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Tag: How to become a data Scientist?

Is an engineering background essential to becoming a data scientist?

Posted on by Imarticus Learning

Data science is one of the most popular career options at present. It is one of the rare fields that offer a combination of both exciting and challenging job profiles. What is best about data science is that it does not restrict. Whether you’re from a science, commerce, or arts background, if you’re good at programming and statistics, you can become a data scientist.

Now coming to the big question, is an engineering background essential for becoming a data scientist? The answer is no, it is not at all necessary to have an engineering background if you want to be a data scientist. 

Although being from a technical background gives a lot of edges, this job profile doesn’t necessarily ask for a degree from a particular stream, be it engineering or some other. Instead, data science recruiters always look for candidates which have strong problem-solving and analytical skills along with a good knowledge of programming languages such as R, Python, Java, AWS, etc. 

How to Become a Data Scientist Without Engineering? 

If you’re not from an engineering background and want to pursue a career in data science, then it is definitely possible to do so. The job of a data scientist doesn’t discriminate on the basis of educational background and degrees that one has. In fact, an engineering degree is of no relevance in the field of data science unless it is computer science. 

Below are some tips that will help you set your foot in the data science industry and build a successful data scientist career:

Have a Basic Understanding 

The very thing you should do is have a basic knowledge of what exactly is data science, what skills it requires, and what job profiles it offers. Here are some common streams in data science:

  • Business Analyst 
  • Data Analyst 
  • Data Engineering 
  • MIS Reporting Executive 

A good idea is to learn about all the job roles and profiles in this sector and then try to figure out what roles you’re interested in. It is recommended to have a clear mindset of the industry or business sector that you want to work with, especially when you don’t belong to a technical background. 

Learn Programming Languages and Mathematics 

When you finally decide to switch your career path, then the first skills you need to acquire to become a data scientist include programming languages and mathematics. 

If you haven’t done engineering, then learning mathematics and programming languages can sound a bit tough at first, but always remember that nothing is too difficult to learn. You can easily gain these skills with practice and the right guidance. 

Programming languages such as Python, SQL, R, etc. are the prerequisites in data science. And believe it or not, these languages are much more comfortable and easier to learn than other languages like Java, C, and C++.

However, remember that learning only the programming languages is not enough. You also have to brush up on your mathematics and learn statistics very thoroughly. 

Take Data Scientist Course 

If you don’t know where to get started, then the best option is to take the right data scientist course. The best course from a reputed institute can help you learn data science online in the most effective and convenient way. 

Practice with Real-world Projects 

Once you’ve enough knowledge of mathematics and programming, you need to start practicing with real-world projects to enhance your practical knowledge. Always remember, data science doesn’t require any bookish knowledge, it requires the candidates to be exceptional at problem-solving, instead. 

Working on real projects not only helps you gain more expertise but also helps you build a strong portfolio to get a good job. 

Be a Part of the Data Science Community

Always keep looking for conferences, events, and summits related to data science. By attending these events, you get a great chance to network along with an opportunity to learn more about the field. Not just offline, you can join such events online as well. Furthermore, for networking, a good idea is to stay active on platforms like LinkedIn. 

Finally, Prepare to Face the Interviews

Once you’ve understood data science, acquired the right skills, and taken the right course, you can start applying for data science jobs and prepare for the interviews. Build your resume, write cover letters, and start contacting the network you’ve built by highlighting the projects you’ve worked upon. 

Conclusion

Is an engineering degree necessary for becoming a data scientist? The short answer is, NO! 

Anyone from any field, be it science, commerce, or arts can become a data scientist. More than the educational background and degree, data science recruiters look for the right skills such as programming and mathematics. By gaining great proficiency in these skills, you can definitely build a successful data scientist career. We hope the information provided in this article helps you in taking the right direction! 

How to Excel in Data Science?

Posted on by Imarticus Learning

Data science has been growing and has infiltrated everyday life, even if sometimes we are not aware of it. To excel in this discipline that is becoming so popular there are several things you can do, and you should know that the first of these is not necessarily to learn data science although it is of course on the list. It is very common that when you go to a website you will be recommended products that might also be of interest to you. 

Or when you search about something, the search engine completes the sentence for you or makes a suggestion. All this is driven by data science, but do you know what data science is? Do you understand what it means? Do you know where data science is applied? Understanding this is the first step to becoming an excellent data scientist followed of course by a good data science certification course. 

What Is Data Science?

Data science is first and foremost the discipline of making data useful. Above all data science has become a new approach to problem-solving and strategizing. Although the computing power of today’s computers and data centers is an element without which Data Science would not have much scope, we are dealing with a discipline where other areas of knowledge converge that cannot always be clearly defined.

Several subfields include mining large amounts of information, making decisions based on limited information, and using patterns to automate tasks. Each subfield encompasses a science or technology, and it is important to understand the differences, 

 

  • Analytics

 

Analytics allows for the analysis of all types of data in real-time, historical, unstructured, etc. Above all, it is the process of examining data sets to find trends, hidden patterns, correlations, and conclude the information extracted. It is now possible to analyze data and get answers almost immediately, which is not possible with traditional solutions.

 

  • Statistics

 

Statistics is mainly concerned with putting data in order and analyzing it to obtain predictions and forecasts about specific phenomena. It is made up of methods, procedures, and formulas that enable relevant conclusions to be drawn. Its main objective is to improve the understanding of information. 

 

  • Artificial Intelligence

 

Thanks to machine learning, artificial intelligence can process massive amounts of data, which we as humans could never do. It also refines models through algorithms and predictive analytics, allowing machines to perform activities that we can consider intelligent on their own.

In short, data science employs a variety of technologies and methods to process and analyze data. The important thing to become excellent at data science is to find a good data science certification course. By finding a course that fully exploits your capabilities and develops your skills you can become an excellent data scientist.

Why Study Data Science?

If you learn data science you will make you part of the changing world. You will develop skills in computer science, programming, statistics and learn how to analyze and use the information to solve problems and develop strategies. There are many sources of learning but not all of them will suit your needs and those of the market. Our data science courses are designed by industry experts so you will learn real-world applications to generate useful solutions.  

The program Post Graduate Program In Data Analytics & Machine Learning is designed for those looking to build their career in data science especially for recent graduates and early career professionals. The Data Science courses will go a long way to ensuring that you become an architect of your future. Seeing the increasing demand for the application of this discipline, it is logical to expect an increase in the demand for data science professionals.

How to become a data scientist by optimizing your career

Posted on by Imarticus Learning

In 2021, many businesses have transformed themselves digitally. Businesses are interacting with customers on digital channels. The digital channels are also helping businesses to provide services round-the-clock to customers. Business organizations have never cared about their IT infrastructure more than now. With accelerating digital transformation, the amount of data produced by business organizations is also increasing.

There is a need for skilled data scientists in the industry that can perform high-end data analysis and extract insights. It is why many young aspirants want to learn data science and secure their careers. Read on to the best way to learn data science and secure your career. 

Roles of data scientists in 2021

Before you go on a job hunt in the industry, you should know about the roles of a data scientist. Some of the tasks performed by a data scientist in 2021 are as follows:

  • Data scientists must collect data from various sources to perform analysis. In starting, data scientists collect unstructured data that makes no sense. However, the unstructured data contains many meaningful insights that will be uncovered after analysis.
  • Once data is collected from different sources, it needs to be cleaned and classified. Redundant data points or outliers need to be removed for high-end data analysis. At present, data scientists use many analytics platforms for faster data cleaning and classification.
  • A data scientist also performs exploratory data analysis to identify the main characteristics of a data set. Via exploratory data analysis, data sources are manipulated to get the answers needed.
  • Data scientists are also involved in developing better analytics models. Analytics models run on algorithms are performing data analysis without any manual support. Data scientists also use new-age technologies to develop better analytics models.
  • Data scientists often collaborate with other IT teams to find loopholes in the IT infrastructure. With analytics results, data scientists try to implement methods that could drive business performance.
  • Data scientists also make data accessible for everyone in a business organization. They represent complex data via several visualization techniques. It makes data easier to understand by every employee of a business organization.
  • Business organizations rely on data scientists to find trends and patterns among data sets. Those patterns are then used to prepare market forecasts and demand forecasts.

Skillset required for becoming a data scientist in 2021

A data science online course can help you in acquiring the competencies required by a data scientist. The skills required by data scientists in 2021 are listed below:

  • Data scientists need to be fluent in coding languages like Python, Java, SQL, and MATLAB. A reliable data science online course can help learn the coding languages used by data scientists.
  • Data scientists need to have a sense of the current business landscape in which they are working. Many firms spend funds on data science training with business in focus for their employees.
  • Analytical and data visualization skills are necessary for a data scientist. Young aspirants can undergo data science training to learn data mining, munging, visualization, and reporting.

Which is the best course to learn the skills needed by a data scientist?

Imarticus provides the best data science certification courses for young aspirants. It also offers a PG Program in Data Analytics & ML for working data scientists. Its Data Science Prodegree is also popular among aspirants in India. 

All the data science courses offered by Imarticus Learning follow an industry-endorsed curriculum. The industry-oriented curriculum focuses on teaching industry practices to job seekers. By learning techniques and practices used by data scientists, you will be job-ready. Start your data science certification course to become a successful data scientist!

Tools Data Scientists Use to Make Precise Predictions

Posted on by Imarticus Learning

It is no secret that the accuracy of predictions in the business world can make or break a company. Data scientists create these accurate predictions to help businesses understand what will happen and prepare for it. It’s not easy, but data science has many tools that can make this process easier. In this blog post, we’ll explore some of those tools and how they work!

Tools data scientists use to make precise predictions:

Predictive analytics algorithms help data scientists predict future events and behaviors by using existing data. These tools build mathematical models that capture the connection between demographics, location, time of day, etc., and measurements such as the number of web visits or revenue.

One type of algorithm is a decision tree, a set of rules used to classify things. For example, if the weather is sunny and warm, there’s an 80 percent chance it will be hot outside. Still, if the weather is rainy or cool, there’s only a 30 percent chance it will be hot outside. A data scientist can use this information to determine an appropriate temperature for an office during a particular weather pattern.

Another type of algorithm is a random forest based on the same idea as decision trees but performs better in some cases. Random forests use when data scientists want to make accurate predictions with many different variables. The randomized process behind the tool helps ensure that each prediction is different from the last one.

Artificial neural networks (ANNs) are machine learning algorithms inspired by the neurons in our brains. They let computers complete tasks like recognizing images, handwriting recognition, and other forms of pattern recognition that machines can use to make predictions.

Support vector machines (SVMs) are another machine learning algorithm. These designs are for computer vision, which is the science of how computers can detect, receive, and process images. In a support vector machine model, there’s one variable being predicted from many different inputs. The goal of SVMs is to find a hyperplane that best separates the input data into two distinct sets.

Decision trees, random forests, ANNs, and SVMs are examples of algorithms that can make accurate predictions. These tools work well with large datasets; however, they require careful preparation and data feeding (known as “feature engineering”).

Explore and learn Data Science 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. Learn Python, SQL, Data Analytics, Machine Learning, and Data Visualization using Tableau. This PG program is for industry professionals to help students master real-world Data Science applications from the ground up. Therefore construct strong models to provide meaningful business insights and forecasts.

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.

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.

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.

A Complete Guide to Data Science, Artificial Intelligence and Machine Learning

Posted on by Imarticus Learning

Data science often referred to as the ‘oil of the 21st century can be simply defined as the subject dealing with the collection, storage, analysis, deployment, and prediction of data. It collects the clean information from the raw data of the user and uses it for actionable insights. It is also used in predicting certain events in the future. Scientists define it as another form of statistics and YES! IT IS.:-

best data science courses in India

Data science vs AI vs Machine Learning

Data science obviously has the upper hand when compared with artificial intelligence and machine learning. Indeed machine learning and AI is a subset of data science.

After all data science, machine learning, and AI are associated with each other to build the technology.

 By 2013, the total data created was 2.7 zettabytes which 9x times more than it was collected in the previous 92,000 years of humankind combined. And is 90% of entire world data has been created in just 2 years. YEP! That’s amazing.

And it is still growing at a rapid pace. By 2020, the total data created was 44 zettabytes and it is projected to a rise of 175 zettabytes at the dawn of 2025.

Processes in Data science:-

  1. Understanding Business problem
  2. Data Acquisition
  3. Data preparation
  4. Exploratory data analysis
  5. Data modeling
  6. Visualization and communication
  7. Deploy and maintenance

Potential of data science:-

The power of data science is beyond our vision. We use it in our day-to-day life. It made our lives easier. Data science is being currently being used by many companies like Google, Instagram, Apple, etc. Whatever we browse, we watch is everything monitored from second to second.

Some of these determine its potential:-

  1. Genomic data provides a deeper understanding of genetic issues.
  2. Logistics companies like DHL and FedEx have discovered the best time and route to the ship.
  3. Used to predict the employee artition and understand the variables that influence employee turnover.
  4. Airline companies can now easily predict flight delays and notify passengers.

best data science courses with placement in India

Applications of Data science:-

 Data science plays a major role in many fields of the world like health, finance, Entertainment, cyber security, social networking, weather forecasting, etc.

  • Apps like Instagram Facebook YouTube collect the data from which we are interested and designs a user-friendly profile with recommendations popping up.
  • Data science is also used in detecting earthquakes’ location and magnitude by Seismograph.
  • Often used in cyber security and crime-related issues because data science has every single information of a person like his address, phone number, salary, what type of device he uses, etc.
  • Entertainment sites like Netflix and Prime video analyze the information from the videos which we have watched recently and creates our recommendations.

You might wonder which company has the most data. And the prize goes to google.

Because Google’s entire business is based on data science. Google uses apps like Google Maps to show us the best route in the traffic is an example often build by data science.

Companies like Apple supports the user’s privacy and does not allow the companies to go through our personal information using data science.

A tool like VPN helps in disguising or diverting our IP address from ISP and third parties.

Another segment to know under data science is hacking.

Hacking is done by hackers who are unauthorized users who break into one’s system and steal or destroy their personal information.

Hacking can be prevented by installing anti-software and keeping it up-to-date.

Another way of preventing hacking is setting up two-factor authentication.

Artificial intelligence like Siri, Alexa, etc are designed for user assistance and can be referred to as user-friendly software.

best data science courses with placement in IndiaFuture outlook:-

In the future foresight for sure, Dada signs will rise rapidly and will make our lives much easier with the better implementation of technology in the upcoming generations. For sure we can see the golden ages of artificial intelligence in the upcoming era.

We will be able to get the use of robots for better development. But how much ever it grows it must be always embedded in the limits because if it overtakes the human race it will be the end of Mankind. But it is difficult to equal the level of human intelligence.

Case Study:- Instagram algorithm

The main objective of the Instagram algorithm is to keep its users online for as much time as possible. Its algorithm works like popping the ads that users might be interested in. Now you might wonder how can Instagram know about its user’s interests.

Instagram algorithm stores the set of information of each user separately like how much time a user spends on a post or a real or what type of post he likes frequently or what type of ads the user visits.

So it analyzes from all these statistics and organizes the homepage and search engine of one account to hold them online for most of the time. It might seem surprising and tactical but at the end of the day, it’s all business.

The ones who are interested in data science is a very good field of the subject to opt for.

One can opt for data engineering at the graduation level. They would have a very good scope of becoming a data scientist or a data engineer. And The mean average salary is around $90,000 to 120,000 $.

And that’s it in today’s blog. Hope you had an informative day.

Hasta la vista.

Article Credit – 

“This blog was written and submitted by Ruthvik Rao, Hyderabad as a part of Imarticus National Blogging Contest. All views and opinions expressed within this article are the personal opinions of the author.

Disclaimer:

The facts and opinions appearing in the article do not reflect the views of Imarticus Learning and Imarticus Learning does not assume any responsibility or liability for the same.

What Skills Are Needed to Be A Data Scientist?

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A career in data science is highly attractive owing to its payment structure, job opportunities, and future career prospects. There is any number of Data scientist courses that you can find and that makes you qualify for the job.
The major criteria for this career are a few skills that one can easily master through the right path.

These skills could very well be different from any former experience in the career thus far. Developing these skills will help the recruiters to identify you as the best option for what they are looking for!

Programming language
A strong knowledge base of any major programming languages such as Python, R, or SQL is the foremost requirement to be an expert in data science. No matter what the company or the job profile is, this is one field of expertise that is non-negotiable.

Statistics
Statistics hold more value in data science since it helps to deal with the raw data of the companies. It helps with the evaluation, designing, and making decisions in the later stages.

Deep learning
This machine learning technology enables computers to work like the human brain.

Data Science CourseAn enormous amount of data is managed through computing power to make it possible. A career in data science, especially that in the automobile and AI industry requires this particular skill.

Working with unstructured data
Data science is mainly about the gigantic amount of data from various sources. The vast majority of this data is in a raw and unstructured format. A skilled data analyst can easily go through them to find and identify what they are looking for to make it useful.

Appetite for problem-solving
Simply looking at the data is not what makes the analyst skillful. It also calls for the right appetite to identify the problems underneath and finding the ideal solution as well. For which the analyst needs to have the drive for problem-solving and look in the right areas.

Data visualization
This is the skill that enables a data scientist to identify and decode the raw data into an identifiable visual to use it to convey. This skill enables the analyst to see what the data is useful for with the help of the various data visualization tools.

Communication skill
It comes next to the visualization part. The visualized data needs to be explained in a simple and well-constructed plan to the stakeholders. AT this juncture, the analyst must have strong communication skills to convey the key points and make them believe in the same. Polishing communication skills would be an added advantage to improve career prospects.

Familiarity with data science tools
Data science involves various types of tools to help with data processing. An analyst must have a fairly good idea about the working of most of the tools. Since each type of data requires different tools, it is highly imperative to be on familiar terms with these tools. Most of them are pre-programmed, so you just need to know how to use them in the proper way.

Intuition
Last but not the least, having a strong intuition on what to look for, how to use it, and which tool needs when to get the best result out of the data analysis happens to be the strongest point of being a successful data scientist.

Conclusion
Most of these skills are covered in the Online data scientist course in India available from various sources online or otherwise. What needs more work would be on soft skills which also have an equally important role in a successful career. A career in data science does not have refined eligibility criteria, instead, it mainly depends on these acquired skills.

What are the Perks of Learning Data Science with Imarticus post COVID-19?

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Covid-19 has pushed most corporate sectors to the inside of people’s homes. This in turn has made the already big flow of data turn into a tidal wave. Basically, the whole industry more or less relies on data analytics now. Experts state that there is going to be a major hike in the positions for data scientists in the near future.

artificial intelligence and machine learning coursesHowever, one thing to be concerned about is that it is going to make the already competitive industry even more neck and neck.  The first preference for positions is going to be data scientists with experience, and then freshers with a high level of skills.

The best thing to do in this situation is to properly learn data science with artificial intelligence and machine learning from a good institution.

Imarticus Learning is one of the topmost options when it comes to data science in this country. They offer PG programs in the data science course with placement in renowned companies. This will give you a much-needed boost when you are starting as a fresher in the sharp-edged competitive world of data science.

Major changes

Because of the world working in a virtual space, it has recently been in the trend for companies to hire professionals from other parts of the country along with locals. This is true for all sectors, not just data science. The perk of this trend is you can get a job anywhere in the country without moving an inch from your home. The downside is, you’re competing against numerous data scientists all over the country.

The only thing that will give you an edge over others in this condition is to learn data science from institutions that will put you in a speed race with a proper destination. Basically, institutes that will enhance your skills to the maximum while giving you a placement offer right out of your course.

This will help you gain all the real-world experience you might miss out on while being stuck at home, as companies used to provide workshops as well as in-person training for the new data scientists joining the team.

 Benefits of a data science course with Imarticus Learning post Covid-19

Many institutes in India offer an artificial intelligence and machine learning course after graduation. Imarticus Learning is one of the foremost institutions when it comes to this field. They have various forms of learning to offer, such as full-time courses for students, as well as part-time ones for working professionals who want to polish their skills again or change careers. There are lots of benefits of getting a data science degree from Imarticus Learning, such as:

  • They offer a full-time course, as well as a part-time one for those already with a job.
  • They have a course set so versatile that you will never have any problems working in any sector with your data science degree.
  • They provide a data science course with placement offers to renowned companies in different sectors. So, you have a chance of working in your dream job right from the start.

Conclusion

If expert reports are to be followed, companies in the future may be inclined to hire more versatile workers than specialists. So future data scientists will need to be razor-sharp all the time with an ability to do a variety of different types of work at the same time. Check out Imarticus Learning’s all-rounded PG program on data science if you are thinking of pursuing this career or re-polishing your skills.

The Impact of Data Science on Current Events and the World

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The Impact of Data Science on Current Events and the World

Data science remains one of the most lucrative and challenging career pathways for experts. Successful data professionals now grasp the traditional skills of analyzing massive quantities of data, data mining, and programming.

best data science courses in IndiaData scientists must control the complete spectrum of the data science life cycle and must be flexible and understandable so as to optimize returns at each stage of the process to detect meaningful intelligence for their organizations.

You can also contribute to this surge by doing proper data science online training.

Skills that data scientists must have:

According to a study by IBM, a data scientist must be able to perform the following tasks:

  • Use math, statistics, and a scientific approach
  • Use a variety of tools and strategies for data assessment and preparation – for example, SQL, data mining, and data integration methods
  • Data extraction through predictive analysis and artificial intelligence (AI), including in-depth learning and models
  • Write apps for data processing and calculating automation
  • Tell — and illustrate — stories that show the importance of findings at every level of technical knowledge and comprehension to decision-makers and stakeholders
  • Explain the use of these results for business challenges

The number of job opportunities in the industry is increasing by more than 5% a year, according to an IBM study.

What is the role of data science in the current scenario?

  • Inadequacies can cost companies up to 30% of their income. The data science course allows you to follow a number of business indicators, including manufacturing times, delivery expenses, productivity for employees, and more, and suggest improvements.

It is feasible to reduce total expenses and increase return on investment by limiting waste of resources.

  • Data science enables companies to consistently refine their products and services to suit a changing market by assuring a ready-flow of practical insight into customer psychology, behavior, and satisfaction.

Data on clients can be accessed from a range of sources, and information mining from third-party platforms such as social media, search engines, and data sets.

  • One of the most intriguing aspects of data science is testing. New, inventive options are compared with current features and often produce surprising outcomes.

Companies can create incremental revenue gains through consistent, long-term testing. Data scientists are in charge of conducting thorough tests to ensure the effectiveness of marketing campaigns, product launches, job satisfaction, website optimization, et al.

  • Data science is used in the current scenario to improve a company’s safeguarding of sensitive information. Banks, for example, deploy sophisticated machine-learning algorithms for detecting fraud based on variations from a user’s normal financial activities. Because of the vast volume of data created every day, these algorithms can detect fraud faster and more accurately than humans.

Algorithms can be utilized to protect sensitive information via encryption.  By ensuring data privacy you can help guarantee that your organization does not misuse or reveal sensitive information about its consumers, such as credit card numbers, medical information, or Social Security numbers.

  • Data collection and analysis on a bigger scale can help you spot developing trends in your market. Purchase information, stars and influencers, and search engine searches can all be utilized to discover the things people want.

Conclusion

It can be concluded that a career as a data scientist is an extremely lucrative option in the current world as data science is gradually taking over the entire world. The data science pro degree can help you understand the intricacies of this field and learn data science effectively.

If you are a recent graduate and want to learn data science, a post-graduate program in data analytics and machine learning can help you learn better from live faculty and bag guaranteed jobs in the future. Proper data science online training can help the audience come here.