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Tag: Data Science Training

Welcome to the Data Science Club of Imarticus Learning!

Posted on by Imarticus Learning
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Imarticus Learning is among the top online education providers in India. After its data science course helped many data science aspirants to build a successful career in the industry, it has now come with a new campaign i.e. the ‘Data Science Club’. This club will aim at addressing the shortage of data scientists in India. It will also help in unifying data science aspirants from all over the country & giving them a chance to interact.

Data Science CourseYou can bring the data science club to your college/university with just a simple process. They already have registered 30 colleges from locations like Delhi, Tamil Nadu, and Karnataka.

The registration process is open & you can experience a whole new aspect of data science. One can also join the data science community of Imarticus on various social media platforms.

Mission & Vision of the Data Science Club

  • To promote students across India to build a successful career in data science.
  • To address the talent gap in the data science industry & shortage of skilled data scientists in India.
  • To facilitate the exchange of ideas & information relating to data science between club members across PAN India.
  • To provide industry-oriented learning of data science involving technological advancements & tools used in the industry.

Registration Process

Generally, most of the colleges don’t even have a data science club. You could be the first to introduce a data science club at your college/university. You can visit the Imarticus website and can easily register your college/university for the data science club. You will be required to fill a google form asking for a few details like college name, address, department, designation, email address, etc. The Imarticus panel will get back to you and will inform you about further proceedings.

Benefits of Being a Club Member

This data science club will facilitate its members from various colleges across India in understanding the importance of data science. It aims at motivating aspirants for building a successful career in data science and bridging the talent gap in the current data science industry. The benefits of joining the data science club of Imarticus are as follows:

  • Students of member colleges can attend any event/competition under the data science club for free.
  • You will get to attend lectures or webinars from industry experts/professionals.
  • The members of the club will get to test themselves by participating in the national level hackathon.
  • You will get to attend data science workshops under this club. You will also get a certification from Imarticus Learning for being a part of the data science club.
  • The members of the club will also undergo the faculty development programme.
  • Eligible members/students of the club will also get full placement support from Imarticus.
  • You will get to know about the industry practices & trends by being a member of this club. You will also get to know about the right career roadmap in the data science industry.

If you want to make a transition from data science aspirant to an expert, you have to grab this wonderful opportunity which will bring you closer to the data science community in India. One can also opt for the data science course provided by Imarticus Learning to know about the data science aspects in detail.

Register for the data science club now!

Data Literacy Is Very Much a Life Skill– Here Are 4 Reasons Why?

Posted on by Imarticus Learning
Reading Time: 3 minutes

The world is no stranger to data; in fact, in recent times, the world has found itself being bombarded by more facts and statistics than ever before. At quite the same speed, people have also been faced with fake facts, viral social media forwards with little to no truth.

Being data literate has moved from being a niche requirement to being a life skill that allows people to distinguish between fact and fiction. Data literacy is a way of exploring and understanding statistics in a manner that provides meaning and insight.

This meaning isn’t relegated only a data science career or to businesses looking for an edge over competitors. It applies to society and its interconnected systems as a whole.

To drive the point home, here are a few advantages that data literacy offers when looked at as a life skill:

Recognising the Sources of Data

Data is everywhere, especially in a world where nearly everything is digital and produces and consumes more data. There are many different ways in which data exists, including graphs, images, text, speech, video, audio and more. Recognising the different sources of data is the first step towards working with data. The sources, formats and types all have a role to play in determining the use (and potential misuse) of data, which in turn drives data literacy.

Acknowledging the Self as a Consumer and Producer of Data

The messages you send, images you post and likes you leave on social media are examples of data. So are the transactions you make and the searches you conduct on search engines such as Google and Bing. Today, nearly every single person in the world is a data producer; those sources of data are vital to value-generating processes across industries and markets.

Similarly, people are daily consumers of data even if they don’t perceive it as that. The COVID19 pandemic has brought this into the light even further– front page statistics are at the back of everyone’s mind, as are the names of containment zones and the best practices for sanitisation.

Recognize Biases and Fallacies

Data literacy gives the people more agency to call out those producing statistical data that is biased, twisted or outright incorrect. As citizens, consumers and valued members of a society, it is imperative that every individual is able to identify false promises or glossed-over issues that allow wrong-doers to continue as they were.

Data ScienceData literacy gives people the power and the evidential backing to call out those intentionally or unintentionally propagating mistruths and fallacies through awry statistics. This way, data literacy plays a pivotal part in politics, economics and ethics of a society, indeed of the world.

Improves Data Storytelling

Instead of data points presented on their own, data that is presented as descriptive stories make individuals more likely to understand the effect, decipher trends and make more educated decisions. While data storytelling is imperative to learn for those taking a data science course, it is just as important for members of all other fields to better present their arguments such that they catch eyes.

Data has never been a strictly academic factor; however, it has often been painted as complicated, invasive or unnecessary to penetrate everyday lives. Data storytelling ensures that data is taken even further out of that box and presented as actionable insights to even the average Joe Bloggs.

Conclusion

The focus on data science and literacy shouldn’t just be restricted to mathematics and algorithms but everyday applications of data in daily lives. Data understanding allows people all over the world to take more control of what they’re producing and consuming. Data fluency and literacy is achievable by all.

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

Posted on by Imarticus Learning
Reading Time: 3 minutes

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.

Preparing for your data science interview: Common R programming, SQL and Tableau questions

Posted on by Imarticus Learning
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Preparing for your data science interview: Common R programming, SQL and Tableau questions

This data science interview questions blog includes the most frequently asked data science questions. Here is the list of top R programming, SQL and Tableau questions.

R Programming Interview Questions

R finds application in various use cases, from statistical analysis to predictive modelling, data visualisation and data manipulation. Facebook, Twitter and Google use R-programming training to process the huge amount of data they collect.

Which are the R packages used for data imputation?

Missing data is a challenging problem to deal with. In such cases, you can impute the lost values with plausible values. Amelia, Hmisc, missForest, Mice and mi are the data imputation packages used by R. In R, missing values are represented by NA, which should be in capital letters. 

Define clustering. Explain how hierarchical clustering is different from K-means clustering.

A cluster, just like the literal meaning of the word, is a group of similar objects. K denotes the number of centroids needed in a data set. While performing data mining, k selects random centroids and optimises the positions through iterative calculations.

The optimisation process stops when the desired number of repetitive calculations have taken place or when the centroids stabilise after successful clustering. Hierarchical clustering starts by considering every single observation in the data as a cluster.  Then it works to discover two closely placed clusters and merges them.  This process continues until all the clusters merge to form just a single cluster. 

SQL Interview Questions

If you have completed your SQL training, the following questions will give you a taste of the technical questions you may face during the interview.

What is the difference between MySQL and SQL?

Standard Query Language (SQL) is an English-based query language, while MySQL is used for database management.

What do you mean by DBMS, and how many types of DBMS are there?

DBMS or the Database Management System is a software set that interacts with the user and the database to analyse the available data. Thus, it allows the user to access the data presented in different forms – images, strings, or numbers – modify them, retrieve them and even delete them.

There are two types of DBMS:

Relational: The data is placed in some relations (tables).

Non-Relational: Random data that are not placed in any relations or attributes.

Tableau Interview Questions

Tableau is becoming popular among the leading business houses. If you have just completed your Tableau training, then the interview questions listed below could be good examples.

What is Tableau? How is Tableau different from the traditional BI tools?

Tableau is a business intelligence software connecting users to their respective data. It also helps develop and visualise interactive dashboards and facilitates dashboard sharing. Traditional BI tools work on an old data architecture supported by complex technologies. Tableau is fast and dynamic and is supported by advanced technology. It supports in-memory computing. ‘Measures’ denote the measurable values of data. These values are stored in specific tables, and each dimension is associated with a specific key. Dimensions are the attributes that define the characteristics of data. For instance, a dimension table with a product key reference can be associated with attributes such as product name, colour, size, description, etc.

The above questions are examples to help you get a feel of the technical questions generally asked during the interviews.

How Data Science Training Will Ensure Business Continuity In The Post-Covid World?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Lockdown restrictions have pushed us into recession along with a health crisis we have never seen before. Businesses are struggling to make ends meet, and many have had to take tough decisions like layoffs, frozen hiring, salary cuts, and more. The year has also shown us how vital digitalization is and why data science is the driving force in the post-COVID era.

One of the most prominent examples is remote work and how people have adapted to such a form of work. Businesses are accepting digital tools to optimize their functionality, and this is where data science comes in.

Digital tools not only help businesses measure ROI but also determines every small or big aspect like regulating spends, analyzing the long-term impact, and more. The change is making more companies include AI, VR, AR, and cybersecurity to transform their businesses.

Why is data science essential for business continuity?

Here are some of the reasons why adopting a data science course is crucial for current and aspiring businesses leaders:

Analyzing and forecasting

The pandemic gave us clarity of how uncertain times can be. Businesses can go through drastic changes due to such conditions unexpectedly.

With the help of data analytics, we can predict our future better. It helps us analyze risks and develop strategies to mitigate them.

Companies can use historical data from the current situation to estimate t

he trends of the future. Data science training can also give us cues of future obstacles and how to handle them efficiently.

Data Science course

Moreover, historical data can help plan different outcomes of businesses during an unfortunate phase.

Assessing resources for maximum utilization

While analyzing risks and mitigating them fast can prevent disrupting business processes, knowing the way to put resources to use during such times is also crucial.

Every company needs to imply analytic practices to get through critical times and understand the state of their business.

One example of this would be setting up an analytics team for finance. The process will help a business find how changes in the economy can or are affecting their business. Data assists in allocating resources and promotes effective decision-making.

Identify Opportunities

When you adopt data science, you can identify new opportunities for business continuity. The process of analyzing available data resources helps catch loopholes early and find new possibilities to overcome them.

Currently, most companies are aggregating COVID data and combining the same with employee data. It helps one understand better ways to support employees in this crisis. For example, updating a dashboard to check geographical data helps offices take preventive measures or recommend closedown. HR’s are using such data to determine if offices are safe to open or not.

Necessity of cybersecurity

As soon as you step into the data science career, you will know how critical cybersecurity is. It is the most vulnerable risk that any form of data can pose.

Much of this has been at stake since people have been working remotely. There is also an increase in cloud services, which need constant technical tracking, maintenance, and recovery.

Even when companies work remotely, they have to imply IT security to safeguard data exposure and threat. Data science and AI give us solutions to cyber threats and monitor network traffics more effectively across VPNs. It quickly detects points of breaches and infringement in real-time.

Helps in quick changes of services

E-commerce and retail industries use data science and AI to attract customers; both online and offline. Companies now leverage AI-powered solutions to get insights into changing consumer demands. Such data helps optimize the supply chain and minimize disruption.

Data Science CareerThere is a high usage of AI chatbots to get quick solutions to external factors. These chatbots communicate with customers, answer their questions, and record their responses. It is assumed that the usage of chatbots will multiply by 50 times compared to what it was before COVID.

Final thoughts

Business continuity is critical and crucial. More and more companies need to incorporate data science to make businesses effective and reliable. While it has been vital for a long time already, it will become non-negotiable in the post-COVID era. The above-listed reasons give you a brief insight into the bigger picture and why data science is the future of your business goals.

Breaking the Data Science Myths For a Better Career!

Posted on by Imarticus Learning
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Data Science is a scientific discipline that employs algorithms, statistics, processes, and analysis to gain insights and understand in-depth unstructured data. Data Science is a very useful branch of science which is becoming widely popular among organizations.

It helps predict results and makes decisions in a variety of tasks. Data Science involves machine learning principles and analytics to understand patterns and find information.

Data Science Career

Data Science, as a field, evolved after the 90s. Today, it is a widely adopted and used AI platform. Data Science career is becoming a hugely in-demand profession globally.

And as with many other popular jobs, the job of a data scientist is also associated with a lot of myths. But myths are natural. Any attractive thing induce thoughts and beliefs in people’s minds and these can result in myths.

If you are looking to build a career in Data Science, you need to uncover the myths related to this profession as myths can impact your career choices. In this article, we will burst the common myths of Data Science.

No compulsory Ph.D. required
Yes, you read that right! A doctorate is not mandatory for the role of a data scientist. The data scientist profession is divided into two parts – Research and Applied data science. If you are looking to pursue a career as an applied data scientist, then all it requires is the knowledge of basic applications of techniques, the functioning of algorithms, and an in-depth understanding of this field.

However, if you want a research role, then it is good to have a Ph.D. as it will involve working on research papers, creating new algorithms, etc.

Online courses or Part-time degree are acceptable
Contrary to the popular belief, a person need not have a full-time data science degree to pursue a career in data science. There are many online data science courses, part-time or correspondence degrees available that equip you with the knowledge required to pursue this career. All you need is the right skill-set and passion for the field of data science.

Background in Specialized Subjects is not necessary
Data Science is a combination of different subjects like Programming, Communication, Computer Science, and Mathematics. It is important for data scientists to possess knowledge of all these subjects, as each of them plays a major role in a successful data scientist career. Programming is needed to understand data hierarchies and develop algorithms.

Communication is needed to reach out to people and convey them useful information in an easy-to-comprehend manner. Mathematics is needed to deal with structures, models, and designs. Computer Science is needed to incorporate different strategies and plans in the projects. However, one need not have a background in any of these subjects to become a data scientist. A good understanding of all these sectors is enough for a fruitful data science career.

Related Previous Work Experience is not required
Anyone with work experience in any technology related to the field of data science is enough to build a career as a data scientist. One can also step into this field without any relevant technological experience. However, in that case, you will start with the beginner level.

One must equip themselves with the domain knowledge and skills required for this role to become a successful data scientist.

Interesting Puzzles To Prepare For Data Science Interviews !

Posted on by Imarticus Learning
Reading Time: 3 minutes

A Data science career is a lucrative opportunity with many young professionals opting for it. With the easy accessibility to data science courses, the number of professionals pursuing it is rising. There is a huge demand for expertise in this area and it has been voted as the best career by Glassdoor in the United States.

Though there is a need for professionals in this field, it is often not easy to get into. Organizations look for problem-solving and analytical skills in their potential employees and judge them based on creative and logical reasoning ability.

Having a different approach towards a problem and solving it in a unique way can help one stand out from the crowd. It isn’t a cakewalk to master these abilities. One has to practice and try to improve their skills. Solving puzzles is a way to test the individual’s ability to think out of the ordinary and also puts to test problem-solving skills.

The interviewers while hiring fresher especially give them puzzles to solve during their interviews. Due to the pandemic, many companies now have a stricter policy when it comes to choosing the right candidate for the job. It is challenging and the chances of selection are less compared to earlier.

Data Science Career Interview

Some are even assessing the candidates based on their coding skills. To provide an insight into what is in store for the candidates, below mentioned are some of the commonly asked puzzles during a data science job interview.

  1. There are 4 boys A, B, C, and D who are supposed to cross a rope bridge. It is very dark and they have just one flashlight. It is difficult to cross the bridge without the flashlight and the rope bridge can only stand 2 people at once. The 4 boys take 1, 2, 5, and 8 minutes each. What is the minimum time required for the four boys to cross the rope bridge? 

Sol:

This is a question that is most repeated and has an easy solution. A and B are the fastest boys and can cross the rope bridge first. They take 2 minutes. B stands on one side and A returns with the flashlight in 1 minute. So the total time taken is 3 minutes. After that, C and D have to cross the rope bridge. They have taken 5 and 8 minutes each. The total time taken is 8 minutes.

When we add the time taken by all, it is 3+8 which equals 11 minutes. C and D stand on the other side and B takes 2 minutes to return. Hence the total time that is taken by all is 11+2 which equals 13 minutes. At last, A and B will cross the rope bridge and will take 2 minutes and that adds the total time to 13+2 which is 15 minutes. So the time required by all the 4 to cross is 15 minutes.

  1. A person is in a room with the lights turned off. There is a table. A total of 50 coins have been kept on the table. Out of the 50, 10 coins are in the head position while the other 40 are in the tails position. The person has to segregate the coins into 2 different sets in a way that both sets have equal numbers of coins that are in the tails position.

Sol:

Segregate the coins into two groups, one with 10 coins and the other with 40 coins. Turnover the coins of the group that has 10 coins

  1. A bike has 2 tyres and a spare one. Each tyre can only cover a distance of 5 kilometers. What is the maximum distance the scooter will complete? 

Sol: 

To simplify the problem, we will name the tyres X, Y and Z respectively. 

X runs 5 kms

Runs 5 kms

Z runs 5 kms

Initially, the bike can cover a distance of 2.5 kms with tyres X and Y

X=2.5 kms, Y=2.5 km, and Z=5 kms

Take off tyre X and ride the bike with YZ another 2.5 kms

Remaining X= 2.5, Y=0 and Z=2.5

Take off tyre Y and ride the bike with XZ another 2.5 kms

Remaining X=0, Y=0 and Z=0.

Hence, the total distance covered by the bike is 2.5+2.5+2.5 = 7.5 kms

The more an individual practices such puzzles, the better the chances of landing a data science job.

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Data Science and Analytics Career Trends for 2021!

Posted on by Imarticus Learning
Reading Time: 3 minutes

A career in data analytics and/or data sciences is presently in extreme demand. This is due to the need to optimize new modes of data collection to identify large-scale problems and find solutions in a world after Covid-19, despite a minor drop in job openings at the start of the worldwide lockdown.

data analytics career

There are several trends that one must look out for if he/she wishes to pursue a career in data sciences and/or data analytics, including and beyond ones that involve adjusting to the ‘new normal.

It can be argued that 90% of data that is generated and collected were over the past 3 years. The demand for data science and analytics is therefore only going to grow in demand, at least for the next 10 years (and probably more). To ride this wave of opportunities in jobs and research and beyond, one must keep up with career trends relating to these fields.

What are the trends one must keep up with to enter a career relating to data?

  1. Understanding Data Collection

One must take a look at the avenues which entertain the possibility of data collection – preferably in new, never-before-seen ways. One may look to his/her area of expertise and collect data on it while combining newly learned data management skills to become a data analyst and/or scientist. This may definitely be aided by undertaking programs like data analytics courses at Imarticus learning.

  1. Analytical Problem Solving

In addition to hands-on experience, data analytics online learning may cover various fields relating to data. One must learn the basics like spreadsheet management in order to tabulate data more efficiently for analyst work.

Data Analytics Career

It is a useful skill to know what to recognize as possible data and convert that into an absorbable format, which will ensure later calculations, problem identifications, and solutions.

  1. Understanding Data Management Tools

If one is more interested in being a data scientist, then he/she must work to observe trends in big data. This involves learning big data management tools like Hadoop to find newer frameworks to collect, store and make sense of data. With earning SQL and no-SQL programming in addition to managing databases, one may find new problems to solve, whether for research or for aiding a business (or one of the myriads of other uses).

  1. Machine Learning

This is further aided by mastering other tools like machine learning and artificial intelligence. There are various tools that one may incorporate into his/her data studies, be they included in basic data science and/or data analytics courses or not. Undertaking this endeavor will allow one to master various avenues for finding and exercising ideas, which the world will go on to greatly benefit from.

  1. Communication

A possibly surprising trend that can be observed in regular data analysts and data scientists is the presence of soft skills. Someone dealing with data is required to regularly articulate and advertise new ways of improving things in his/her burgeoning field. Skills like effectively communicating one’s ideas and building useful chains of interpersonal relations go a long way in aiding the career of a data analyst and/or scientist.

  1. Artificial Collection of Data

One must find ways for his/her data collection and processing models to work without his/her presence. This process involves training replacements – both artificial and human. Ideally, a data scientist is expected to design systems that function without his/her interference, not only to undertake routine tasks but also to identify new problems and calculate possible solutions. A noted data trend is the undertaking of this process.

Conclusion

In conclusion, one can say that he/she must observe several trends relating to data on a regular basis, to adapt and grow into the self that can make a huge impact on this frontier field.

Data Analytics CareerData science and analytics are making strides in the tech market, and it is clearly the future. So, a career in data analytics can be really fruitful in the long run.

Stay Competent with most In-Demand Data Science Skills!

Posted on by Imarticus Learning
Reading Time: 3 minutes

What is Data Science?

The Science of combining capital processes, algorithms, and many such best tools to collect, manage and analyze the most important data to make business decisions is Data Science.

Who is a Data Scientist? 

A computing professional beholding the skill of data collection, data storage and management, and data analysis enabling the organization to make data-driven decisions quickly are Data Scientists.

 In-Demand Data Science Skills

Some of the most In-Demand Data Science Skills are:

Understanding of Math & Statistics 

Online Data Science course in India is all about extracting the required information from the data. A depth understanding of mathematical probabilities and statistical methodologies helps in data analysis.

Data Science SkillsThe majority of the data science models are built using one or more, known or unknown variables. Thus, the in-depth understanding of multivariate calculus is the key requirement to develop Machine Learning models.

A detailed understanding of functions such as Logit, Cost, rectified Linear unit, Step, Sigmoid, etc. is very much required to deal with the large data. Apart from these functions, the detailed understanding of Matrix algebra.

vector Algebra and Differential and Integral calculus help the Data Scientists to develop and understand the systems at a faster pace.

 Programming Skills for Data Science

In order to achieve the objective to transform the raw data into business insights, Programming skills plays a crucial role. Among all the programming languages, the go-to languages are Python and R, Python being the lingua franca in the data science field.

Skill to wrangle the Data

The process of removing imperfections from the raw data to get the data that can be easily analyzed is known as Data Wrangling. The entire process includes acquiring the data, combining the data with relevant fields, and cleansing the data. In short mapping the raw data from one form to the other to set up the data to get business insights.

Management Skills

Database management is a prerequisite of Data analysis. The basic requirements for a Database Management System is the family of programs to edit and manipulate the data and the operating system to provide the specific data.

Data Science Career 

The special skills set will definitely make you stand out from the crowd when the field and hence the number of jobs in the market are increasing at a faster pace.

Data Science Career Job Requirements Average salary
Data Scientist ·      Data collection and organization

·      Find the pattern in the data to help the strategic business
decision

  

$139,840
Data Engineer ·      Batch Processing of the database

·      Build and maintain data pipelines

·      Make the information available to the Data Scientists

 $102,864
Machine Learning Scientist Research for the new data approaches and deep learning techniques. $114,121
Machine Learning Engineer ·      Create data funnels

·      In-depth understanding of statistics and programming

·      Designing and developing machine learning systems

 $114,826
Data Analyst ·      Transform the large Database to meet the purpose.

·      Prepare the reports to facilitate the decision-making process by communicating trends and insights from the data.

 $62,453
Business Intelligence Developer ·      Design and develop the strategies to make the specific information accessible for business decisions in lesser time.

·      Facilitate the system understanding to the end-users to use the data effectively

 $81,514
Statistician ·      Facilitate the Data Collection process.

·      In-depth Data analysis

·      Data interpretation

·      Identify the relevant trends from the data

·      Design data collection processes

·      Advise the overall organizational strategy

 $76,884
Applications Developer ·   Keeping track of the applications used in the business and internal interaction

·   Design the overall process flow of applications with the inclusion of development of user interface components etc.

 $113,757

 

 Average Salary data is taken from https://www.glassdoor.co.in/Salaries/data-scientist-salary

Why Does Data Ops For Data Science Project Matter?

Posted on by Imarticus Learning
Reading Time: 3 minutes

What is Data Science?

Data plays a major role in every organization as it helps in making decisions based on facts, statistics, and trends. Data science helps to trace insights from the raw data generated, which in turn is used to make major business decisions. Implementing Data Science in business has several advantages.

  • It helps in reducing risks and identifying fraud models. Data scientists are trained to identify data that stands out in some way and they use methodologies to predict fraud models along with creating alerts every time unusual data is identified.
  • It helps organizations in identifying when and where the products best sell. This helps the organization to deliver the right products at the right time as per the customers’ needs.
  • It helps the sales and marketing teams to understand their audience well and helps with providing personalized customer experiences.

Why Data Science Needs DataOps?

Data scientists deal with searching for data, labeling, cleaning, and performing other tasks that consume a lot of time. Especially if the business has to maintain a backlog legacy, then the amount of data keeps multiplying every year. This is where the need for DataOps rises.

DataOps involves collaboration, automation, and continuous innovation to data within a data-driven environment. Just like software can not be expected to provide exact results outside its live environment, data projects may also tend to behave similarly and may have to be reworked completely to make it work in a production environment. It also has to be continuously monitored even after deployment. Which makes it even more necessary to implement DataOps in a Data Science project.

Data Ops for Data ScienceDataOps plays a major role in building best practices throughout a function. Through continuous production, DataOps helps organizations to deliver value to a range of stakeholders.

Another significance of using DataOps in Data Science is Automation. Data moves through a particular process within an organization. While Data is entered in one form, it does not exist in the same form. Data scientists have to build data pipelines, test, and change them before data is deployed.

Making use of DataOps best practices, you can get a constant stream of data flowing through the pipelines. Which in turn, helps to attain real-time insights from the data. This ensures to reduce the time taken in converting raw data into Valuable information.

Combining Machine Learning with DataOps helps in maintaining a continuous workflow through internal communication. With this, the data quality can be controlled through version control, constant development, and integration. Combining ML also improves the insights and has a great potential for extracting value from DataOps.

Introducing DataOps in the organization also means changes in the work process. It builds a new ecosystem with consistent communication between the departments. Employees of each department work together, in real-time, sharing a common goal.

Therefore, using DataOps in Data Science ensures to develop projects keeping in mind the business impact along with delivering it in a way that the management can understand.

Why Data Science Course?

The Data Science course covers a mix of topics like mathematics, Tools, Machine Learning techniques, Business Acumen, and several algorithms. The main principle behind Data Science is finding patterns from gigabytes of raw data collected.

In today’s competitive world, more and more organizations are opening up to big data, and the need for data scientists is also on the rise. They get exciting opportunities to work on and also get to come up with solutions for businesses.