Contact Us
×

Tag: career in data science

What Are The Resources to Learn Data Science Online?

Posted on by Imarticus Learning

What is Data Science?
In the modern digital era, data is at the heart of every business that relies on the use of technological solutions to boost customer experience and increase revenue. The decision-making process has changed after the advent of data science. Businesses no longer work on assumption; they are using complex data analysis to obtain valuable insights about the market and consumers. So what exactly is data science and how does it work to further business objectives?

Well, data science can be simply explained as a discipline that deals with data collection, structuring and analysis. It involves the use of the scientific process and algorithms to obtain valuable insights from seemingly irrelevant pieces of information. Big data is at the centre of data science. Let’s delve deeper into why you should consider learning data science.

Why Learn Data Science?

The demand for data science professionals is ever increasing as more and more companies are deploying data science to obtain deeper insights.

Data Science Course OnlineThe demand for data science course online is also growing as more individuals are lured in towards the lucrative career prospects offered by this industry. There are numerous reasons to learn data science in the contemporary landscape.

The first and foremost is the outstanding remuneration offered to data science professionals. This is partly because data science is still in its nascent stage and there is a scarcity of trained professionals in this industry.

However, the demand for data science professionals by companies is on an upward trend.

 

In addition to this, the role played by data science professionals is very crucial for businesses as it involves analysing valuable company data to obtain insights and make predictions regarding the market.

Let’s explore how you can easily get trained for data science online.

Resources to Learn Data Science Online
Online learning is the new norm, the benefits of this method of learning is enormous. Moreover, the online courses are designed in such a way that it caters to specific training needs of individuals and there is no irrelevant content included in the courses. It is also feasible for people who are already working at a job and have limited time to learn a new subject. Here are a few resources that can help you learn data science online with ease and in a limited budget.

Google’s Machine Learning Crash Course

The machine learning technology is being extensively used by companies to cater to a growing audience base. Google’s Machine Learning Crash Course is designed for everyone; it doesn’t require you to have any prerequisite knowledge regarding the subject. Even people who have some knowledge in the field can opt for this course as it focuses on important concepts like loss functions, gradient descent, etc.

In addition to this, you will also learn about presenting algorithms from linear regression models to neural networks. The course learning materials include exercises, readings, and notebooks with actual code implementation using Tensorflow.

In addition to this crash course, you will also have access to a plethora of learning materials on data science and AI. These learning materials include courses, Practica, Guides and Glossary.

Imarticus Learning’s Data Science Prodegree

If you are looking to make a professional career in the field of data science then the data science course offered by Imarticus Learning is surely the best way to learn data science. The best thing about this course by Imarticus is that the knowledge partner for this course is KPMG.

This data science course takes a comprehensive approach towards learning data science and covers topics such as R, Python, SAS Programming, Data visualisation with Tableau, etc.

Data Science And Machine Learning Course with iHUB DivyaSampark @IIT Roorkee

Data science is a competitive field and to be successful you need to master the foundational concepts of data science. Imarticus Learning has created a 5-month data science program with iHUB DivyaSampark @IIT Roorkee. It will equip you with the most in-demand data science skills and knowledge that will help you to pursue a career as a data scientist, business analyst, data analyst and data manager. It features a 2-day campus immersion program at iHUB Divyasampark @IIT Roorkee and is delivered by top IIT faculty through live online training. Through this program, you will also get an opportunity to showcase your startup idea and get funding support.

In addition to this, the course trains individuals using industry sneak peeks, case studies and projects. The capstone projects allow individuals to work on real-world business problems in the guidance of expert project mentors. Upon the successful completion of this course, you will also receive a certification by Imarticus learning in association with Genpact. In addition to all this, you will receive interview preparation guidance and placement assistance.

 

Top Career Options in Data Science!

Posted on by Imarticus Learning

Data Science is an emerging and yet established interdisciplinary filled that makes use of objectively led processes, methodology, systems, and carefully curated algorithms to study data. This file is very close to and often overlaps with Big Data and Data Mining.

By careful study of the Data Science Course, this field aims to extract important information and patterns that can be used for a number of decision making, information gathering, and data collection tasks.

Where is Data Science Used? 

Data Science is being used by a myriad of fields ranging from state-sponsored departments, the police, military, private companies, NGOs, marketing experts, researchers, and customer service support groups around the world. Most recent and successful technologies such as face recognition are a product of data science innovation. Cookies that online retail stores and online publications use are based on this field too. Data science has entered almost every aspect of our digital lives in a short span of time.

What are some of the jobs that Data Science has?

Here is a list of top Data Science Career Options in Data Science that are shaping our future:

  • Data Scientist: This is a highly sought after job in the field of Data Science. A data scientist is expected to study all big and small data that has been gathered. They are also supposed to form the recommender systems and organize the data for analysis. All major corporations like Facebook, Google, Microsoft, Twitter, etc. employ data scientists. This job is better suited for people who are good at mathematics and coding.
  • Machine learning engineer: A machine learning engineer is entrusted with the job of making data funnels that aid in software creation. They also construct the appropriate and suitable algorithms needed for problem-solving. The machine learning engineers study the systems and its prototypes by running regular tests. They experiment with different problem-solving techniques and modify the current operating steps to improve the current methodology and quality of work. Machine learning engineers are highly paid.
  • Business Analyst: A business analyst tests data by keeping in mind the requirements of the business house it is serving. One does not have to be specifically from a technical field to perform this job. A business analyst has knowledge about industries like telecom, finance, logistics, marketing, and retail.These people are well informed about government and legal policies related to financial technology. A business analyst helps a company find out what information they need to enhance the company’s consumer behavior, marketing strategies, and relationship with its customers.
  • Data Analyst: Like the name suggests data analysts are responsible for primarily web tracking, testing, and operating big and large data sets. They use a mix of statistical tests and interdisciplinary methodology made up of qualitative and quantitative tools to study big data.They have to pick relevant patters and form conclusions based on a set of figures available. A good data analyst is equipped with the number of fact-finding and statistical tests that can be applied to a varied set of data packs depending on the availability of the information. A capable data analyst will be perceptive and informed about which tool and method have the best probability of revealing the most reliable information.

Conclusion

Data Science is one of the most expansive and quickly growing fields in the world. There has been a steep rise in several Data Science Coursetakers in the last few years. The reason for this recent increase in popularity is the number of jobs that have emerged in this area. Since data science is multidisciplinary, people from different subjects and work fields can collaboratively work in it.

Don’t Miss These Comprehensive Questions To Ace Data Science Interview!

Posted on by Imarticus Learning

109 common data science interview questions to remember

 Data science interviews are often considered to be difficult and it might be difficult for you to anticipate what questions you will be asked. The interviewer can ask technical questions or throw you off guard with questions you hadn’t prepared for.

To pursue a full-fledged Data Science Career, it is important for you to be up to date on an array of questions that might be asked during the interview, ranging from programming skills to statistical knowledge, or even field expertise and plain communication skills.

Here is a segmentation of the various categories along with the list down of the possible questions you can expect in each category as an interviewee during a data science interview.

Statistics

As an interviewee, it is essential for you to be prepared on statistical questions since statistics is considered to be the backbone of data science.

  • What are the various sampling methods that you know of?
  • Explain the importance of the Central Limit Theorem.
  • Explain the term linear regression.
  • How is the term P-value different from R-Squared value?
  • What are the various assumptions you need to come up with for linear regression?
  • Define the term- statistical interaction.
  • Explain the Binomial Probability Formula.
  • If you were to work on a non-Gaussian distribution, what is the dataset you would use?
  • How does selection bias work?

Programming

Interviewers may ask completely general questions on programming to test your overall skills or may try and test your knowledge on big data, SQL, Python or R. Listed are a couple of questions that may turn out to be relevant for you to crack that interview like a pro.

  • List the pros and cons of working with statistical software.
  • How do you create an original algorithm?
  • If you were to contribute to an open-source project, how would you do it?
  • Name your favorite programming languages and explain why do you feel comfortable working in them.
  • What is the process of cleaning a dataset?
  • What is the method you would take for sorting a large list of numbers?
  • How does MapReduce work?
  • What is Hadoop Framework?
  • If you are given a big dataset, explain how would you deal with missing values, outliners and transformations.
  • List the various data types in Python.
  • How would you use a file to store R objects?
  • If you were to conduct an analysis, would you use Hadoop or R, and why?
  • Explain the process using R to splitting a continuous variable into various groups in R.
  • What is the function of a UNION?
  • Explain the most important difference between SQL, SQL Server, and MSQL?
  • If you are programming in SQL, how would you use the group functions?

Modeling

While a Data Science Course will teach you the basics of modeling, at an interview you may be asked technical questions like building a model, your experiences, success stories and more.

  • What is a 5-dimensional data representation?
  • Describe the various techniques of data visualization.
  • Have you designed a model on your own? If yes, explain how.
  • What is a logic regression model?
  • What is the process of validating a model?
  • Explain the difference between root cause analysis and hash table collisions.
  • What is the importance of model accuracy and model performance while working on a machine learning model.
  • Define the term- exact test.
  • What would you rather have; more false negatives than false positives and vice versa?
  • Would you prefer to invest more time in designing a 100% accurate model, or design a 90% accurate model in less time?
  • Under what circumstances would a liner model fail?
  • What is a decision tree and why is it important?

Problem Solving

Most interviewers will try and test your problem-solving ability during a data science interview. You may be asked trick questions or be subjected to topics that evoke your critical thinking abilities.

Listed are some questions that will help you prepare for an upcoming interview.

  • How would you expedite the delivery of a hundred thousand emails? How would you track the response for the same?
  • How would you detect plagiarism issues?
  • If you had to identify spam social media accounts, how would you do so?
  • Can you control responses, positive or negative to a social media review?
  • Explain how would you perform the function of clustering and what are the challenges you might face while doing so.
  • What is the method to achieve cleaner databases and analyze data better?

What is The Salary Of A Data Scientist?

Posted on by Imarticus Learning

One of the biggest drivers of data science as a career in India is its earning ability. In India alone, the average annual pay of a data scientist (analyst) is upwards of INR 8 lakh. This can definitely vary depending upon the location, the type of the job, and the employer.

While you can improve the chances of earning a higher salary by signing up for a data science course, it is a good idea to look at how the remuneration looks like through the cycle of a data science career.

Hierarchy of Salaries of a Data Scientist

This is a quick look at how your salary will grow as you gain more experience and get trained for a data science career.

  • Beginner – An entry-level data scientist from a reputed institution can look at earning upwards of INR 5 lakh a year. This is a better salary compared to those that engineering graduates receive in India. Here, as a beginner, your goal should be to gain more hands-on experience in your specific field to demand a better salary. For example, a data science training course can improve your chances by 2x.
  • Analyst With 1-4 Years of Experience – Although there is a possibility that your data science salary can touch the INR 10 lakh mark in this period, it will depend on your projects and overall work experience. This is a good time to shift from your existing employer and look at greener pastures.
  • Professional – This is when you achieve more than 10 years of experience. Here you can dictate a salary in the range of INR 15 lakhs to INR 25 lakhs. With several credentials and projects in your name, you can really carve out a prosperous life for yourself and your family. This is also where you can think of spending some time on research.

Just remember that when you start off, make sure you are well trained for a data science career. As a fresher, your academic qualifications will play a vital role during interviews. A data science training course that will help you choose a specialty and make it a focal point is the way to go.

Promotions and Salary Hikes in Data Science

As with every other professional field, a promotion in a data science career is entirely dependent on performance. However, a salary hike of 30% to 50% can be expected. Again, this will be influenced by how well you perform in your role, but given that skilled data scientists are hard to find in India, you can be optimistic.

In any case, if you don’t feel you are professionally growing in your current company, you can always look at upskilling or switching jobs. A data science training course can make you a potential candidate then.

As noted above, getting trained for a data science career can boost your earning and promotion capabilities. While data scientist intrinsically is a well-paying job, a training course in the field will do no harm.

Why is Data Science a Good Career in 2021?

Posted on by Imarticus Learning

Being a data scientist is only growing in demand over 2021 and is showing no signs of slowing down. It is estimated that around 11.5 million jobs in data science will be created by 2026 in the US. But, why is that the case? This article seeks to answer that very question.

  1. Use in Companies

Due to the ever-growing base of Big Data, every company is looking to utilize all available information to have a massive competitive edge.

Data Science CareerA data science career under a company is a frontier-field that finds new ways to better one’s products and services after utilizing past stores of information and/or case studies.

This work hence involves finding various avenues of data and finding new ways of processing and drawing conclusions from that data.

  1. Use in Studies

Being a form of study that is still in its nascent stages, a data science career may not be motivated by finding profit for a certain industry but also increasing the ambit of human knowledge. One might also work on designing a data science course from others to learn from.

  1. Proper Pathway

While being a data scientist requires a lot of work, the exact path to such a goal has been charted time and time again. There is a great degree of resources available now to become proficient in various aspects related to the data sciences. Other than doing a basic data science course, one may partake in learning various related fields like programming and big data processing from various online platforms (e.g. Imarticus learning).

  1. Demand Doesn’t Slack

The demands for data sciences have also increased due to the new atmosphere generated by Covid-19 and the near-worldwide lockdown because of it.

Data Science Roles

It has been studied that 50% of the data science organization showed no slow-down and have seen growth. This requires one to find new ways to collect data, as well as use that data to aid in multiple projects. These may involve helping set up new modes of businesses, and helping older businesses change their plans to suit their new circumstances. Furthermore, it may aid in improving a range of services on a global level.

  1. Diverse Skillsets

It is easy to switch into being a data scientist incorporating your present skillset. Whatever your present occupations and/or interests may be, it can lend an avenue to collecting data on that specific domain.

Data Science TrainingThey can complement these skills with learning standard data sciences’ skills. Former data analysts may also expand on their present sphere of knowledge to become data scientists, with relative ease.

  1. An Expanding Field

In 2021, a lot of past data science models are up to open-source scrutiny. Hence, even in this new field of human knowledge, one can have a sizable understanding of multiple avenues of collecting and processing data. Their entry into data sciences will work to expand on this field of knowledge.

In conclusion, one can see that it is indeed highly fruitful to be a data science in this present day and age. One can channel his/her present skillset into this occupation as well and aid a burgeoning field of human growth and knowledge.

Python Developer Salary in Terms of Job Roles

Posted on by Imarticus Learning

What is Python?

The second most liked Programming language in the world, Python is one of the widely used term in the web-development world.

Who are Python Developers?

The web-developers who design and code the software applications with the help of Python language are referred to as Python Developers.

Roles and responsibilities of Python Developers

 Python Developers as Data Scientists

Major businesses in today’s world require tools and skilled people for the data-related tasks such as data collection, data cleaning and processing.

Python Programming Course with Data ScienceData Scientists are the programmers who do these tasks for the organizations. Data Scientists gather a large quantity of data and convert it into a useful form, followed by recognizing data-analytics solutions for organizational growth.

Data Scientists encourage the data-driven approach in organizations to deal with complex business problems.

 Artificial Intelligence

In AI Python Developers create and implement the required Machine Language algorithms. They analyze the success and failure of the algorithm and rank them according to their performance for future use. Training and Retraining ML algorithms is one of the key tasks performed by Python Developers.

Salaries of Python Developers in India

The changing focus of the organizations on data-driven solutions is resulting in a manifold increase in the salaries of the Python Developers. In the coming years the demand for AI-skilled people will increase, and hence the salaries.

Major IT giants like Google, YouTube, Amazon etc. are adopting Python-driven systems and hence, manifold increases in the salaries of the Python Developers.

Python Programming Course with Data scienceThe entry level salary of a Data Scientist is approximately INR 500,000 per annum (Source: payscale.com) Data scientists with 5-9 years of work experience gets +100% more salary in India. With the experience and refinement of the skill, the salary of Data scientists shows exponential growth.

 An entry-level engineer who develops the ML Algorithms earns the average salary of approximately INR 700,000 annually. With further experience and refinement of the skills, the average salary of the Data Scientist shows exponential growth.

Factors affecting the Salaries of Python Developers

Job location: Considering the increasing demand of Python Developers, not only in India, the faster salary growth is visible in UK and in US.

Location

 Approximate Average Salary in INR

Gurgaon

 700,717

Bangalore

669,787

Delhi

600,000

Mumbai

579,728

Chennai

540,131

Hyderabad

 475,000

UK Python Developer annual salary

£67,000

US Python Developer annual salary

 $117,000

Experience:

Python Developer

Approximate Salaries in INR

Entry Level Python Developer

427,293

Med-Level Python Developer

 909,818

Experienced Python Developer

 1,150,000

Refined Skills:

Mere understanding of the Python is of no use, till is integrated with the problems and solutions. How one uses the well-known Python tools define the person’s skill set, which is a determiner of the salary.

Job Role:

Python Developer

Approximate Average Salary in INR

Data Scientists

700,000

ML Engineer

 670,000

DevOps Engineer

660,000

Software Engineer

500,000

Web Developer

 300,000

 

Python Programming Course with Data ScienceShould know more interesting things about Python programming training and Python career.

 

How Imarticus Helps For A Data Science Career in Pandemic Times?

Posted on by Imarticus Learning

Data-driven strategies have shot up in popularity after the coronavirus pandemic wreaked havoc on business plans. Data science is a key player in sustaining businesses not just now, but in the future when similar turbulent circumstances threaten to bring down shutters on previously stead organisations.

As a result, hundreds of companies across India and overseas are looking to add more data scientists to their repertoire. This comes from a need to drive more data-driven decisions and make businesses more resilient to change.

Here are some specific reasons that make a case for how choosing a data science career can be beneficial in times like these:

  • A need for general expertise

While previously companies favored data science specialists, today they prefer generalists and Jacks of all trades. While specialists come with in-depth knowledge and specific skill sets, they often cannot think beyond their domain. Companies today need someone who has skills to use across the board so that they can both learn on the job and be useful where they’re needed.

  • A need for understanding project flows

Many companies who are delving into data science now are probably unsure of their footing and their way forward.

Data Science CourseA data scientist is critical in companies like these, as they bring expertise to the table and understand the flow of projects much better than anyone else. With a data scientist at the helm, all other players in the process can fall into place. This reduces the pressure on upper management to figure out project flows; they can now leave it to the experts.

  • Higher chances for growth

Data scientist generalists are more likely to grow with the company– something many organisations prefer. Unlike a specialist, who already has a defined skill set, rookie data scientists can be shaped and molded into an ideal employee for the company. In the process, the data scientist becomes an intrinsic part of the organisation, learns business tactics and applications and develops skills through experience rather than through specializations. As a result, they become both experts and creative problem-solvers.

  • Immediate requirements

Businesses are struggling to stay afloat during the aftermath of the pandemic and are realizing their urgent need for data-driven business plans. As a result, many of them have put out feelers and immediate job offers for data scientists. This is in complete contrast to other fields that are seeing scores of job cuts, furloughs and pink slips, and goes to show that data science is only increasing in popularity.

It is worth keeping in mind that, despite recruitment into data science roles, many companies have slashed budgets and can’t afford to pay more experienced scientists at this stage. Rookie data scientists form the perfect compromise–  they’re eager to learn, have the necessary skills and can be accommodated within tighter budgets without reduced salaries.

  • Opportunities for upskilling

As rookie data scientists settle into their roles, many companies consider upskilling them for higher positions or specific technical projects.

Data Science CareerThis is an invaluable opportunity for fresh data scientists as the company takes care of all the costs and only asks for your attention and application in exchange. Adding a data science course to your CV will also help you get a leg up on the competition when you’re ready to switch roles or companies.

The final word

The data science landscape has shifted significantly in response to the coronavirus pandemic. As a result, rookie data scientists who are only just entering the field have a once-in-a-lifetime chance to make their mark and cement their place for when things stabilize.

Using Near-Miss Algorithm For Imbalanced Datasets!

Posted on by Imarticus Learning

Data scientists are required to obtain, pre-process, and analyze data. Companies can use the insights gathered by data scientists for making important business decisions. While this task seems straightforward, there is a multitude of challenges witnessed by a career in data science.

All seems to be a tedious task, right from learning the fundamentals from data science courses to generating data science. But the major challenge lies in data cleaning for any data science operation. To be specific, 70 percent of the work of a data scientist consists of cleaning and preparing data.

Data Science CoursesAn imbalanced dataset is a typical example of unbalanced data. Let us see how to use the Near-Miss Algorithm for imbalanced datasets.

What is an Imbalance Dataset?

For classification problems, imbalanced datasets are a special case where the distribution between classes is not uniform. They are usually composed of two classes: the majority or negative class and the minority class which is also known as the positive class.

Imagine, in your dataset, you have two categories to predict: Category-A and Category-B. You have a problem with imbalanced datasets when Category-A is higher than Category-B or vice versa.

So how could this be a problem?

Imagine that Category-A contains 90 records in a dataset of 100 rows and Category-B contains 10 records. You run a model for machine learning and end up with 90 percent precision. Then comes the certainty check and you get to realize that the results are not accurate. This is a common error caused by imbalanced datasets.

Near-Miss Algorithm

The Near-miss Algorithm is used to balance an imbalanced dataset and is considered as an algorithm for undersampling and is one of the most powerful ways to balance data.

The Near-Miss algorithm works by observing the class distribution, removing samples located in the higher class. Simply put, if the algorithm witnesses a case in which two near points that pertain to different classes occur, it simply excludes the one from the higher class and ensures that the balance is preserved.

Types of Near-Miss Algorithm

There are 3 main versions of the near-miss algorithm. They are listed as follows:

Type 1: In this type of Near-Miss Algorithm, unbalanced data is improvised by assessing the minimum distance (avg) between the large distribution and three farther small distribution.

Type 2: In this version, the balancing of data occurs by figuring out the distance between ‘n’ neighbors of the data points belonging to smaller classes. The largest distance obtained from this calculation is eliminated.

Type 3: This version involves the calculation of the minimum or shortest base distance between the larger distribution and three other smaller distributions close to it.

Using the Near-Miss Algorithm for an unbalanced dataset

To use the Near-Miss Algorithm for an unbalanced dataset, three major steps are followed. As a part of the first step, the distance between the points belonging to the larger class and the point belonging to the smaller class is considered.

This is done to ensure that the undersampling process is simplified. Moving to the second part, the instances belonging to the larger class are selected. While selecting these instances, it should be noted that only those who have the shortest distance are chosen. As a final step, the algorithm returns m*n instances from the larger class.

Conclusion

The choice for an appropriate method depends on the dataset and the approach as desired by the user. Near-Miss is a popular undersampling technique that is used to deal with imbalanced classes.

However, it is not the only one. Other methods of dealing with unbalanced data include random sampling, SMOTE, etc. Therefore, make sure you are thoroughly aware of the technique before proceeding with it.

How Freshers Can Get Real-World Job Experience In Data Science

Posted on by Imarticus Learning

Introduction

For most freshers, landing a Data Science job seems like a chicken-or-egg situation. You need to have hands-on work experience to get selected for such a job, but how do you get any work experience without first being hired?

By now, you must have heard, read or seen a lot about the scope for immense growth that a Data Science career can offer. However, for many aspiring Data Scientists, the reality appears to be hard-hitting.

The career potential of a Data Scientist is undoubtedly very rewarding once an individual gets the job, but getting the job without prior work experience is the main obstacle they face.  Below, we examine some practical solutions to this dilemma:

 Work on personal Data Science projects

Data ScienceThis is an interesting and highly practical way to gain real-life Data Science experience. Once you finish a project, you can showcase your work on a platform like GitHub. Focus on small projects, and try to demonstrate important Data Science skills in your efforts.

The advantages of working on your own project are that you gain hands-on experience in generating ideas, collecting data, cleaning data, analysing data and building predictive models.

Therefore, you gain a comprehensive understanding of the entire process. As far as possible, try to script clean codes and develop clear visualizations that potential stakeholders can find easier to follow.

Do not attempt to display too many skills at once, as you might end up unnecessarily complicating matters for your audience. Simple and small projects will illuminate the core skills you wish to draw attention to. For example, consider obtaining a complicated datasheet and cleaning it up. This simple project will demonstrate your prowess in:

  • Scoping a data project and formulating a suitable plan
  • Gathering data using different collection methods
  • Contemplating different data cleaning methods and choosing the most suitable one
  • Handling different data formats such as XML, CSV and JSON

 Contribute to open-source projects

The best way to enhance your coding skills and get hands-on Data Science experience is to join an open-source community. Providing solutions to projects that are already in progress will help you deal with real-world problems, while giving you a taste of what working in a Data Science team would be like.

As a member of an open-source community, you need to constantly communicate with the other stakeholders when making your contributions. Open-source projects are an excellent way to access Data Science libraries, such as NumPy, Pandas, Scikit-learn, and more. Above all else, being a part of these communities will help you build a professional network with relevant people in the Data industry, and also significantly add to your existing knowledge.

 Make tutorial / educational content

If you have confidence in your Data Science skills and knowledge, you can try authoring a Data Science blog feed, or creating tutorial videos that explain the core concepts of Data Science. These are excellent ways to highlight your abilities to prospective employers.

 In-person meetups

After you complete a Data Science course, in-person meetups can present great opportunities for face-to-face interactions with industry leaders and representatives. Meetups are essentially corporate events being held in your city, such as business conferences, presentations, seminars, expos or coding competitions.

Data ScienceThese events are excellent venues for networking with like-minded professionals who work for a range of different organizations. A simple Google search with keywords like Data Science meetups, along with the name of your city, will generate information about ongoing or upcoming events near you.

 Volunteer for a good cause

Many non-profit organizations need Data Science professionals to volunteer for them. This is a good way to give back to society, while at the same time, you could get to work alongside experienced Data Scientists who can guide you and offer valuable career advice.

The tasks you perform can be showcased in your resume, and will be considered as valid work experience. Poverty, Environmental Protection, Equal Education, Public Health and Human Rights are some of the non-profit areas that you can contribute to.

 Conclusion

The career scope for a Data Scientist is tremendous, but it often proves difficult to get a Data Science job without a certain amount of relevant work experience. The key is to show recruiters that you possess the requisite expertise and skills to do justice to the job if you are given the opportunity, and the steps listed above will go a long way towards accomplishing that.

What Does It Take To Be A Good Data Scientist?

Posted on by Imarticus Learning

What does a data scientist do?

The importance and applications of data science have grown exponentially over the past decade. Data science is still in its nascent stage and there’s a whole lot to be identified about this discipline. Businesses have started implanting strategic decision-making tools that leverage data science.

Data helps businesses by providing them with hidden insights and helps them predict the future outcome of their decision. This helps organizations to make a better business decision.

Let’s delve deeper into what these data scientists do and how it helps the organizations.

  • Finding a solution to business problems

Data ScienceOne of the most basic and key responsibilities of data scientists in an organization is to identify existing challenges and problems that a business is facing and finding solutions to remedy the situation. This might seem like a generic responsibility of every important professional but the main difference here is that data scientists use tons of relevant data to find the problem.

They try to come up with solutions after properly assessing the situation using various analytical tools that provide them with useful insights. They leverage statistical analysis, data visualization and mining techniques to provide effective solutions.

  • Find out relevant data using complex research

Data Science CareerThe 21st Century businesses are complex than ever, there are various factors that determine the fate of an organization. With the number of complexities that exist, it’s very difficult to figure out what impacts your business and how it does that.

Data scientists simplify this for organizations by studying all variables affecting a business. They use complex research work to identify the variables that have a maximum impact over the business and which are highly relevant.

  • Identify patterns and trends

Another important work of a data scientist that helps businesses is to identify patterns and trends. Data scientists use sophisticated data analysis techniques to find trends and patterns from the data sets at hand. These data sets are generally historical records of the organization. It helps them to identify the existing patterns and trends which is used to make predictions regarding the future movement of the variables.

How to become a data scientist?

Data Science CourseData science is one of the most in-demand skills in the industry and given the wide range of applications that it has, the demand for a data science professional will continue to rise in the future. One of the most common questions in the minds of data science aspirants is how to become a data scientist? There is no specific answer to this particular question. It depends on what stage of your career you are at and the skillset that you have.

A data science course by reputed institutions such as Imarticus Learning guarantees placement with top-notch firms in the industry in addition to providing relevant knowledge and skills. It also helps you provide guidance from the industry experts who are highly experienced in this domain.

Let’s delve deeper into some of the most prominent skills for data scientists that you should hone if you are planning to opt for a career in this field.

Analytical skills

One of the key skills that are required in this profession and that forms the base of all your work is your analytical skills. One should have an analytical mindset and should be able to identify trends and patterns from a big chunk of data. You should be able to assess a situation from a different perspective to reach a successful conclusion. One should be trained to work with software like Python and R and should be equipped enough to handle large volumes of data.

Problem-solving skills

Another important skill that you need to work on is your problem-solving skills. You need to use data to figure out challenges that exist in the business. After you have figured out the problems you will have to provide a solution using data analytics tools that will help the business to achieve its goals and objectives.