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Career as a Data Engineer: Scope, skills needed, job profile and other details


With a humongous 2.5 quintillion bytes of data engendered every day, data scientists are more diligent than at any other time. The more data we have, the more we can do with it. Furthermore, data science gives us strategies to efficaciously utilize this data. It just bodes well that software engineering has developed to incorporate data engineering adeptness, a subdiscipline that fixates on the conveyance, change, and storage of data.

Who is data engineer?

Data Engineer is a person who is responsible for managing data workflows, pipelines, and ETL processes. As the denomination suggests, “Data Engineering”, denotes it is associated with data, namely, their distribution, storage, and processing. In short Data Engineer is a person who collects, move, store, and pre-process the data for Data Scientist and Data Analyst.

What does data engineer do?

Data engineers involve in preparing data for analytics or operational users. They withal build data pipelines to pull all the information together from different sources.

The aim of a Data Engineer is to make data secure and accessible for data scientists and analysts so that they can analyze it felicitously. Data engineers deal with raw data that often contains an abundance of errors.

Data engineers use sundry implements and ways to ameliorate the quality, reliability, and efficiency of data. You will understand more about Data Engineering in the next section- Roles and Responsibilities.

Qualification required for a data engineer

As a Data Engineer, you just need an Undergraduate degree in Computer Science, IT, Software Engineering, Math, or a business-cognate field. So, this is the required qualification for Data Engineers, but only having a degree is not enough. You should have some required skills in order to become a Data Engineer.

Skills required to become data engineer

Data engineers need to be comfortable with a wide array of technologies and programming languages. These are perpetually subject to transmute, so one of the most consequential skills that a data engineer possesses is the underlying cognizance for when to employ which language and for what purport. Data engineers must be fascinated with perpetually updating their technical adeptness-sets. A good data engineer will possess erudition of and skills in all the following:

  • Building and designing astronomically immense-scale applications
  • Database architecture and data warehousing
  • Data modeling and mining
  • Statistical modeling and regression analysis
  • Distributed computing and splitting algorithms to yield predictive precision
  • Proficiency in languages, especially R, SAS, Python, C/C++, Ruby Perl, Java, and MatLab
  • Database solution languages, especially SQL, as well as Cassandra, and Bigtable
  • Hadoop-predicated analytics, such as HBase, Hive, Pig, and MapReduce
  • Operating systems, especially UNIX, Linux, and Solaris
  • Machine learning, including AForge.NET and Scikit-learn


Conclusion:

Skills for any expert relate to the obligations they’re responsible for. The range of skills would vary, as there is a wide range of data engineer key skills. However, for the most part, their tasks can be arranged into three primary territories: engineering, data science, and databases/warehouses.

About Sankhyana: Sankhyana Consultancy Services is India’s Premium and best data analytics training institute in India offers the best classroom, online/ live- web, corporate & academia Training on SAS & Data Management tools. Our programs feature instructor-led classroom and real-world projects to ensure you get hands-on experience and relevant skills.

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Thursday, 30 September 2021

What is Machine Learning? Types of Machine Learning – Sankhyana Education

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the conception that systems can learn from data, identify patterns, and make decisions with minimal human intervention. This super-powerful, enabling technology is one of the most sought-after technical skills to have in this data-driven world.

In this article, we will discuss about types of machine learning. There are three types of machine learning.

  1. Supervised Learning
  2. Un-Supervised Learning
  3. Reinforcement Learning

Supervised Learning:  Supervised machine learning builds a model that makes presages predicated on evidence in the presence of uncertainty. A supervised learning algorithm takes a kenned set of input data and kenned replications to the data (output) and trains a model to engender plausible prognostications for the replication to incipient data. Utilize supervised learning if you have kenned data for the output you are endeavoring to predict. Supervised learning uses relegation and regression techniques to develop predictive models.

Supervised machine learning includes two major processes: classification and regression.

  • Classification is the process where incoming data is labeled predicated on past data samples and manually trains the algorithm to apperceive certain types of objects and categorize them accordingly. The system must ken how to differentiate types of information, perform an optical character, image, or binary apperception (whether a bit of data is compliant or non-compliant to categorical requisites in a manner of “yes” or “no”).
  • Regression is the process of identifying patterns and calculating the prognostications of perpetual outcomes. The system must understand the numbers, their values, grouping (for example, heights and widths), etc.

The most widely used supervised algorithms are:

  • Linear Regressions
  • Logistic Regression
  • Support Vector Machines (SVM)
  • Neural Networks
  • Decision Trees
  • Random Forest

Un-Supervised Learning: Unsupervised learning finds hidden patterns or intrinsic structures in data. It is utilized to draw inferences from datasets consisting of input data without labeled replications.

Unsupervised learning algorithms apply the following techniques to describe the data:

Clustering: it is an exploration of data used to segment it into paramount groups (i.e., clusters) predicated on their internal patterns without prior erudition of group credentials. The credentials are defined by a homogeneous attributes of individual data objects and withal aspects of its dissimilarity from the rest (which can additionally be habituated to detect anomalies).

Dimensionality reduction: there is an abundance of noise in the incoming data. Machine learning algorithms use dimensionality truncation to abstract this noise while distilling the pertinent information.

The most widely used unsupervised algorithms are:

  • k-denotes clustering
  • t-SNE (t-Distributed Stochastic Neighbor Embedding)
  • PCA (Principal Component Analysis)
  • Association rule


Reinforcement Learning:  This is mainly utilized in navigation, robotics, and gaming. Actions that yield the best rewards are identified by algorithms that use tribulation and error methods. There are three major components in reinforcement learning, namely, the agent, the actions, and the environment. The agent in this case is the decision-maker, the actions are what an agent does, and the environment is anything that an agent interacts with. The main aim of this kind of learning is to cull the actions that maximize the reward, within a designated time. By following a good policy, the agent can achieve the goal more expeditiously.

The most widely used reinforcement learning is:

  • Q-Learning
  • Temporal Difference (TD)
  • Monte-Carlo Tree Search (MCTS)
  • Asynchronous Actor-Reprover Agents (A3C)

Conclusion: Machine learning is everywhere. Machine learning can provide value to consumers as well as to enterprises. An enterprise can gain insights into its competitive landscape and customer allegiance and forecast sales or demand in authentic time with machine learning. Machine Learning algorithms can help us to solve many problems and make new discoveries.

Hope you got some idea about machine learning types.

Reach us:+254 740288931 | veronica.wahome@sankhyana.com

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