Facts You Need to Know About Data Science





Are you considering pursuing data science? or perhaps you want to change your career path to a data scientist? If so, then you go to know the upcoming information on data science.

Data science is an interdisciplinary domain that uses scientific methods, processes, algorithms, and systems to obtain knowledge and insights from structured and unstructured data and implement that knowledge and actionable insights from data across an extensive range of application domains.

What do data scientists do?

This is one of the most frequently asked or wondered questions.

Primarily, there are three kinds of jobs in data science: Data Analyst, Data Engineer, and Data Scientists. This classification solely depends on the company's inclinations.

If it is a prominent firm, then, there will be three kinds of posts to choose from according to the individual's qualification and interests. However, if the organization is subordinate, then all of the duties (data analyst, data engineer, and data scientist) are done by the same individual, which is a comparably hard/stressful task.

Data Analyst duties and responsibilities:

Data Analysts often make recommendations about the methods and ways during which a corporation obtains and analyses data to enhance the standard and therefore the efficiency of knowledge systems. a knowledge Analyst description should include, but not be limited to:




1) Collecting and interpreting data

2) Analyzing results

3) Summarizing the results back to the relevant members of the business

4) Identifying patterns and trends in data sets

5) Working alongside teams within the business or the management team to determine business needs

6) Defining new data collection and analysis processes.


Skills required:

1) Computer Science

2)
Statistics

3)Mathematics

4) Economics

5) Ability to analyse large datasets

6) Ability to write down comprehensive reports

7) Strong verbal and written language skills

8) An analytical mind and inclination for problem-solving

9) Attention to detail

Data engineer’s duties and responsibilities:

Data engineers are vital members of any enterprise data analytics team, liable for managing, optimizing, overseeing, and monitoring data retrieval, storage, and distribution throughout the organization.

Data engineers are liable for finding trends in data sets and developing algorithms to assist make data more useful to the enterprise. This IT role requires a huge set of technical skills, which includes a deep knowledge of SQL database sustem and multiple programming languages. But data engineers also need communication skills to operate across departments to know what business leaders want to realize from the company’s large datasets.


Data engineer’s Responsibilities:


1) Develop data set processes

2) Use programing language and tools

3) Identify ways to enhance data reliability, efficiency, and quality

4) Conduct research for industry and business questions

5) Use large data sets to deal with business issues

6) Deploy sophisticated analytics programs, machine learning, and statistical methods

7) Prepare data for predictive and prescriptive modeling

8) Find hidden patterns using data

9) Use data to get tasks that will be automated


Skills required:


The skills on your resume might impact your salary negotiations — in some cases by quite 10 or 15 percent, counting on the skill. consistent with data from PayScale, the subsequent data engineering skills are related to a big boost in reported salaries:

· Python

· Apache Spark

· Scala

· Data warehouse

· Java

· Data modeling

· Apache Hadoop

· ETL (extra, transform, load)

· Linux

· Amazon Web Services (AWS)

· Big data analytics

· Software development

Though the above-mentioned skills may boost your remuneration, it is not necessary to have all the skills introduced above. Knowledge in Python or R programming language and cloud computing concepts will greatly help.

Data Scientist Role and Responsibilities:

Data scientists work intently with business stakeholders to know their objectives and determine how data are often want to achieve those goals. The design data modeling processes create algorithms and predictive models to extract the info the business needs and help analyze the info and share insights with peers. While each project is different, the method for gathering and analyzing data generally follows the below path:


· Ask the proper inquiries to begin the invention process

· Acquire data

· Process and clean the info

· Integrate and store data

· Initial data investigation and exploratory data analysis

· Choose one or more potential models and algorithms

· Apply data science techniques, like machine learning, statistical modeling, and       AI

· Measure and improve results

· Present outcome to stakeholders

· Repeat the method to unravel a replacement problem

Skills required:

1) Statistical analysis

2) Machine learning

3) Computer science (SQL, Python/R programming language)

4) Data storytelling (Communicate insights to a non-technical audience)

5) Analytical thinking

6) Critical thinking

7) Interpersonal skills

Changing your career path to data science is a huge step since it needs a lot of thinking to do (problem-solving, implementing algorithms, etc..) and to deal with a lot of ‘data’. It is especially challenging for people who are very much used to code (creating websites and applications etc..). But, be it as it may, data science has its own big share of fun and a whole new level of advantages. Thus, it is best to follow your heart and pursue the career you desire.

Written By - Kirthiga Morais P
Edited By - Daniel Deepak Charles

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