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Companies of the different domains have a variety of ways of defining job roles. Job titles do not entitle and do not reflect one’s true job responsibilities. In the IT industry, there are several jobs in the industry where the opinion differs about roles and skills and even creates a lot of confusion. Data scientists and data analysts are two different prominent job titles where different individuals seem to believe that a data scientist is alike the data analyst.

Both these job roles work with data and one of the key difference which lies between the two is that data analysts examine large data set for identifying trends and create charts, and visual presentation for helping the organization make more strategic decision whereas data scientist caters to design and construct various processes for data modeling and production with the help of prototype, etc.

PG in data science helps one in upskilling the requirement of the individual in any IT company. Various information available for different organizations to use in decision-making is exponentially more massive than it was. New technologies have developed to analyze and interrupt different amounts of data.

Data analyst: this is a skilled professional who is responsible for collecting data from various sources, organizing it, and even performing analysis for the same. Organizations create data in the form of log files, customer information, transaction data, etc.

This is the job role of a data analyst for transforming valuable company data into actionable insights. Data analysts use different data manipulation for analyzing and interpreting complex data sets which help businesses and companies make better and more reliable decisions.

Let us also have a look at the job description for data analyst:

  • Delivery reports.
  • Examining patterns.
  • Collaborate with the clients or stakeholders: data analyst role includes collaborating with different departments in the company including marketers and salespeople.
  • Consolidating data and setting up infrastructure: it is the technical aspect of the analyst’s job that collects data.
  • Perform analysis and interpretation of data using statistical techniques.
  • Extract data and store it in a database.
  • Perform data cleaning and data filtration.
  • Using exploratory data analysis for visual communication of data.
  • Working with a team in analyzing the business requirement.

Data Analyst Education:

Entry into data analytics requires a bachelor’s degree. Various fields of study mainly include information technology, computer science, and statistics. Minoring or taking additional classes in database management, and project management will make one marketable. As a data analyst, one must consider studying with a master’s degree or certificate course. The certificate program, Bootcamp, and independent study are the best method of advancing the skill in data analysis.

How Long Does One Take to Become a Data Analyst?

Many graduation degrees are completed in a time span of four years. If you wish to have an advanced degree, a master’s degree in data analysis takes about two years to complete. Learning specific system which is used in data analytics is done in a time frame of a few months.

There are certificate courses and boot camps in which one will get to know about SQL, Python, and advanced Microsft Excel skills. Data analysts even compete in a data analysis Bootcamp for increasing their skills in less than a year. Here, one will be prepared for real-world settings such as finance, healthcare, and business.

Data Scientists: 

They are skilled professionals who understand the challenges and opportunities and even develop the best solution with the help of modern tools and techniques. They use statistical methods, data visualization techniques, and machine learning for building predictive models and even solving complex problems. They are responsible for deriving effective information from unstructured data and they also communicate various information and insights to business leaders and different stakeholders.

Let us also have a look at the job description for a data scientist. 

Data scientist are mainly problem solvers as they help in seeking one to determine the needs one require. One must have a different approach and even solve the problem. Let us check the data related tasks that data scientists tackle on a day to day basis including:

  • Pulling, merging, and analyzing data.
  • Look for patterns or trends.
  • Use a wide range of tools such as Tableau, Python, Hive, Hadoop, etc for developing and testing different algorithms.
  • Trying to simply data problems and even develop predictive models.
  • Build data visulaization.
  • Data transformation as well as data cleansings.
  • Utilize machine learning for forecasting and pattern classification.
  • Perform the optimization of predicting models & tuning them.
  • Analyze the requirement of the company and formulate a question.
  • Perform interactive visualizations for communication results with the team.

Data Scientist Education:

For becoming a data scientist, a bachelor’s degree is expected. There are various colleges that help one in preparing for a career in data science. Popular courses include information technology, applied mathematics, computer science, economics, finance, etc. A career in a specific industry various courses such as artificial intelligence, mathematical biology, and computational science.

How Long Does it Take to Become a Data Scientist?

Data scientists need to have a bachelor’s degree which takes about four years to complete. There are various options available for individuals who are interested in studying data science. Becoming a data scientist needs huge curiosity, excellence in prioritizing tasks, and even consider communication skills which help one in dealing with complex topics into understandable information.

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Many bootcamps help for sharpening the skills and even gaining experience in which one can enter the field of data science. Data analysis Bootcamp teaches one to have technical skills which help one in moving forward in a practical, real-world environment.

Conclusion

When thinking of two disciplines, it is important to forget the view them as data science and data analytics. It is vital to understand that information is necessary and one must better analyze and find the best one to review.

A data engineer is always improving analytics solutions to guarantee that the data the company relies on is accurate and available. A good data engineer saves the company time and effort. A data engineer is in such great demand right now. Data engineers create a foundation for data analysts and scientists.

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