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Category: Analytics

Understanding the Differences between Linear & Logistic Regression

Posted on by Imarticus Learning

Linear regression is one of the most common types of statistical models used in data prediction. It’s easy to use, and it assumes that a straight line can express a relationship between two variables. Logistic is another type of statistical model that also makes predictions.

Still, it does so using a logarithmic function rather than a linear function. This post will explain how these two methods differ and give you some practical applications for them both.

What is Linear Regression?

The linear discriminant analysis uses when predicting one variable based on another. This model assumes that if the slope of the line changes, so does the value of the predictions. It fits a straight line to your statistics using an ordinary least squares method. If you have more than two variables, consider using multivariate instead.

What is Logistic Regression?

Suppose you have an extensive collection of data that includes three different variables, one of which is the outcome you’re trying to predict (label). Using Linear might not make sense if your result isn’t directly related to the other two variables (independent variables or features).

Logistics can easily handle non-linear relationships between the label and the elements. For example, if you’re trying to predict if an email is a spam or not. The probabilities generated by logistics can work even if your features are unrelated to each other.

The Differences between Linear & Logistic Regression

LINEAR REGRESSION LOGISTIC REGRESSION
It is a simple and robust method based on linear algebra. It is one of the most well-known machine learning algorithms.
It assumes that the relationship between features and the outcome is linear, making it ideal for modeling or analyzing existing relationships in data. It is for binary classification problems, but you can also use it to solve multi-class classification problems by using an approach called “one versus all.”
You can, however, add interactions between variables to create non-linear models such as quadratic functions. You can not add interactions between variables to create non-linear models.
Unlike logistic, linear doesn’t require the outcome to be known beforehand.

 

 Logistic gets considered a supervised algorithm because you must provide the predicted (outcome) label beforehand.
 It is excellent at handling these events because it only cares about the relationship between features and outcomes. Logistic, on the other hand, is designed for discrete data (categorical or binary). It can’t handle these cases effectively because of how the sigmoid function works.

 

The table summarizes some of the critical differences between linear and logistic. Notice that, like all machine learning models, both algorithms are affected by outliers or data points outside the normal range (tails) that generate significant errors.

Explore and learn with Imarticus Learning

Learn the fundamentals of data analytics and machine learning and the most in-demand data science tools and methods to become job-ready. With Tableau, a student can learn Python,  SQL, Data Analytics, Machine Learning, and data visualization. The curriculum includes Capstone Projects, real-world projects, relevant case studies, and mentoring to assist students in becoming a master in their fields.

Some course USP:

  • Data science courses in India aid the students in learning job-relevant skills that prepare them for an exciting data scientist career.
  • Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations.

World-Class Academic Professors to learn from through live online sessions and discussions. It will help students understand the 360-degree practical learning implementation with assignments.

Contact us through the live chat support system or schedule a visit to Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon training centers.

How Data Scientists Make Data-Driven Decisions using Logistic Regression

Posted on by Imarticus Learning

A data scientist is a person who uses statistics and information technology to analyze data, identify patterns, and generate insights. They use sophisticated algorithms for these purposes. This blog post will cover logistic regression and how to apply it to your business problems effectively!

What is Logistic Regression?

It is a machine learning algorithm that means that the results are learned from a training set and used to make valuable predictions about unseen data. In the case of regression, those predictions are probabilities.

Some Challenges of using Logistic Regression?

The regression and other algorithms can be challenging to interpret and may only provide a probability between 0 and 1.

For instance, if we feed in a set of data for people who have been diagnosed with cancer, the algorithm will learn which variables are most important for predicting that diagnosis.

However, it will give us an output representing the probability that a patient has cancer. This number does not necessarily mean that the person has or doesn’t have cancer — it is simply the probability we can use to make an informed decision.

How is data science used to make a data-driven decision?

One of the most significant impacts data science has today can be seen in its use as a tool for business decision-making.

Predictive modeling and regression are two popular techniques that many companies have adopted across all industries because they empower businesses to make more accurate decisions, resulting in greater efficiency.

Logistic does this by taking historical data and learning which variables are most helpful in making predictions.

The future of Data Science?

  • The future of data science lies in developing new techniques that can build on these existing methods while overcoming their limitations.
  • It also depends on working effectively with vast volumes of data from various sources, such as sensors, images, and video.
  • One of the most talented research areas is the development of techniques that can learn on their own – without being fed historical data to train them first.

Explore Data Science with Imarticus Learning

Corporate experts gave input for this postgraduate program to help students master actual Data Science applications. Be it grounding up or constructing solid models to provide meaningful business insights and forecasts. This course helps you learn all. The program is for early-career workers (0-5 years) who dream of growing and building data science careers.

With this program’s employment guarantee, the student may take a significant step forward in their career. After satisfactorily finishing the program, students assure interview chances.

Some course USP:

  • Data science courses in India aid the students in learning job-relevant skills that prepare them for an exciting data scientist career.
  • Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations.
  • World-Class Academic Professors to learn from through live online sessions and discussions.
  • The program helps students understand the 360-degree practical learning implementation with assignments.

Contact us through the live chat support system or schedule a visit to Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon training centers.

Why do organizations emphasize on delivering business value through efficient procurement and logistical management?

Posted on by Imarticus Learning

Looking for the best logistics and supply chain management course on the block? Before you choose the supply chain Management course that suits you, understand why efficient procurement and logistical management are vital for organizations in the first place.

Procurement and logistics are two of the most common terms that you will hear in a business environment. While procurement refers to sourcing supplies, logistics means the holistic process of acquiring resources from their origin, managing them, and transporting them to the point of consumption.  

But why do organizations emphasize procurement and logistics management to deliver business value? Let’s find out.

Why is Procurement Management Important?

Procurement management entails overseeing all the processes involved in sourcing the materials, products, goods, and services required for business operations to run efficiently. The terms “purchasing,” “sourcing,” and “procurement” are often used interchangeably to describe the procuring of supplies and management of the process. However, it is “procurement” and “purchasing” that essentially refers to the operational function, while “sourcing” is usually more strategic.

Procurement management has a direct impact on strategic business operations and the company’s bottom line for the following reasons:

  • Procurement management ensures the proper acquisition of items and services so that processes and projects can proceed efficiently.
  • Procurement management can be instrumental in sealing valuable production and supplier contracts.
  • It can also set the stage for new and innovative processes and help domestic and local businesses go global.
  • When optimized, procurement management can save time, money, and resources while offering a competitive advantage.
  • By actively pursuing diverse suppliers, procurement management can be at the forefront of trailblazing CSR initiatives to promote diversity and inclusion.

Why is Logistics Management Important?

What earlier used to be a term restricted to military operations has now become a widespread business concept. Today, the business sector uses the term logistics to describe the efficient flow, storage, and management of goods from their source to the point of consumption.

Here’s how logistics management is critical to stay competitive and keep pace with customers’ demands:

  • Managing logistics significantly reduces overhead costs, increases productivity, and ensures faster delivery. 
  • Logistics is the foundation of a successful supply chain. It helps boost the sales and profit of businesses dealing with the production, warehousing, shipment, and delivery of products. 
  • The analysis of a company’s supply chain management data improves efficiency. Using a transportation management system to track the movement of goods in and out of a business optimizes the process and minimizes disruptions. 
  • Well-managed and organized logistics ensure fast and seamless product delivery to customers. 
  • Last but not least, efficient logistics management improves customer service, meeting and exceeding customer demands. It enhances the brand image and helps the business attract and retain long-term customers.

What to Look For in a Logistics and Supply Chain Management Course?

If your goal is to get a professional certification in Supply Chain Management and Analytics, there are several online courses that you can choose from. While each of them claims to be the best, ensure that the program caters to your professional needs.

For example, the course curriculum should cover key programming languages and tools such as Python, Excel, and TOra, and hands-on training in real-world SCM projects. Overall, the course must be robust enough to prepare you for the operations and supply chain industry with a cutting-edge curriculum and industry exposure.

Why artificial intelligence and machine learning is the new blue print for the data science industry

Posted on by Imarticus Learning

Data science is a broad discipline concerning data systems and processes with the aim to maintain data sets and derive insights from them. On the other hand, Artificial Intelligence (AI) pertains to mathematical algorithms that can replicate human thought processes to understand complex relationships, plan for the future and make actionable decisions.

Machine Learning (ML), on the contrary, helps to implement AI by “training” computers to solve various tasks. Data science incorporates several areas of artificial intelligence and machine learning while primarily focusing on gaining insights from data.

But, how are these three fields related, and what is the impact of AI and ML in shaping the data science industry? Let’s find out.

Data Science, AI, and ML: Where Lies the Difference? 

Data science finds widespread use in several businesses to improve production processes, innovation of product design, and enable strategic planning. It involves techniques of mathematics, statistics, computer science, and even ML to extract knowledge from data and provide insights and decision paths. 

On the contrary, AI enables computers to observe their environment and make decisions based on what they observe. Some of the most widespread uses of AI include processing clinical data, creating chatbots and smart assistants, and financial planning. Add the machine learning component, and AI can enable computers to solve new problems such as classification and predictions.

The fields of data science, AI and ML overlap significantly and yet have subtle differences. In a nutshell, data science gives insights, AI produces actions, and ML facilitates predictions.

The Combined Effect of AI and ML on Data Science

Data science and data analytics have long been revolutionizing the business landscape. Companies that have mastered their use of data science and analytics aim to delve deeper into data to increase efficiency, boost their bottom lines and gain a competitive edge.

Thus, they are looking to incorporate AI and ML into their data infrastructure to achieve business goals. For instance, call centers have long been using conversation analytics software, platforms that leverage AI and ML to gain better data insights. 

Following are a few more examples to show how AI and ML combined with data science make a remarkable difference to organizations:

  • Conversational AI systems such as chatbots and smart assistants engage in highly interactive conversations with customers and users and capture actionable user insights in the process. 
  • Predictive analytics applications enable the analysis of dynamic datasets to make financial predictions, forecast business trends, customer behavior, etc. 
  • Hyper-personalization systems enable customized offerings to customers, such as product recommendations, targeted advertising, personalized medical care, and financial planning. 
  • Also, organizations can consistently respond to evolving threats, thanks to anomaly detection systems that leverage the potential of adaptive fraud detection and cybersecurity processes.

The business value of data science alone cannot be understated. However, integrating it with the tools and techniques of AI and ML has way more potential to produce actionable insights from the ever-expanding data pool. In conclusion, AI and ML have been impacting the data science industry for a long time and will continue to do so in the foreseeable future with even more ground-breaking innovations.

How to Learn AI?

Looking for an artificial intelligence course? There are several AI and ML courses available online with extensive coverage of data science, ML, NLP, deep learning, and computer vision. But before you settle on a course, ensure that the curriculum offers practical learning through real-world projects, has scope for ample industry exposure, and provides a globally recognized certification after course completion.

Why Linear Regression is Important for Data Scientists & How to Learn It?

Posted on by Imarticus Learning

Linear regression is a powerful predictive modeling technique that enables the statistical analysis of continuous variables. It is the most popular technique for estimating relationships between inputs and outputs.

This post discusses linear regression, how to use it in data science, and why you need to know about it as a professional data scientist.  Now let’s dive into the topic!

What is Linear Regression?

We start this section by defining linear regression. Here, in simple words, it is an approach to estimate the relationship between the input and output. It simplifies the modeling process and produces more interpretable results. When you need to make predictions on new data, Linear discriminant analysis is a better option for making predictions on new data points (i.e., test set) because of its solid statistical foundation and mathematical proofs of performance guarantees.

Why is Linear Regression Essential for Data science?

For a Data Scientist, it is essential to know and understand the concept of linear regression and how to use it. This section provides some reasons why it is critical for data scientists:

When you don’t know which variables are important: In many real-world problems, no one tells you which input variable(s) affect the output variable. In cases where you have access to historical data, it is possible to find the relationship(s) between input and output variables (i.e., linear regression).

When your model needs linearity assumption: Incorporating nonlinearities in the prediction function requires complex modeling techniques like applying polynomial transformations or neural networks.

How can we use linear regression?

Here are some common scenarios where we use in the industry.

  • You can predict the price of a house/cars/robots etc., indicating loan eligibility for an individual based on his salary. How many items will you sell tomorrow? What time of the day am I likely to buy something?
    Estimating Expected Weight of a baby based on mother’s weight during pregnancy, Estimating the passengers who will purchase tickets for an airline, etc.
  • Now you can solve all these real-world problems with linear regression!
  • Linear regression is a beautiful yet straightforward statistical technique to estimate the relationship between input and output variables. In other words, it helps you to find a function that best explains the relationship between input and output variables.

Input features = house size, car speed, age of a person, flight duration, etc

Output variable = price of a house/car/flight ticket etc

Explore Data Science career with Imarticus Learning

Students can master the fundamentals of data analytics and machine learning and the most in-demand data science tools and methodologies. With Tableau, you can learn Python, SQL, Data Analytics, Machine Learning, and Data Visualization. With this program’s job assurance guarantee, students may take a significant step forward in their career.

Some course USP:

  • This Data science courses with placement assurance aid the students to learn job-relevant skills that prepare them for an exciting career.
  • Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations.
  • World-Class Academic Professors to learn from through live online sessions and discussions. It will help students understand the 360-degree practical learning implementation with assignments.

Contact us through the live chat support system or schedule a visit to training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon.

The future of artificial intelligence and machine learning in the Biosciences

Posted on by Imarticus Learning

Do you know why artificial intelligence courses are so popular? For the last 70 to 80 years, we have been trying to simulate our intelligence in many artificial entities, which has given rise to the growing field of artificial intelligence (AI). Although AI has surpassed humans in many respects, it still does not live up to its name. AI, as we define it, does not yet exist, nor is there a consensus among experts as to whether it can be achieved.

However, while AI is captivating with its incredible applications and rapid growth (autonomous cars, nanorobots, etc), AI has infiltrated almost all disciplines and has had a particular impact on biosciences. AI offers sufficient computational power and capacity to address the complexity of biological research through simulations (known as “artificial life”). It presents itself as an ideal testing ground, a bounded but unbounded environment where physical laws are adaptable, all parameters are traceable, measurable, storable and retrievable.

AI in Biology

This translates into the possibility of overcoming some of the most important challenges of research in biology. For example, the ethical limits of animal experimentation with drugs for cancer and other diseases, or the methodological difficulties in studying complex systems such as human language, multicellularity or collective intelligence. AI also benefits from this interaction. After all, the key to being able to reproduce a natural system in an artificial environment depends on the knowledge one has of the system in question.

Deep Learning

Deep Learning is one of the many approaches to AI and is inspired by the structure and functioning of the brain through the interconnection of neurons, mimicking the biological structure of the brain through algorithms called Artificial Neural Networks that specialise in detecting specific features, through different layers of neurons, to achieve unsupervised learning. The concept is given by the multiple layers it can comprise.

A neural network needs approximately 50,000 times more energy to function than the human brain. For this reason, computers with traditional architectures are not suited to support the parallel processing that the brain carries out so efficiently. Therefore, research is being carried out into brain-mimicking computing techniques called Neuromorphic Computing.

Artificial Immune Systems

There is an initiative that aims to understand how different parts of the brain work in order to diagnose and treat brain diseases and to develop neuromorphic computers that can learn in the same way as the brain does. These advances need to incorporate multidisciplinary knowledge from neuroscience research, psychology, and ICTs. But it is not only the human brain that is a source of inspiration. Artificial Immune Systems comprise computational methods based on the processes and mechanisms of the human immune system and are used for learning and protecting information systems from malware.

AI and IOT

Finally, we could compare the relationship between Artificial Intelligence and the Internet of Things as the relationship between the brain and the human body. Our bodies collect sensory information (sight, hearing, touch, etc) and send it to the brain, to make sense of this information in order to make the decisions and/or actions, sending signals back to our body if necessary, for example, to pick up an object.

Conclusion

In conclusion, the symbiotic relationship between AI and bioscience has provided the ultimate testing ground for solving some mysteries of biology, as well as the theoretical framework needed to achieve real artificial intelligence. Any of us can learn AI or do a machine learning certification, but only the best prepared will be part of this amazing field of study, so study with Imarticus and go as far as you want.

Different Ways Data Mining Helps Marketers

Posted on by Imarticus Learning

What is Data Mining?

Everything that we do leaves data behind. That data is ultimately used to extract useful information. Data mining is a technique of analyzing that large amount of data and making use of it in marketing strategies by determining the correlations and patterns among them. Budding industries tend to use data mining at a great advantage by using the information uncovered to target audiences accordingly.

It is highly advisable to have a data analytics certification through a data mining course to become a professional in this field.

Steps of data mining –

  1.     Extract and transform the information collected
  2.     Digitally store and manage the data
  3.     Analyze the data by using different data mining techniques
  4.     Display the data in a useful way, like in a graph or number chart

Frequently used data mining techniques

Data mining can turn out to be a highly effective technique if done correctly. Here are some basic techniques that are quite common:

  • Observe a pattern – Simple line graphs and bar charts can be used to represent the data collected so it becomes easier to recognize certain patterns and intervals in them.

  • Classification – Classification is a more detailed data analysis technique. It helps in classifying data further into groups or clusters for better analysis.

  • Association – This helps in finding a hidden pattern. This technique helps you unfold links between two different types of variables.

  • Regression – This technique is used to spot the relationship between different variables depending on the presence of another factor. It is a form of planning and modeling.

  • Outer detection – This technique is also known as an outlier technique or analysis that helps in finding variables in a data set or group that do not match the pattern.

  • Prediction – As its name suggests, this technique helps in analyzing past or similar data to predict future patterns or events.

Benefits of data mining

Data mining can add great value to a business that is why marketers interested in growing a business must understand its benefits.

  • Marketing campaigns are more optimized –. Data mining is a great way to analyze marketing campaigns and which methods will help in generating more engagement and leads.

  • Helps in making better business decisions – The information and insights gathered from data mining can help you make better decisions for your business.

  • It will help you detect any kind of fraud – Data mining in business can help in detecting any kind of fraud or potential fraud like fake money transactions.

  • Improves external market performance – It helps to understand and predict employee behavior which in turn helps in improving external market performances.

Data mining is the future

Data analysis and management is a huge source of employment even today. Data mining professionals can work individually or as employees of a company to evaluate important information and discard the ones that are not reliable.

This requires detailed knowledge of understanding data. This knowledge can be acquired through data mining courses in India that are available online. Since every company is going digital now, reliable data analysts will be in a lot of demand so it is better to start now.

Understanding Linear Discriminant Analysis in Python for Data Science

Posted on by Imarticus Learning

When we are working with more than two classes in data, LDA or Linear Discriminant Analysis is the best classification technique we can use. This model provides very important benefits to data mining, data retrieval, analytics, and Data Science in general such as the reduction of variables in a multi-dimensional dataset.

This is very useful for minimizing the variance between the means of the classes while maximizing the distances between the same. LDA removes excess variables while retaining most of the necessary data. This is extremely crucial for Applied Machine learning and various Data Science applications such as complex predictive systems.

What is Linear Discriminant Analysis?

LDA is a linear classification technique that allows us to fundamentally reduce the dimensions inside a dataset while also retaining most of the crucial data and utilizing important information from each of the classes. Multi-dimensional data contains multiple features that have a correlation with other features. Using dimensionality reduction, one can easily plot multidimensional data into two or three dimensions.

This also helps make data more cognizable for non-technical team members while still being highly informative (with more relevant details). LDA estimates the probabilities of new sets of inputs belonging to each class and then makes predictions accordingly.

Classes with the highest probability of having new sets of inputs are identified as the output class for making these predictions. The LDA model uses Bayes Theorem for estimating these probabilities from classes and data belonging to these classes.

LDA allows unnecessary features that are “dependent”, to be removed from the dataset when converting the dataset and reducing its dimensions. LDA is also very closely related to regression analysis and analysis of variance. This is due to all of their core objectives of trying to express individual dependent variables as linear combinations of other measurements or features.

However, Linear Discriminant Analysis uses a categorical dependent variable and continuous independent variables. Unlike different regression methods and other classification methods, LDA assumes that independent variables are distributed normally. For example, logistic regression is only useful when working with classification problems that have two classes.

How is LDA used in Python?

Using LDA is quite easy, it uses statistical properties that are predicted from the given data using various distribution methods such as multivariate Gaussian (when there are multiple variables). Then these statistical properties are used by the LDA model for making predictions. In order to effectively use the LDA model or to use Python for Data Science, one must first employ various libraries such as pandas, matplotlib, and numpy.

First, you must import a dataset such as the ones available in the UCI Machine Learning repository. You can also use scikit-learn to import a library more easily. Then, a data frame must be created that contains both the classes and the features.

Once that is done, the LDA model can be put into action, which will compute and calculate within the classes and class scatter matrices. Then, new matrixes will be created and new features will be collected. This is how a successful LDA model can be run in Python to obtain LDA components.

Conclusion

Linear Discriminant Analysis is one of the most simple and effective methods for classification and due to it being so preferred, there were many variations such as Quadratic Discriminant Analysis, Flexible Discriminant Analysis, Regularized Discriminant Analysis, and Multiple Discriminant Analysis. However, these are all known as LDA now. In order to learn Python for Data Science, a reputed PG Analytics program is recommended.

Looking to Upgrade Your Career in Supply Chain Management and Analytics? Find Your Path Here

Posted on by Imarticus Learning

Supply chain management is a useful process for any organisation that wants to boost its ROI (Return on Investment). Businesses have to keep track of their services and products in the supply chain for more insights. With more insights, businesses can take better decisions related to the supply chain. To extract meaningful insights, data analytics is used by supply chain experts. If you want to upgrade your career in supply chain management, you can go for an online course that covers the analytical aspects. Read on to know more about building a career in supply chain management and analytics.

What is SCM?

SCM (Supply Chain Management) helps businesses in understanding the flow of goods and services. All the processes that convert the raw materials into finished goods come under the supply chain. SCM enhances the transparency of day-to-day business processes and you can make better decisions. When a firm knows the cost of its supply chain processes, it can make better decisions for slashing the costs. From the movement of products to the storage of raw materials, everything comes under the supply chain.

What are the pros of SCM?

The benefits of SCM in the current business scenario are as follows:

  • Supply chain management helps in meeting the supplies as per the customer demands. SCM helps a business in steering through market challenges and fluctuating economies.
  • You can enhance your inventory system with SCM and reduce damages in storing products/services.
  • With better SCM, a business can have better relationships with vendors and distributors. Better communication between different points of the supply chain adds to overall productivity.
  • You can gain insight into day-to-day operational costs incurred by your organisation with better SCM. Cutting overhead costs is one of the major advantages of SCM.
  • With SCM, you can reduce the delays in your business processes. Customers can receive finished goods and services in less time with better SCM.
  • You can optimise the space at your sales outlets with better SCM. You can highlight products according to the demands of customers in your sales outlets.

Due to the numerous benefits of SCM, firms need expert individuals who can enhance their overall productivity. Firms are hiring individuals who have completed a supply chain management course for better results. At present, data analytics is being used heavily for the supply chain management. With data analytics, you can know the demands of the customers and can forecast market situations.

What are different job roles in supply chain management?

Job roles in the supply chain management industry are as follows:

  • Production Manager: It prepares the staff schedules and ensures all equipment is working fine.
  • Supply Chain Manager: A supply chain manager interacts with vendors and distributors to ensure continuous supply/sale.
  • Logistics Manager: The logistics manager is concerned with the supply of products/services to the customer.
  • Purchasing Manager: Major purchase contracts are overseen by purchasing managers in respective firms.

How to learn SCM and analytics?

Young enthusiasts can go for the Professional Certification in Supply Chain Management & Analytics offered by Imarticus Learning. This supply chain management online course is sponsored by IIT Roorkee, one of the premier educational institutions of the country. You get to learn from industry experts and faculty members of IIT Roorkee. Not to forget, this course provides hands-on learning with numerous real-world SCM projects.

Conclusion

At the end of the IIT supply chain management course, Imarticus will offer extensive placement support. You can kickstart your career in the industry with an online course. Working professionals can also uplift their careers by completing an SCM online course. Start your IIT supply chain management course now!  

Is Python Difficult to Learn?

Posted on by Imarticus Learning

The average pay of a software programmer in India goes up to INR 10 lakh per annum. To start your professional journey in the tech field, you will need to learn a programming language.

In this article, we will look at one of the most recommended programming languages for beginners and professionals alike. You will also discover why you should learn Python and how much it is easier.

Learn PythonWhat is Python?

Python is one of the most popular programming languages in the world. It is a high-level general-purpose programming language and entered the market in 1991. Since its arrival, it has become an industry staple.

Programmers use Python in numerous industries, such as entertainment, healthcare, software, and finance. Due to its vast popularity, you will find it in the curriculum of most data science courses in India.

Why you should learn Python?

There are many reasons why you should learn this programming language. Here are some of them:

Easy syntax

If you are familiar with English, then learning Python is quite easy for you. One of the many reasons behind Python’s widespread popularity is its simple syntax. Many of its commands are derived directly from English, hence you can learn the fundamentals of programming along with learning the fundamentals of Python.

Pro tip: If you are a beginner, then you should look for an online data science course in India that covers Python. It will be much easier to learn programming through such a data science course in India.

High demand

Python ranked third in Stack Overflow’s 2020 Developer Survey. For languages that developers aren’t using but want to learn, Python took the top spot for the fourth year in a row.

Most Python developers are programmers, software engineers, team leads, DevOps engineers, and data science professionals. The demand for Python expertise is substantially high among companies, which is another prominent reason why you should learn this programming language. It is also very reliable and hence, has high popularity in the IT world.

Versatile application

Due to its vast collection of libraries, Python finds applications in various sectors, including data science, artificial intelligence, machine learning, app development, and others.

This means you can use a Python programming course to develop a mobile application as well as create a machine learning algorithm. It allows you to focus on an application’s core functioning while taking care of the common programming tasks.

Learn PythonHow to learn Python?

You know that it would be great to learn this language but where can you start. Well, the best way to start learning Python is by joining an online data science course in India. Data science courses teach you the basics and advanced applications of Python along with many related technologies.

After all, learning Python alone would not be sufficient in this cut-throat competition-ridden world. Joining a data science course in India would help you in multiple other ways. First, data science courses provide you with a step-by-step structure so you can plan your studies accordingly.

You can always check your data science course details to see how much progress you have made. On top of that, if you want to start a tech career, you can join a data science course with placement support. A data science course with placement support will help you kickstart your career right after you complete the program.

Before picking a program, be sure to check the data science course fees and other data science course details.

Learning Python is quite simple. Its easy-to-learn syntax and versatile applications make it a perfect programming language for a beginner. If you are interested in learning more about data science courses in India, then you can check out the programs offered by Imarticus.