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Data Science (10 blogs)

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Last updated on Nov. 16, 2020, 2:57 p.m. 680 Views



He is a tech-expert with 7 years of industrial experience in Python, Data Analysis, Big Data, Machine Learning and NLP. He has 360 degrees of expertise in all these subjects. He is known for his practical approach to different real-time industrial problems. He is known for his great interest in helping students reach their true potential and scale greater heights. Believing in Problem-based teaching pedagogies, he left his job in Malaysia as a data engineer and came back to the newly born state to fill the void between students and the industry.


Last updated on Nov. 16, 2020, 2:57 p.m. 680 Views


After hearing the term “Data Scientist”, what’s the first thing that came to your mind? You might be thinking that Data Scientist is the person who deals with a giant amount of data and perform scientific analysis on it. Well, you are partially right if you think the same. From which breed Data Scientists belongs to? Data Scientists are often seen dealing with clients. They collaborate a large amount of unstructured data, excerpt relevant data and analyse data to come up with the required insights. This data is gathered from various sources like email systems, databases, and social media with the use of the input of emails, pictures, and chat messages. This data is structured by using multiple techniques, algorithms, creating models to predict the future.

Data Scientist’s purpose is to create value from data through analytics

This buzzword of Data Scientist is used for any activity that has to do with the creation of business value that is done from Data. You can say Data Scientists do everything but that everything is nothing! If you ask a Data Scientist what tasks he does at the job, you’ll realize every job offer is different ranging from database admin to a data analyst. Data Scientist is a really broad and ambiguous term.


The most demanding job of the decade

The profession of Data Scientist is popular, successful, and mature

Do you know, when computers were first invented, people who were the first computer engineers were known to be the sexy job of 1990. The criterion is now with Data Scientists. It simply means the people with rare qualities are the top players. Also, think of Big Data, how big its requirement is! If you want to catch such waves, you need people who can surf. Data Scientists are the ones! Funny fact is that – Data Scientists are like oregano on delicious pizza.

Our online conduct via social networking sites, e-commerce sites and more are touchpoints of information and these are utilized by Data Scientists to furnish us with a superior shopping experience, to comprehend us better and convey quality help. The interesting perspective is that the volume of data produced is actually the motivation behind why Data Scientists are in colossal demand today.


Is becoming a Data Scientist really sexy?


Do you know: Companies are on serious constraints of Data Scientists in various sectors

Various companies are actually wrestling with information that is in the varieties and volumes that are never come crossed before. If your association stores numerous petabytes of data, if the data generally basic to your business dwells in structures other than columns and rows of numbers, or if addressing your greatest inquiry would include a "mashup" of a few scientific endeavours, you have received a big data opportunity. Due to this, companies actually are in the requirement of people who can manage it and find insights in it.

What does a Data Scientist do?

If you want your data to be valued, you need a Data Scientist. A Data Scientist or engineer might be X% scientist, Y% programming specialist, and Z% hacker, which is the reason the meaning of this job becomes complicated. The real proportions depend upon the aptitudes required and kind of job. As a rule, it's viewed as ordinary to bring individuals with various arrangements of abilities into the Data Science group.

Data Scientist obligations normally incorporate creating different ML-based devices or procedures within the organization, for example, suggestion engines or automated lead scoring frameworks. Individuals inside this job ought to perform the statistical investigation.

Job responsibilities of a Data Scientist:

  • Work with stakeholders all through the organization to distinguish opportunities for utilizing organization data to drive business solutions. 
  • Examine data from organization databases to drive streamlining and improvement of item development, marketing systems, and business techniques. 
  • Evaluate the viability and precision of new data sources and data gathering procedures.
  • Create custom data models and calculations to apply to informational collections. 
  • Utilize predictive modeling to increment and improve client experiences, income generation, focus on business results. 
  • Create organization A/B testing structure and test model quality. 
  • Organize with various practical groups to actualize models and screen results. 
  • Create procedures and devices to screen and break down model execution and data accuracy.

Skills and qualifications of a Data Scientist:

  • Solid problem-solving aptitudes with an emphasis on the development of the product.
  • Experience utilizing statistical scripts (R, Python, SQL, and so on) to control information and draw bits of knowledge from the large collection of data. 
  • Experience working with and making information designs.
  • Knowledge on an assortment of AI methods (clustering, decision tree learning, artificial neural networks, so forth) 
  • In-depth information on advanced techniques and ideas (regression, properties of distributions, statistical tests, and proper usage, so forth) and experience in applications. 
  • Astounding composed and verbal relational abilities for planning across groups. 
  • A drive to learn and ace new innovations and techniques 

Technical skills required:

A person with experience in the manipulation of Data sets and building measurable models, has a Master's or Ph.D. in Statistics, Mathematics, Computer Science or another quantitative field, and knows about the accompanying programming/apparatuses: 

  • Coding knowledge and experience with C, C++, Java, JavaScript, etc. 
  • Knowledge and involvement with factual and information mining systems: GLM/Regression, Random Forest, Boosting, Trees, content mining, interpersonal organization investigation, and so forth. 
  • Experience with languages like R, Python, SQL, etc. 
  • Experience utilizing web services like Redshift, S3, Spark, DigitalOcean, and so on. 
  • Experience in making ML algorithms and measurement regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
  • Experience in analyzing information from third party suppliers: Google Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, Facebook Insights, and so on.
  • Experience in cloud computing tools Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, and so forth.
  • Experience visualization for stakeholders utilizing: Periscope, Business Objects, D3, ggplot, and so forth.

 So, what are your next steps?

  As per the prediction of IBM, the demand for Data Scientists will rise by 28% in the near future. Also, as per the analysis on job requirements of Data Scientists, Glassdoor says, Data Science is at counted at the 15th highest paid job in the USA. Data Scientist is a sexy job means it has rare qualities that are so much in demand. The market for talented Data Scientists is profoundly expensive to hire and considerably hard to hold. There aren't many individuals with their mix of scientific background and computational and analytical abilities. It's like the quant lack in the '80s and '90s when banks were paying oodles of cash for anybody with science and mathematics abilities. As the ascent of University programs showing interest in Data Science, the demand for Data Scientists will be in great demand and pay rates will level out. Meanwhile – Stay Data Scientific folks!

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