Best 7 Data Science Skills You Should Not Miss

By Guest Contributor on August 27, 2019

Leveraging big data as an insight-generating engine has driven the demand for data scientists across all industry verticals. As the demand for data scientists advances, it extends an enticing career path for students as well as professionals. While a fruitful career choice, it requires a deep understanding of the business world and a set of critical traits in order to become successful data scientists in today’s competitive marketplace. Following are some of the skills that companies look out for in a data scientist:-

  • Critical Thinking

Being a critical thinker is one of the most imperative skills as it allows data scientists to perform objective analysis on the facts of a particular subject matter or problems, before offering the right solution.

  • Expertise in Mathematics

Data scientists engage with clients who are looking to develop operational and financial models for their companies, and this involves the analysis of a large amount of data. They leverage their expertise in math to formulate accurate statistical models that further serve as the basis for developing important strategies and facilitating approvals on decisions.

  • Proficiency in Coding

Data Science Course Using R, Excel, Python enables data scientists to write code and efficiently deal with complex tasks associated with coding. To be a successful data scientist, one must have programming skills that include computational aspects (cloud computing, unstructured data, etc.) and statistical aspects (regression optimization, clustering, random forests, etc).

  • Understanding of AI, Machine Learning, and Deep Learning

Owing to advanced connectivity, computing power, and collection of enormous data, companies are increasingly leveraging technologies like AI, machine learning, and deep learning. To be a successful data scientist, one must have extensive knowledge of these technologies and possess the ability to identify which technology to apply in order to avail the most effective results.

Comprehending Data Architecture

From interpretation to the decision-making process, it is important that data scientists understand how the data is being used. Not understanding the data architecture can seriously impact the interpretations that might result in businesses making inaccurate decisions.

  • Good Business Intuitions

Data scientists must look at the business world from various perspectives to understand what needs to be done, and consecutively build strategies in achieving the end result. Therefore, good business intuition and a problem-solving approach are the two common skills that every company looks out for when hiring a data scientist.

  • Ability to Analyze Risk 

A skilled data scientist should be able to understand the concepts of business risk analysis, how systems engineering works and needs to make improvements in the existing process. Risk analysis in the initial stages of the model development allows businesses to mitigate any unforeseen risks and make profitable decisions with care.

Data science is a multi-disciplinary domain that requires professionals to hold a strong knowledge base (through data science course) and domain-specific expertise. According to a recent study by IBM, the demand for data scientists will increase by 20% by 2020. Above are some of the imperative skills that data scientists must possess in order to carve a successful career path in the corporate domain.

This article is contributed by Sid Rawat, Certified Data Science Analyst at TalentEdge.

How is Hadoop helping companies deal with Big Data challenges?

By Guest Contributor on March 21, 2019

Today’s world runs on data. Almost every rideshare application, food order app, retail or shopping site, and even all e-commerce sites require consumer data to provide an optimally satisfying customer experience. As every aspect of the web and applications are becoming experience-driven, every corporation and company are thinking about monetizing their data. Unfortunately, with the rise of mobile computing and multi-device access, gargantuan volumes of data keep flowing in from all directions. The traditional database architecture is no longer sufficient to hold enormous amounts of data or organize it appropriately.

Why is dealing with Big Data a significant challenge?

Big Data usually flow into a heterogeneous environment that data scientists typically refer to as a data lake. They are different from data warehouses. The traditional warehouses of data have a comparatively uniform architecture that is either wholly definite or rigid. Some companies define their data lakes as modern data warehouses, primarily since they use Hadoop. Hadoop makes data collection, storage, and management quite straightforward even for the small businesses that are new to the world of Big Data.

Here are the currently available technologies that deal with Big Data technologies –

  • Traditional RDBMS including SQL databases
  • NoSQL database systems
  • Hadoop and other massively parallel computing technology

What are SQL databases?

RDBMS or relational database management system has been the standard response to all data storage and collection challenges people have faced in the near past. However, SQL databases are usually appropriate for a definite volume of data that has defined structure. Relational databases have been losing popularity in recent times as the age of Big Data dawns upon us. Big Data has massive volume, and it flows in at a tremendous velocity. It is highly variable that a traditional RDBMS database cannot tackle. It is not the primary scalable solution that meets every need for Big Data.

What are NoSQL databases?

NoSQL databases are taking over the data management landscape thanks to the rise of Big Data. Nonetheless, the much popular and time-tested structures are not enough to either store or analyze the ever-evolving nature of Big Data. Database admins now require something dynamic yet robust to tackle the management and analytical problems the new generation of data throws their way.

Unlike traditional SQL technology, NoSQL is flexible, and it is highly scalable. Most NoSQL database leaves room for the DBA to define and redefine data types and database structures. NoSQL allows the database admin to trade off rigid structures for agility and speed. It is the ideal requirement for Big Data management where the primary necessity is speed and not accuracy. Some of the most significant data warehouses including Google and Amazon now leverage the power of NoSQL to manage their unmeasurable bulk of data. Due to its incredible scalability, the users can continue to add more hardware as the data continues to explode.

What is Hadoop?

On the other hand, the state-of-the-art technological solutions that are capable of handling Big Data include the likes of Hadoop. It is not a database. It is a software ecosystem or framework of multiple software programs that support parallel computing. It does enable certain NoSQL database types to store and collect Big Data, like the HBase. It allows the expansion of data across multiple servers with little to no redundancy.

What is the role of MapReduce in the Hadoop framework?

MapReduce is a stable computational model of the Hadoop ecosystem. It plays a critical role in the determination of the intensive data processes from the ecosystem and spreads the computation throughout thousands (potentially endless) of servers. DBAs refer to this as a Hadoop cluster. Hadoop has standardized models that make data management a breeze for new companies and long-time running corporations. It comes with inherent fault tolerance. The data processing enjoys protection against hardware failure. Therefore, in case of a node malfunction, the job automatically goes to another node to ensure that the distribution computing remains continuous. In short, no matter how massive your data-load is, Hadoop has the solution.

Most companies that use Hadoop enjoy high flexibility of data types and scalable storage options at a low cost. Thanks to remote database management services the maintenance and updating of Hadoop enabled NoSQL databases has become a lot easier than it used to be. Users no longer require the presence of on-site DBAs for the optimization of database performance. Off-site database administration services can take care of updating, managing, caching and maintaining complete databases from remote locations. To know more about remote database management.

What are the most prominent uses of Hadoop right now?

Data analytics and predictive analytics — Most corporations and SMBs use Hadoop for analytics purposes. When there is a massive volume of data that require analysis, Hadoop is the primary choice for data scientists. It has the ability to store and process multiple data types simultaneously. That makes Hadoop the perfect fit for Big Data analytics and predictive analytics. Big Data environments are highly heterogeneous, and that consists of various information in structured, semi-structured and unstructured forms. Whether it is social media posts, social networking activities, clickstream records or customer emails, Hadoop has the agility and potential to store and sort it all.

Customer analytics — As a result, most companies use Hadoop for customer analytics purposes exclusively. One of its top functions is to predict customer behavior including conversion rates and track consumer emotions. Analysis like these utilizes information from social media activities of individual users and responses to corporate or promotional emails. E-commerce companies, healthcare organizations, and insurers often use Hadoop for analyzing promotional offers, treatment opportunities, and policy pricing respectively.

Predictive maintenance — Several manufacturers are now leveraging Hadoop in the maintenance of operations to determine equipment failure as they are about to happen. They are running real-time analytics applications including Apache Spark and Apache Flink along with Hadoop for improving their accuracy during prediction. The emergence of Hadoop as a robust and reliable prediction analytics tool has enabled the detection of online fraud, and cybercrime. It has also improved aspects of website and user interface (UI) design by gauging signs of customer satisfaction.

Hadoop has made its mark in the data management realm by attracting prominent IT vendors including Hortonworks, MapR, Cloudera and AWS. The Hadoop framework is attracting users and vendors from all across the globe. Its popularity is soaring along with the increasing importance of Big Data.

This article is contributed by Jack Dsouja, noted data analyst at

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