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10 Areas of Expertise in Data Science

The analytics market is booming, and so is the use of the keyword – Data Science. Professionals from different disciplines are using data in their day to day activities, and feel the need to master the start-of-the-art technology in order to get maximum insights from the data, and subsequently help the business to grow.

Moreover, there are professionals who want to keep them updated with this latest skills such as Machine Learning, Deep Learning, Data Science, and so either to elevate their career or move to a different career altogether. The role of a Data Scientist is regarded as the sexiest job in the 21stcentury making it increasingly lucrative for most people to turn down.

However, making a transition to Data Science, or starting a career in it as a fresher is not an easy task. The supply-demand gap is gradually diminishing as more, and more people are willing to master this technology. There is often a misconception among professionals, and companies as to what Data Science is, and in many scenarios the term has been misused for various small scale tasks.

To be a Data Scientist, you need to have a passion, and zeal to play with data, and a desire to make digits and numbers talk. It is a mixture of various things, and there are a plethora of skills one has to master to be a called a Full Stack Data Scientist. The list of skills often gets overwhelming for an individual who could quit, given the enormity of its applications, and a continuous learning mindset the field of Data Science demands.

In this article, we would walk you through the ten areas in Data Science which are a key part of a project, and you need to master those to be able to work as a Data Scientist in much big organization.

  • Data Engineering – To work in any Data Science project, the most important aspect of it is the data. You need to understand which data to use, how to organize the data, and so on. This bit of manipulation with the data is done by a Data Engineer in a Data Science team. It is a superset of Data Warehousing and Business Intelligence which included the concept of big data in the context.

Building, and maintain a Data warehouse is a key skill which a Data Engineer must have. They would prepare the structured, and the unstructured data to be used by the Analytics team for model building purpose. They build pipelines which extract data from multiple sources and then manipulates it to make it usable.

Python, SQL, Scala, Hadoop, Spark, etc., are some of the skills that a Data Engineer has. They should also understand the concept of ETL. The data lakes in Hadoop is one of the key areas of work for a Data Engineer. The NoSQL database is mostly used as part of the data workflows. Lambda architecture allows both batch and real-time processing.

Some of the job role available in the data engineering domain is Database Developer, Data Engineer, etc.

  • Data Mining – It is the process of extracts insights from the data using certain methodologies for the business to make smart decisions. It distinguishes the previously unknown patterns and relationships from the data. Through data mining, one could transform the data into various meaningful structures in accordance with the business. The application of data mining depends on the industry. Suppose in finance, it is used in risk or fraud analytics. In manufacturing, product safety, and quality issues could be analyzed with accurate mining. Some of the parameters in data mining are Path Analysis, Forecasting, Clustering, and so on. Business Analyst, Statistician are some of the related jobs in the data mining space.
  • Cloud Computing – A lot of companies these days are migrating their infrastructure from local to the cloud merely because of the ready-made availability of the resources, and the huge computational power which not always available in a system. Cloud computing generally refers to the implementation of platforms for distributed computing. The system requirements are analyzed to ensure seamless integration with present applications. Cloud Architect, Platform Engineer are some of the jobs related to it.
  • Database Management – The rapidly changing data makes it imperative for the companies to ensure accuracy in tracking the data on a regular basis. This minute data could empower the business to make time strategic decisions, and maintain a systematic workflow. The collected data is used to generate reports and is made available for the management in the form of relational databases. The Database management system maintains a link among the data, and also allows newer updates. The structured format in the form of databases helps management to look for data in an efficient manner. Data Specialist, Database Administrator are some of the jobs for it.
  • Business Intelligence – The area of business intelligence refers to finding patterns in historical data of a business. Business Intelligence analysts would find the trends for a data scientist to build predictive models upon. It is about answering not-so-obvious questions. Business Intelligence answers the ‘what’ of a business. Business Intelligence is about creating dashboards and drawing insights from the data. For a BI analyst, it is important to learn data handling, and masters the tools like Tableau, Power BI, SQL, and so on. Additionally, proficiency in Excel is a must in business intelligence.
  • Machine Learning – Machine Learning is the state-of-the-art methodology to make predictions from the data, and help the business make better decisions. Once the data is curated by the Data Engineer and analyzed by a Business Intelligence Analyst, it is provided to a Machine Learning Engineer to build predictive models based on the use case in hand. The field of machine learning is categorized into supervised, unsupervised, and reinforcement learning. The dataset is labeled in supervised unlike in unsupervised learning. To build a model, it is first trained with data to let them identify the patterns and learn from it to make predictions on the unknown set of data. The accuracy of the model is determined based on the metric, and the KPI used which is decided by the business beforehand.
  • Deep Learning – Deep Learning is a branch of Machine Learning which h uses neural network to make predictions. The neural networks work similar to our brain and makes builds predictive models compared to the traditional ML systems. Unlike in Machine Learning, no manual feature selection is required in Deep Learning but huge volumes of data and enormous computational power is needed to run deep learning frameworks. Some of the Deep Learning frameworks like TensorFlow, Keras, PyTorch.
  • Natural Language Processing – NLP or Natural Language Processing is a specialization in Data Science which deals with raw text. The natural language or speech is processed using several NLP libraries, and various hidden insights could be extracted from it. NLP has gained popularity in recent times with the amount of unstructured raw text that’s getting generated from a plethora of sources, and the unprecedented information that those natural data carries. Some of the applications of Natural Language Processing are Amazon’s Alexa, Google’s Siri. Even many companies are using NLP for sentiment analysis, resume parsing, and so on.
  • Data Visualization – Needless to say, the importance of presenting your insights either through scripting or with the help of various visualization tools. A lot of Data Science tasks could be solved with an accurate data visualizations as the charts, and the graphs presents enough hidden information for the business to take relevant decisions. Often, it gets difficult for an organization to build predictive models, and thus they rely on only visualizing the data for their workflow. Moreover, one needs to understand which graphs or charts to use for a particular business, and keep the visualization simple, as well as informative.
  • Domain Expertise – As mentioned earlier, professionals from different disciplines are using data in their business, and thus its wide range of applications makes it imperative for people to understand the domain they are applying their Data Science skills. The domain knowledge could be operations-related where you would leverage the tools to improve the business operations that could be focused on financials, logistics, etc.  It could also be sector specific such as Finance, Healthcare, etc.

Conclusion –

Data Science is a broad field with a multitude of skills, and technology that needs to be mastered. It is a life-long learning journey, and with frequent arrival of new technologies, one has to update themselves constantly.

Often it could be challenging to keep up with some frequent changes. Thus it is required to learn all these skills, and at least be a master of one particular skill. In a big corporation, a Data Science team would comprise of people assigned with different roles such as data engineering, modeling, and so on. Thus focusing on one particular area would give you an edge over others in finding a role within a Data Science team in an organization.

Data Scientist is the most sort after job in this decade, and it would continue to be so in years to come. Now is the right time to enter this field, and Dimensionless has several blogs and training to get started with Data Science.

Follow this link, if you are looking to learn more about data science online!

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