can easily be turned cloud-based.
  • Manufacturing: this again can be cloudified very easily.
  • Planning and Forecasting: predictive analytics is evolving to offer better planning and forecasting options.

Conclusion

Supply chain management is evolving constantly. This is why it has opened a new horizon of job prospects. Check out Imarticus Learnings’ Supply Chain Management online course to gear up for it and give your career the spark it needs.

What is Business Analytics All About?

Posted on by Imarticus Learning

Business Analytics Definition

The importance of Business Analytics stems from the fact that it is the method by which firms analyze historical data using statistical methods and techniques to generate new insights and enhance tactical decision-making.

Since data-driven firms see their data as a business asset and actively seek methods to transform it into a competitive advantage, an increasing number of employees are taking data analytics and machine learning courses and acquiring a business analytics certification.

Data quality, trained analysts who understand the technology and the business, and a dedication to leveraging data to uncover insights that influence business choices are all essential components of business analytics success.

What is Business Analytics?

Business analytics is a data managing solution and a subset of business intelligence that involves analyzing and transforming data into valuable information, identifying and predicting outcomes and trends, and making better, data-driven business choices using methodologies such as data mining, predictive analytics, and statistical analysis.

The key elements of a conventional business analytics dash are as follows:

  • Data Visualization: For easy and rapid data analysis, visual representations such as charts and graphs are offered.
  • Optimization: after identifying patterns and making forecasts, firms may use simulation tools to test best-case scenarios.
  • Predictive Analytics: predicting business analytics uses a number of statistical approaches to building predictive models that extract data from datasets, discover trends, and offer a score for a variety of organizational results.
  • Forecasting: examines historical data from a given time period to make educated predictions about future occurrences or behaviors.
  • Association and Sequence Identification: identifying predictable behaviors that are conducted concurrently or sequentially with other acts
  • Text Mining: examines and organizes huge, unstructured text collections for quantitative and qualitative analysis 
  • Data mining for business analytics: data mining for business analytics sifts through large datasets to uncover patterns and connections using databases, statistics, and machine learning.
  • Data Aggregation: data must be acquired, structured, and filtered before being analyzed, whether through provided transactional records or data.

Why is business analytics important?

Business analytics has a lot of moving pieces, but it’s not always evident why it’s vital to your company. To begin with, business analytics is the instrument that your company requires in order to make informed judgments. These decisions are likely to have an impact throughout your whole organization, assisting you in increasing profitability, market share, and possible shareholder returns.

There’s no doubting that technology has an influence on many enterprises, but when utilized appropriately, BA may have a beneficial impact on your business by giving you a competitive advantage in a variety of ways.

While some firms are unclear what to do with vast volumes of data, business analytics combines data with actionable insights to help you make better business decisions.

Furthermore, because this data may be provided in any manner, your organization’s decision-makers will be well-informed in a way that suits them and the objectives you set at the start of the process.

Conclusion

If you are aware of the importance of Business Analytics and are interested in obtaining a business analytics certification, then you should subscribe to our data analytics and machine learning course given at Imarticus.  

Related Article:

https://imarticus.org/what-are-the-benefits-of-business-analytics/

An introduction to neural networks: AI/ML for beginners

Posted on by Imarticus Learning

The field of AI and machine learning is overgrowing, with new advancements in algorithms happening nearly every day. One area with a lot of growth recently is neural networks, which are artificially intelligent systems built on an architecture inspired by the human brain. In this post, we will explore what precisely neural networks are and how they work so you can get started today!

What is a neural network?

Neural networks are machine learning algorithms that you can use to recognize objects in pictures or understand human speech. 

For example, imagine you wish to teach a convolutional neural network how to recognize pictures of cats. You might show the computer thousands of examples of what cats look like and let it learn from that data. Then, when somebody shows the computer a picture that isn’t a cat, it could determine whether or not this is an image of something else using its knowledge of cats.

A step-by-step tutorial on how to train the convolutional neural network and make predictions:

 

  • Choose your dataset:

 

The first step is choosing a dataset to train your neural network. It could be a data set of images, text, or anything else you want to predict.

 

  • Preprocess the data:

 

Before starting training your neural network, you need to preprocess the data. It includes cleaning and formatting the data to be ready to be used by the deep neural network.

 

  • Choose your model:

 

The next step is to choose a model for your neural network. There are many different models, so you need to choose one that will work best for your dataset.

 

  • Train the model:

 

Now it’s time to train the network. It is where you will feed in your data and let the neural network learn from it.

The future of AI/ML:

AI/ML is becoming more widely used today. AI/ML has many benefits for the world around us. Machine learning help diagnose diseases, drive cars and even write music!

  • Websites like Amazon use AI/ML to recommend products you may like based on what you have bought in the past.
  • Facebook uses AI/ML to determine which posts or status to show first in your newsfeed.
  • Google uses AI/ML to generate search results.

The possibilities are endless, and the future of AI/ML is inspiring!

Discover Artificial intelligence and machine learning course with Imarticus Learning

This Artificial intelligence and machine learning course is by industry specialists to assist students in learning real-world applications from the ground up and building sophisticated models to offer helpful business insights and forecasts. This AIML course is for recent graduates and early-career professionals (0-5 years) who want to further their careers in Data Science and Analytics, the most in-demand job skill.

Course Benefit For Learner: 

  • Students get a solid understanding of the fundamentals of data analytics and machine learning and the most in-demand data science tools and methodologies. 
  • Learn data science skills by participating in 25 in-class real-world projects and case studies from business partners. 
  • Impress employers & showcase skills with artificial intelligence courses recognized by India’s prestigious academic collaborations.

Which languages should you learn for data analytics?

Posted on by Imarticus Learning

Data science is a fascinating topic to work in since it combines high statistical and mathematical abilities with practical programming experience. There are a variety of programming languages in which a prospective data scientist might specialize.

In this article, we will tell you how by learning machine learning and taking a python course you can obtain a Data analytics Certification. 

big data analytics courseWhile there is no one-size-fits-all solution, there are various factors to consider. Many factors will determine your performance as a data scientist, including:

  • Specificity: When it comes to sophisticated data science, re-inventing the wheel each time can only get you so far. Master the numerous packages and modules available in the language of your choice. The extent to which this is feasible is determined by the domain-specific packages that are initially accessible to you! 
  • Generality: A smart data scientist will be able to program in a variety of languages and will be able to crunch statistics. Much of data science’s day-to-day job is locating and processing raw data, sometimes known as ‘data cleaning.’ No amount of clever machine learning software can assist with this. 
  • Productivity: In the fast-paced world of commercial data science, getting the work done quickly has a lot of appeal. This, however, is what allows technical debt to accumulate, and only rational procedures may help to reduce it.
  • Performance: In some circumstances, especially when working with enormous amounts of mission-critical data, it’s crucial to maximize the performance of your code. Compile-time languages are often substantially quicker than interpreted languages and statically typed languages are far more reliable than dynamically typed languages. The clear trade-off is between efficiency and productivity.

These can be viewed as a pair of axes to some extent (Generality-Specificity, Performance-Productivity). Each of the languages listed below can be found on one of these spectra. 

Let’s look at some of the more popular data science languages with these key ideas in mind. What follows is based on research as well as personal experience from myself, friends, and coworkers – but it is by no means exhaustive! Here they are, roughly in order of popularity:

    • R: R is a sophisticated language that excels in a wide range of statistical and data visualization applications, and it’s open-source, which means it has a vibrant community of contributors. Its current popularity is a reflection of how effective it is at what it accomplishes. 
    • Python: Python is a fantastic language for data research, and not only for beginners. The ETL process is at the heart of most of the data science processes (extraction-transformation-loading). Python’s generality is appropriate for this task. Python is a tremendously interesting language to work with for machine learning, thanks to libraries like Google’s Tensorflow.
    • SQL: SQL is best used as a data processing language rather than as a sophisticated analytical tool. Yet ETL is critical to so much of the data science process, and SQL’s endurance and efficiency demonstrate that it is a valuable language for the current data scientist to grasp. 
    • Java: There are several advantages to studying Java as a primary data science language. Many businesses will value the ability to easily incorporate data science production code into their existing codebase, and Java’s performance and type safety will be significant benefits. However, you won’t have access to the stats-specific packages that other languages provide. That said, it’s worth thinking about, especially if you’re already familiar with R and/or Python.

 

  • Scala: When it comes to working with Big Data using cluster computing, Scala + Spark are wonderful options. Scala’s characteristics will appeal to anybody who has worked with Java or other statically typed languages. However, if your application doesn’t deal with large amounts of data, you’ll likely discover that adopting alternative languages like R or Python will increase your productivity significantly.

 

Conclusion

At Imarticus we commit to giving the best quality education, so if you are interested in getting a data analytics certification, taking a python course, and learning machine learning come and visit us! 

Related Article:

https://imarticus.org/what-are-top-15-data-analyst-interview-questions-and-answers/