Data Scientist

Data scientists are analytical experts who utilize their skills in both technology and social science to find trends and manage data. They use industry knowledge, contextual understanding, skepticism of existing assumptions – to uncover solutions to business challenges.Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, deep learning, and big data.

Because big data is a rapidly growing field, there are constantly new tools available, and those tools need experts who can quickly learn their applications. Data scientists can help companies create a business plan to achieve goals based on research and not just intuition.Some professionals who might engage in data science work or become full-time data scientists include computer scientists, database and software programmers, disciplinary experts, curators, and expert annotators and librarians. Job postings for data scientists may also advertise the opening as “machine learning architect” or “data strategy architect.”

Data science plays a very important role in security and fraud detection because the massive amounts of information allow for drilling down to find slight irregularities in data that can expose weaknesses in security systems.Data scientists generally need enough educational or experiential background to complete a wide range of extremely complex planning and analytical tasks in real time. While a specific job might call for specific qualifications, most to all data science roles require at bare minimum a bachelor’s degree in a technical field.

Data science is a driving force between highly specialized user experiences created through personalization and customization. The analysis can be used to make customers feel seen and understood by a company. The concept of data scientists is derived from some of the most important major technological modern fields, including science, math, statistics, chemometrics, and computer science. The mix of personality traits, experience, and analytics skills required for this role are rare, so the demand for qualified data scientists is in an upward swing.

Data science is a highly interdisciplinary practice involving a large scope of information and one that usually takes into account the big picture more than other analytical fields. In business, the goal of data science is to provide intelligence about consumers and campaigns and help companies create strong plans to engage their audience and sell their products.

Data scientists must rely on creative insights using big data, the large amounts of information collected through various collection processes, like data mining.

On an even more fundamental level, big data analytics can help brands understand the customers who ultimately help determine the long-term success of a business or initiative. In addition to targeting the right audience, data science can be used to help companies control the stories of their brands.

Data Scientist Technology:


  • R
  • Python
  • SQL
  • Hadoop
  • Tableau
  • Data Mining
  • Deep Learning
  • Big Data
  • Programming: Python, SQL, Scala, Java, R, MATLAB
  • Machine Learning: Natural Language Processing, Classification, Clustering, Ensemble methods, Deep Learning
  • Data Visualization: Tableau, SAS, D3.js, Python, Java, R libraries
  • Big data platforms: MongoDB, Oracle, Microsoft Azure, Cloudera

A data scientist builds an analysis on top of data. This may come in the form of a one-off analysis for a team trying to better understand customer behavior or a machine learning algorithm that is then implemented into the codebase by software engineers and data engineers.
A data scientist is the alchemist of the 21st century: someone who can turn raw data into purified insights. Data scientists apply statistics, machine learning and analytic approaches to solve critical business problems. Their primary function is to help organizations turn their volumes of big data into valuable and actionable insights.

Indeed, data science is not necessarily a new field per se, but it can be considered as an advanced level of data analysis that is driven and automated by machine learning and computer science. In another word, in comparison with ‘data analysts’, in addition to data analytical skills, Data Scientists are expected to have strong programming skills, an ability to design new algorithms, handle big data, with some expertise in the domain knowledge.

Moreover, Data Scientists are also expected to interpret and eloquently deliver the results of their findings, by visualization techniques, building data science apps, or narrating interesting stories about the solutions to their data (business) problems.

The problem-solving skills of a data scientist requires an understanding of traditional and new data analysis methods to build statistical models or discover patterns in data. For example, creating a recommendation engine, predicting the stock market, diagnosing patients based on their similarity, or finding the patterns of fraudulent transactions.

Data Scientists may sometimes be presented with big data without a particular business problem in mind. In this case, the curious Data Scientist is expected to explore the data, come up with the right questions, and provide interesting findings! This is tricky because, in order to analyze the data, a strong Data Scientists should have a very broad knowledge of different techniques in machine learning, data mining, statistics and big data infrastructures.

They should have experience working with different datasets of different sizes and shapes, and be able to run his algorithms on large size data effectively and efficiently, which typically means staying up-to-date with all the latest cutting-edge technologies. This is why it is essential to know computer science fundamentals and programming, including experience with languages and database (big/small) technologies.

Work includes:

  • Data modeling
  • Machine learning
  • Algorithms
  • Business Intelligence dashboards

Skills: Python, R, Scala, Apache Spark, Hadoop, machine learning, deep learning, and statistics.
Tools: Data Science Experience, Jupyter, and RStudio.

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