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IBM predicts that the data related jobs in the U.S. will rise to 2,720,000 by the year 2020. Thus, there is no denying to the fact that data professionals such as data analyst, data engineers, and data scientists will not be in-demand.
However, there is still a slight misconception with the job roles and the skills this profession entails. Most importantly, one first must understand these different job roles before taking that plunge.
Before you take that decision, you should know the job roles and the differences between data analyst, data scientist and a data engineer.
A data analyst is someone who analyzes the numeric data and uses this data to help companies and organizations make better decisions. Most data scientists have started their career as a data analyst. Not all data analyst is of the junior level, they possess the potentiality of transforming traditional businesses into a data-driven business.
Their core responsibility is to help companies track progress and optimize their focus.
The common task of a data analyst or a big data analyst includes cleaning and organizing raw data, using statistics to get a clearer view of their data, analyze the trends found in the data, create visualizations that help companies interpret the findings making decisions with the data, and presenting these results to clients or team members.
To get into a data analyst profile one should at least have a bachelor’s degree in statistics or computer science or any related field. Having additional programming experience is an added advantage. Besides these, companies will expect you to have skills such as data modeling, data handling, and reporting techniques.
Data scientist are skilled in analyzing large and complex data. A certified data science professional possesses the ability to use their expertise in statistics in building machine learning models that help predict the future of the company. These are done by taking up the past data and analyzing it using statistical analysis and applying predictive models to predict the future outcome. For instance, if an analyst is focused on understanding the data from the past in comparison with the present. Then the data scientist’s core focus involves on predicting positive insights for the future based on the past data and the present data.
A data scientist’s job is to unravel hidden data insights by leveraging both supervised and unsupervised learning methods toward machine learning models. This takes place to have a better understanding of the patterns to derive accurate predictions.
The role and responsibility of a data scientist include evaluating statistical models to determine the validity of analyses. Then they use machine learning algorithms that help them build predictive algorithms. Testing the accuracy of the machine learning models and building data visualization to summarize the conclusion of the analyses.
The skills required for a professional to get into data science includes skills such as R and Python programming, statistics, machine learning algorithms, data visualization tools such as Tableau, ggplot, etc. databases such as MongoDB, SQL.
They’re the minds behind the system that allows data scientists and analysts to perform their work. Data analytics professionals or data engineers are responsible for constructing data pipelines and require to use complex tools and techniques in handing large data.
Their core responsibilities include building APIs for data consumption, integrating new dataset into existing data pipelines, applying feature transformation for machine learning models, etc.
The skills required are data warehousing, programming knowledge, Hadoop-based analytics, in-depth knowledge of SQL, machine learning, etc.
Now that you’re acquainted with these job roles you must follow the right career path in grasping the required skillset.
When it comes to choosing a data-driven career path, expert professionals have transitioned toward online learning to stay upgraded in the latest technology job roles.
Big data engineer certification and data science certification are the best ways to get hands-on experience and learning an ability that will prove beneficial for professionals looking to enter the data realm.
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