Common job Roles comes under data science.

 

Below is a general description of a few main job roles in the Data science field.

1. Data Scientist

Being a data scientist may be intellectually demanding, and analytically fulfilling, and it can put you at the cutting edge of new technological developments. As big data continues to be more crucial to how businesses make choices, data scientists are becoming more prevalent and in demand.

Data scientists decide what questions their team should be asking and then work out how to use data to respond to those questions. For forecasting and reasoning, they frequently create predictive models.

Daily duties for a data scientist could include the following:

• Analyze databases for patterns and trends to gain new insights.
• To predict outcomes, create algorithms and data models.
• Utilize machine learning methods to raise the caliber of data or product offerings.
• Inform senior staff and other teams of your recommendations.
• Use data analysis software like Python, R, SAS, or SQL.
• Keep up with advancements in the field of data science.

***According to Glassdoor, the average compensation for a data scientist in the United States is $122,499 asof April 2022.

 2. Data Analyst

To find the solution to a problem or provide an answer to a question, a data analyst gathers, purifies, and analyzes data sets. They work in a variety of fields, including as government, business, finance, law enforcement, and science.

The practice of extracting information from data to guide better business decisions is known as data analysis. Five iterative phases typically comprise the data analysis process:

Choose the data you want to examine:

• Collect the data
• Clean the data in preparation for analysis
• Analyze the data
• Interpret the results of the analysis

When it comes to what Data Analyst hat actually do, Here’s what many data analysts do on a day-to-day basis:

1. Gather data: Analysts frequently do their own data collection. This can entail completing surveys, monitoring website visitor demographics, or purchasing datasets from data collection experts.
2. Clean data: Raw data may include outliers, duplicates, or errors. In order to prevent inaccurate or distorted interpretations, cleaning the data refers to maintaining the quality of data in a spreadsheet or through a programming language.
3. Model data: This requires developing and planning a database's structural elements. You may decide which data kinds to save and gather, how to tie different data categories to one another, and how the data will actually look.
4. Interpret data: Finding patterns or trends in the data will enable you to interpret it and use it to support your interpretation of the question at hand.

***The average base pay for a data analyst in the United States in December 2022 is $62,382, according to job listing site Glassdoor

3. Data Engineer

Data engineering is the practice of developing large-scale data collection, storage, and analysis systems. It covers a wide range of topics and has uses in almost every business. Massive volumes of data can be gathered by organizations, but to make sure that it is in a highly useable shape by the time it reaches data scientists and analysts, they need the right personnel and the right technology.

The following are some of the most typical duties of a data engineer:

• Architecture development, construction, testing, and maintenance
• Align the architecture with the needs of the business
• data gathering
• Create data set procedures.
• Utilize tools and programming languages
• Determine how to increase data quality, efficiency, and reliability.
• Make inquiries about your industry and business through research
• Utilize vast data sets to solve business problems
• Utilize high-end analytics software, machine learning, and statistical techniques.
• gather information for predictive and prescriptive modelling
• Utilize data to find hidden patterns.
• Find tasks that can be automated using data.
• based on analytics, provide stakeholders with updates

***The average salary for a Data Engineer in the US is $120,880.

4. Machine Engineer

Machine learning engineers are in high demand today. However, the job profile has some difficulties. Machine learning engineers are expected to perform A/B testing, design data pipelines, and implement popular machine learning algorithms like classification, clustering, etc., apart from having a deep understanding of some of the most powerful technologies like SQL, and REST API. , etc.

A few important roles and responsibilities of a machine learning engineer include:

• Design and development of machine learning systems
• Exploring machine learning algorithms
• Testing machine learning systems
• Application/product development based on client requirements
• Extending existing machine learning frameworks and libraries
• Exploring and visualizing data for a better understanding
• Training and retraining systems
• Know the importance of statistics in machine learning

 

***According to Glassdoor the average salary for a Machine Learning Engineer is $107270 per year in US.

 5. Data Architect

A data architect creates data management plans so that databases can be easily integrated, centralized and protected with the best security measures. They also ensure that data engineers have the best tools and systems to work with.

A few important roles and responsibilities of a data architect include:

• Development and implementation of an overall data strategy aligned with the business/organization
• Identification of data collection sources in accordance with the data strategy
• Collaborate with cross-functional teams and stakeholders for smooth operation of database systems
• End-to-end data architecture planning and management
• Maintaining database systems/architecture with efficiency and security in mind
• Regularly audit the performance of the data management system and make changes to improve the systems accordingly.

 

***According to the Galssdoor The average salary for a Data Architect is $117729 per year in US

Data Scientist Roles and responsibilities.

 Data scientists work closely with business stakeholders to understand their goals and determine how data can be used to achieve those goals. The design data modelling processes create algorithms and predictive models to extract the data the business needs and help analyze the data and share insights with peers. While each project is different, While each project is different, the process for gathering and analyzing data generally follows the below path:

1. Ask the right questions to begin the discovery process.

2. Acquire data.

3. Process and clean the data.

4. Integrate and store data.

5. Initial data investigation and exploratory data analysis

6. Choose one or more potential models and algorithms.

7. Apply data science techniques, such as machine learning, statistical modelling, and artificial intelligence.

8. Measure and improve results.

9. Present final result to stakeholders

10. Make adjustments based on feedback.

11. Repeat the process to solve a new problem.

 

 

 

 

Data scientist: Sexiest job in 21st century

As we are living in the Big Data Era, Data Science is becoming a very promising field to harness and process huge volumes of data generated from various sources. Data Science is a vast discipline in itself, consisting of specialized skill sets such as statistics, mathematics, programming, computer science and so on. Data science consists of several elements, techniques and theories including math, statistics, predictive analysis, data modelling, data engineering, data mining, and visualization.

In this modern era, data scientists are the super powered heroes who lead the digital world.

Who is actually a Data scientist? What do they actually do? Are they struggling with data all day and night or experimenting in his/her laboratory with complex mathematics?  

Let’s explore!

There are several definitions available on Data Scientists. In simple words, A data scientist is a professional responsible for collecting, analyzing, and interpreting extremely large amounts of data.

They’re part mathematician, part computer scientist and part trend-spotter. And, because they straddle both the business and IT worlds, they’re highly sought-after and well-paid.

They’re also a sign of the times. Data scientists weren’t on many radars a decade ago, but their sudden popularity reflects how businesses now think about big data. That unwieldy mass of unstructured information can no longer be ignored and forgotten. It’s a virtual gold mine that helps boost revenue – as long as there’s someone who digs in and unearths business insights that no one thought to look for before.

To take an idea will see some definition of data scientist from different popular websites.

• Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientist’s role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations. (Masters in data science )

• Data Scientist practices the art of Data Science. (Edureka)

• A data scientist is a professional responsible for collecting, analyzing and interpreting extremely large amounts of data. The data scientist role is an offshoot of several traditional technical roles, including mathematician, scientist, statistician and computer professional. This job requires the use of advanced analytics technologies, including machine learning and predictive modelling.(TechTarget)

Inspiring Facts: Top 5 data scientist   (From AnalyticsInsights)

 

1. Geoffrey Hinton

Geoffrey Hilton is called the Godfather of Deep Learning in the field of data science. Mr Hinton is best known for his work on neural networks and artificial intelligence. A PhD in artificial intelligence, he is accredited for his exemplary work on neural nets.

Twitter- @geoffreyhinton

 

Awards– AM Turing (2019), BBVA Foundation Frontiers of Knowledge Award in Information and Communication Technologies (2016), IEEE Frank Rosenblatt Award (2014), IJCAI Award for Research Excellence (2005), Rumelhart Prize (2001).

 

2. Jeff Hammerbacher

 

The co-founder of the term, “Data Science”, Jeff Hammerbacher developed methods and techniques for capturing, storing, and analysing a large amount of data. Credited to start Facebook’s data science team, he threw his weight behind adopting Hadoop enabling the social media giant’s data team to process tons of data in real-time at a lightning-fast speed. Mr Hammerbacher is the co-founder at Cloudera and also been an instructor at the Icahn School of Medicine.

Twitter- @hackingdata

Book- Beautiful Data

 

3. Dhanurjay Patil

Dhananjay Patil is a former US Chief Data Scientist, and along with Jeff Hammerbacher he coined the term “data science”. A doctorate in Applied Mathematics from the University of Maryland College Park, the distinguished Dhanurjay Patil has been a principal consultant to many blue-chip companies which include LinkedIn, Skype, Salesforce, PayPal, eBay, and Greylock Partners.

Twitter- @dpatil

Awards– Medal for Distinguished Public Service

 

4. Alex “Sandy” Pentland

 

Alex “Sandy” Pentland is termed as one of the world’s seven most powerful data scientists along with Larry Page, by Tim O’Reilly in 2011. Mr Pentland also founded and leads an MIT-wide program that works actively in pioneering computational social science using Big Data and AI. A serial entrepreneur he co-leads the World Economic Forum Big Data and Personal Data initiatives and is a founding member of the Advisory Boards for Motorola Mobility, Telefonica, Nissan, and a variety of start-up firms.

Mr Pentland leads the Media Lab Entrepreneurship Program promoting companies using cutting edge technologies to solve real-world problems. Mr Pentland is also an advisor to the Enigma Project & Endor.

Twitter- @alex_pentland

Awards– McKinsey Award from Harvard Business Review, Brandeis Award, The 40th Anniversary of the Internet (from DARPA)

 

5. Dean Abbott

Founder and president of Abbott Analytics, Dean Abbott is a seasoned data science professional. With over 21 years of enriching experience, he is adept at deploying advanced and complex data mining techniques into data preparation and data visualization.

Mr Abbot is credited for his outstanding expertise in fraud detection mechanics, data and modelling, missile guidance, survey analysis, predictive toxicology, and signal processes.

Twitter- @deanabb

Books – IBM SPSS Modeler Cookbook and Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst.