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Career Path in Data Science for Data Scientists

Data scientists are considered as one of the top-notch job profiles of this era. Each and every domain of science, technology, arts, and economics are inclined more towards analytics which needs a massive amount of data collected for a long period of time. As per LinkedIn report, there is a vast requirement for professionals who have the capability to mine as well as interpret data. They are none other than BI Analyst, data analyst, data mining engineer and finally data scientists, etc. Hence, this domain has evolved as a new branch of science that involves the understanding of statistics, numerical methods, probability along with the knowledge of computer science (such as programming, Natural Language Processing, SQL, neural network, algorithms, Machine Learning, deep learning, etc.Choosing a career path in data science and acquiring different data scientist skills associated with it can transform your career. But sadly, great career paths followed by jobs do not merely fall out of the sky by simply mastering some subjects and programming languages like Python, R, SQL, statistics, etc. Finding the perfect Data scientist jobs takes time as well as effort along with proper knowledge.So, you have to take each and every subject or domain of data science in parallel to meet the requirement; so that you can efficiently place yourself or end up with a data science career. This article will help you figure out which career option will be the best for you. It discusses the new data science skills and job profiles that are opted by data science enthusiasts for their career. Also, we will get to know which path is right for you.To start a career in data science for ultimately landing yourself into the role of a data scientist, there is no specific learning path as such that you need to follow from your pre-school or high school. But any exclusive degree program or course will prepare you as a professional to start your career path in this data science area. But, here is a list of some courses and subjects that are beneficial and will give weight and accelerate your career goals towards data science.How to become a data scientist:The very first thing you must know is subjects like basics of mathematics and statistics, data computation and how they get stored, and a programming language which can support the concepts of data science efficiently.Also, students opt for UG and PG courses with specialization in Data Science and Machine Learning.There are various online websites that provide paid as well as free courses on data science and can give an in-depth knowledge on various specialized domains of data science such as data visualization using Matplotlib and Pandas library, data cleaning, combining datasets, analyzing data workflow, prediction models and algorithms, Data analysis using Pandas, NumPy, SciPy libraries, dealing with multi-table databases using SQL, acquiring data using Web Scraping, statistical data analysis, machine learning approaches, Calculus for ML, Linear algebra for ML, Linear Regression with ML, implementation of decision trees, Natural Language Processing, concepts of deep learning in data science, and use of other tools like Kaggle, Tableau, Apache Spark etc.There are scholars who opt for PG courses (both full-time as well as part-time) on data science to get an in-depth understanding of all the subdomains of this subject and to land themselves with a good profile in any reputed company.So, let’s dig deep into the journey of career tracking from initial data science role to a data scientist. First of all, we will make you familiar with the different job titles and profiles opted by data science aspirants, along with their descriptions, which will help you contrast with the different options that data science career can provide you with. You will also get to know some of the unusual in-depth understanding of different Data scientist career paths that you may not have thought about.1. Data analyst: This position is considered as an entry-level position in the data science field and its growth can lead you to a data scientist role with promotions and experience. But, you must have a clear idea that each and every data analyst do not come under the same work level and that their salary package range accordingly from junior level and interns to expert professional analysts.They are primarily engaged in analyzing a company or firm’s data and use such data to answer business questions and predict product growth. Based on such analysis, the company takes proactive actions. A sample scenario can be like this, a senior associate might ask a data analyst to look at the sales data received from the marketing campaign for assessing its efficiency and to recognize its strengths as well as weaknesses. Such work would engross accessing of company’s sales data, perhaps cleaning it, and then performing some statistic analysis on those data for predicting the business outcome, and finally visualizing as well as communicating those outcomes using data visualization programs.The various skills required in this role are –Implementing algorithms using Python or R library packages for transitional data science programmingData cleansingUnderstanding the concepts of probability and statisticsIntermediate SQL queriesVisualizing dataConverse clearly the different complex data analysis for making people understand who do not possess programming or statistics knowledge.2. Data Engineer: These engineers are hired for managing the infrastructure of a company’s data more technically. They are less involved in statistical analysis and invest their development effort more towards software development along with programming abilities. From this job role, one can become a data scientist with work experience.Companies hire data engineers specifically to develop tools and algorithms that can be used to build data pipelines for automating the most up-to-date marketing, sales, and/or profit or returns’ data so that the data analysts or data scientists can rapidly analyze or investigate data in a utilizable format. These data engineers are also responsible for developing and maintaining the application infrastructure required for storing and fast accessing of previously fed data.3. Business Intelligence Analyst: Companies are now hiring  Business Analysts who make use of different data for figuring out trends in marketing and trade by analyzing data for developing an apparent picture of which company products are in lead and which have less demand. Also, they focus on generating analytics for their competitor’s market position to compare and stay a step ahead in the competitive mark4. Data Mining Engineer: They are hired for examining not only the data for the company but also for other third-party clients whose project are also taken up. Other than analyzing different collections of data, the data mining engineer is also responsible for creating efficient algorithms for helping other data analysts make their task easier and well-organized.5. Data Architect: They are a special team of professionals who work closely with company’s clients, data mining engineers, developers and system designers for creating an outline sketch, which data management systems will utilize for integrating, concentrating maintaining, as well as protecting various data sources.6. Data Scientist: They are senior data science experts (who used to be any one of the above) who have gathered experience in this domain. They perform different types of work similar to that of data analysts and are also normally involved in building machine learning models for making accurate predictions about the company’s future business and marketing strategies. But on a good note, the data scientists who work in companies often have extra liberty to practice their individual ideas and thoughts as well as to conduct research for discovering appealing prototypes and trends in the data that is beneficial for the firm or has some new approach with high productivity.Data scientists are also involved in interpreting a business assignment into an analytics program, developing constructive hypotheses, or understanding any series of data, or patterns in raw data for measuring the impact these data will pose on the company’s businesses. They have the freedom to choose their own algorithm or create new approaches to solve a particular task. They use business analytics not only for forecasting data but also for providing the company with meaningful solutions so that their research (algorithms) can increase the revenue of the company.7. Senior Data Scientist: They are senior-most or next-level data scientists who have the capability to foresee what a business’s future needs and how the market is turning its needs. They analyze critical data for actionable intelligence. Their task is not only to gather data but also engages in analyzing those huge data sets for determining extremely complex production problems in an efficient manner. Data scientists usually mine as well as analyze data that has a diverse spectrum like–customer transactions, sensor data, clickstreams, social media data, different corporate log files and GPS plots. They have 10 to 15 years of expertise in their domain, which profits the company as they generate different means of using statistical data or create their own development tools, models and algorithms for efficient data handling and predictions.Other Specialized Job titles and career paths which can lead to data scientists or senior-level data science experts –1. Machine Learning Engineer: There are many organizations or firms who interchangeably use machine learning engineers with data scientists, where some other firms consider them as a data scientist who is specialized in the machine learning domain. Also, there are many companies where “machine learning engineers” are more a blend of programmers or developers who are actively involved in taking the analysis of a data scientist and incorporate those data into deployable software. But in general, all ML engineers have a moderate or good level of programming skills and a very advanced understanding of ML techniques and algorithms.2. Quantitative Analyst: They are also abbreviated as “quants”. They are hired for advanced statistical analyses or for calculating and answering to company’s future needs and make predictions associated with sales, finance or even risk. They also have a common understanding of the machine learning models which can calculate statistical data and crack the company’s economic problems and foresee markets needs and trends.3. Data Warehouse Architect: These professionals are a particular division of experts within the data engineering domain and they are charged to hold the firm’s data storage unit. This role highly needs SQL and understanding of database systems as a major skill, as well as comprehend the different data science domains, as you might need to work with data analysts or data architect.4. Business Intelligence Analyst:A business analyst is basically a data analyst with expertise in analyzing business and sales trends. This role sometimes also needs familiarity and awareness in working with software-based data analysis tools (like Microsoft Power BI) or data visualization tools (like Tableau). Again, these BI Analysts also need skills that data science analysts have because such skills are also necessary for these positions, along with Python or R programming skills.5. Statistician: They are nowadays called junior data scientists. They are highly skilled statisticians with a solid understanding of probability and statistics. They also need to know statistical programming languages (like R programming). As data scientists, they may not know how to build and train using machine learning algorithm and models, but they are much more familiar with the mathematical logic that is lying under the machine learning models.6. Marketing Analyst: Marketing analysts monitor and analyze the data related to sales & marketing for assessing as well as improving the efficiency of marketing operations. In the modern era of digital marketing, these analysts are hired to analyze a progressively large quantity of company’s data and handle software solutions such as Google Analytics which allows its users to analyze company’s marketing approaches without any programming skills. These professionals also need to have sound knowledge of statistics.7. Operations Analyst: Operations analysts are those who are involved in probing and reforming a business’s in-house processes. Not all operations analyst role requires the data skills, but in a lot of situations, being capable of cleaning, analyzing, as well as visualizing data makes it significant for determining the smooth working of the company.8. Data science internship: If you are in search of a data science role where you will experience on-the-job learning as an entry-level role, then joining as an intern can help you learn as well as get a hands-on experience on the applied part of learning. So, an internship is a great option if you are a fresher or a student and wants to give a kick-start in this domain. The internship in this field varies–some are paid internships while others are unpaid and typically run for a short time period–usually, 3 to 6 months and there is no guarantee of permanent employment in the end. Once you gain momentum in this domain by gathering experience, an intern can become a data analyst and then senior data analyst or data science engineer, and finally reach the level of a data scientist as well.Data Scientist - Job listing in different websitesSourceFreelance data analysts or data scientists:So far, you have seen various roles associated with data science that are full-time career options. If you are not looking for a full-time professional job option, then as an alternative you can opt for a freelance role also. It is not always that companies hire full-time data science analysts and data scientists. Companies that grow new interests in this domain hire data scientists and assign them a few freelance projects before committing to permanent job roles in their companies. Again, freelance data scientists are also essential in situations where companies with permanent or established data science team of experts need extra help for saving time or meeting the deadline. These are all potential clients who need a helping hand of freelance data scientists.Till now, you have encountered all the possible ways that can lead to a data scientist career in corporate grounds. There is another broader branch of data science that belongs exclusively from an academic point of view and can trail your career to data scientist. So, from the academic point of view, you can also choose your career option as a data science professor or lecturer in universities or institutes and train yourself to acquire the position of a data scientist.Doing research in this particular domain can also help academic scholars get a good salary package from their respective universities. These researchers can take their data science work to the next level and patent their research methodologies. These researchers also have the opportunity to switch roles from academic research and scientist role to corporate research and development profile. There is also an opportunity to join the different developers’ community of data science tools or technologies or otherwise library design programs.The role of data scientists does not end here. There are various other fields where these experts are spreading their influence and applications. Board members of different sports like cricket, football, hockey, etc. are also hiring data scientists or data analysts who can calculate the efficiency of players and analyze their gameplay and scores as well as predict other such relevant data. In the field of medical science, different data related to patients or research need adequate analysis before operating them to a functional level.Here is the list of workplaces or opportunities where different data science experts can set up their career path with a job based on their work taste and interests –i. Handling servers and data warehouses: Data Engineers, Database Developers, Data Analysts.ii. Data mining and statistical analytics department: Data Scientists, Business Analysts, data science statisticians.iii. Cloud computing and distributed computing: Data Architect (Cloud computing specialist), Cloud Data Engineers, Platform Engineers.iv. Database management and architecture: DBA, Data Specialist.v. Business Intelligence and Strategy-making department: BI Data Engineer, BI Analyst, Quantitative Analysts, Data Strategist, Data Scientists.vi. ML / Cognitive Computing Department: Machine Learning Engineer, AI Specialist, Cognitive Developer and Analysts, Data Science researchers, Data Scientists.vii. Data Visualization and Presentation unit: Data Analysts, Data Visualization Engineers, Operations analysts.viii. Data analytics (related to logistics, technology, sales, financials, human resources, etc.): Marketing analysts, Operations analysts, Data Scientists.ix. Sector-specific data science experts (Healthcare, Insurance, sports, etc.): Data Analyst, Business Analyst, Data Scientist, etc.Once you get a role as a data scientist in any organization or corporate, you will meet with a group of professionals who are from a diverse academic background and other professionals within your team whom you may need to guide or mentor, and lead the team to accomplish the target of the project. In the career path from a simple data science analyst to a data scientist, the working area you will opt for will most likely be interrelated to your academic background, knowledge or interests that you have in parallel fields of learning. Lastly, it can be said that data scientists possess an exceptional blend of technical skills, analytical skills, and business wisdom required for effectual analysis of massive datasets although thinking significantly for ultimately converting raw intelligence into crisp and achieving insight.
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Career Path in Data Science for Data Scientists

Susan May
Blog
13th Aug, 2019
Career Path in Data Science for Data Scientists

Data scientists are considered as one of the top-notch job profiles of this era. Each and every domain of science, technology, arts, and economics are inclined more towards analytics which needs a massive amount of data collected for a long period of time. As per LinkedIn report, there is a vast requirement for professionals who have the capability to mine as well as interpret data. They are none other than BI Analyst, data analyst, data mining engineer and finally data scientists, etc. Hence, this domain has evolved as a new branch of science that involves the understanding of statistics, numerical methods, probability along with the knowledge of computer science (such as programming, Natural Language Processing, SQL, neural network, algorithms, Machine Learning, deep learning, etc.

Choosing a career path in data science and acquiring different data scientist skills associated with it can transform your career. But sadly, great career paths followed by jobs do not merely fall out of the sky by simply mastering some subjects and programming languages like Python, R, SQL, statistics, etc. Finding the perfect Data scientist jobs takes time as well as effort along with proper knowledge.

So, you have to take each and every subject or domain of data science in parallel to meet the requirement; so that you can efficiently place yourself or end up with a data science career. This article will help you figure out which career option will be the best for you. It discusses the new data science skills and job profiles that are opted by data science enthusiasts for their career. Also, we will get to know which path is right for you.

To start a career in data science for ultimately landing yourself into the role of a data scientist, there is no specific learning path as such that you need to follow from your pre-school or high school. But any exclusive degree program or course will prepare you as a professional to start your career path in this data science area. But, here is a list of some courses and subjects that are beneficial and will give weight and accelerate your career goals towards data science.

How to become a data scientist:

  • The very first thing you must know is subjects like basics of mathematics and statistics, data computation and how they get stored, and a programming language which can support the concepts of data science efficiently.
  • Also, students opt for UG and PG courses with specialization in Data Science and Machine Learning.
  • There are various online websites that provide paid as well as free courses on data science and can give an in-depth knowledge on various specialized domains of data science such as data visualization using Matplotlib and Pandas library, data cleaning, combining datasets, analyzing data workflow, prediction models and algorithms, Data analysis using Pandas, NumPy, SciPy libraries, dealing with multi-table databases using SQL, acquiring data using Web Scraping, statistical data analysis, machine learning approaches, Calculus for ML, Linear algebra for ML, Linear Regression with ML, implementation of decision trees, Natural Language Processing, concepts of deep learning in data science, and use of other tools like Kaggle, Tableau, Apache Spark etc.
  • There are scholars who opt for PG courses (both full-time as well as part-time) on data science to get an in-depth understanding of all the subdomains of this subject and to land themselves with a good profile in any reputed company.

So, let’s dig deep into the journey of career tracking from initial data science role to a data scientist. 

First of all, we will make you familiar with the different job titles and profiles opted by data science aspirants, along with their descriptions, which will help you contrast with the different options that data science career can provide you with. You will also get to know some of the unusual in-depth understanding of different Data scientist career paths that you may not have thought about.

Different Career path for data scientist

1. Data analyst: 

This position is considered as an entry-level position in the data science field and its growth can lead you to a data scientist role with promotions and experience. But, you must have a clear idea that each and every data analyst do not come under the same work level and that their salary package range accordingly from junior level and interns to expert professional analysts.

They are primarily engaged in analyzing a company or firm’s data and use such data to answer business questions and predict product growth. Based on such analysis, the company takes proactive actions. A sample scenario can be like this, a senior associate might ask a data analyst to look at the sales data received from the marketing campaign for assessing its efficiency and to recognize its strengths as well as weaknesses. Such work would engross accessing of company’s sales data, perhaps cleaning it, and then performing some statistic analysis on those data for predicting the business outcome, and finally visualizing as well as communicating those outcomes using data visualization programs.

The various skills required in this role are –

  1. Implementing algorithms using Python or R library packages for transitional data science programming
  2. Data cleansing
  3. Understanding the concepts of probability and statistics
  4. Intermediate SQL queries
  5. Visualizing data
  6. Converse clearly the different complex data analysis for making people understand who do not possess programming or statistics knowledge.

2. Data Engineer: 

These engineers are hired for managing the infrastructure of a company’s data more technically. They are less involved in statistical analysis and invest their development effort more towards software development along with programming abilities. From this job role, one can become a data scientist with work experience.

Companies hire data engineers specifically to develop tools and algorithms that can be used to build data pipelines for automating the most up-to-date marketing, sales, and/or profit or returns’ data so that the data analysts or data scientists can rapidly analyze or investigate data in a utilizable format. These data engineers are also responsible for developing and maintaining the application infrastructure required for storing and fast accessing of previously fed data.

3. Business Intelligence Analyst: 

Companies are now hiring  Business Analysts who make use of different data for figuring out trends in marketing and trade by analyzing data for developing an apparent picture of which company products are in lead and which have less demand. Also, they focus on generating analytics for their competitor’s market position to compare and stay a step ahead in the competitive mark

4. Data Mining Engineer: 

They are hired for examining not only the data for the company but also for other third-party clients whose project are also taken up. Other than analyzing different collections of data, the data mining engineer is also responsible for creating efficient algorithms for helping other data analysts make their task easier and well-organized.

5. Data Architect: 

They are a special team of professionals who work closely with company’s clients, data mining engineers, developers and system designers for creating an outline sketch, which data management systems will utilize for integrating, concentrating maintaining, as well as protecting various data sources.

6. Data Scientist: 

They are senior data science experts (who used to be any one of the above) who have gathered experience in this domain. They perform different types of work similar to that of data analysts and are also normally involved in building machine learning models for making accurate predictions about the company’s future business and marketing strategies. But on a good note, the data scientists who work in companies often have extra liberty to practice their individual ideas and thoughts as well as to conduct research for discovering appealing prototypes and trends in the data that is beneficial for the firm or has some new approach with high productivity.

Data scientists are also involved in interpreting a business assignment into an analytics program, developing constructive hypotheses, or understanding any series of data, or patterns in raw data for measuring the impact these data will pose on the company’s businesses. They have the freedom to choose their own algorithm or create new approaches to solve a particular task. They use business analytics not only for forecasting data but also for providing the company with meaningful solutions so that their research (algorithms) can increase the revenue of the company.

7. Senior Data Scientist: 

They are senior-most or next-level data scientists who have the capability to foresee what a business’s future needs and how the market is turning its needs. They analyze critical data for actionable intelligence. Their task is not only to gather data but also engages in analyzing those huge data sets for determining extremely complex production problems in an efficient manner. Data scientists usually mine as well as analyze data that has a diverse spectrum like–customer transactions, sensor data, clickstreams, social media data, different corporate log files and GPS plots. They have 10 to 15 years of expertise in their domain, which profits the company as they generate different means of using statistical data or create their own development tools, models and algorithms for efficient data handling and predictions.

Other Specialized Job titles and career paths which can lead to data scientists or senior-level data science experts –

1. Machine Learning Engineer: 

There are many organizations or firms who interchangeably use machine learning engineers with data scientists, where some other firms consider them as a data scientist who is specialized in the machine learning domain. Also, there are many companies where “machine learning engineers” are more a blend of programmers or developers who are actively involved in taking the analysis of a data scientist and incorporate those data into deployable software. But in general, all ML engineers have a moderate or good level of programming skills and a very advanced understanding of ML techniques and algorithms.

2. Quantitative Analyst: 

They are also abbreviated as “quants”. They are hired for advanced statistical analyses or for calculating and answering to company’s future needs and make predictions associated with sales, finance or even risk. They also have a common understanding of the machine learning models which can calculate statistical data and crack the company’s economic problems and foresee markets needs and trends.

3. Data Warehouse Architect: 

These professionals are a particular division of experts within the data engineering domain and they are charged to hold the firm’s data storage unit. This role highly needs SQL and understanding of database systems as a major skill, as well as comprehend the different data science domains, as you might need to work with data analysts or data architect.

4. Business Intelligence Analyst:

A business analyst is basically a data analyst with expertise in analyzing business and sales trends. This role sometimes also needs familiarity and awareness in working with software-based data analysis tools (like Microsoft Power BI) or data visualization tools (like Tableau). Again, these BI Analysts also need skills that data science analysts have because such skills are also necessary for these positions, along with Python or R programming skills.

5. Statistician:

 They are nowadays called junior data scientists. They are highly skilled statisticians with a solid understanding of probability and statistics. They also need to know statistical programming languages (like R programming). As data scientists, they may not know how to build and train using machine learning algorithm and models, but they are much more familiar with the mathematical logic that is lying under the machine learning models.

6. Marketing Analyst: 

Marketing analysts monitor and analyze the data related to sales & marketing for assessing as well as improving the efficiency of marketing operations. In the modern era of digital marketing, these analysts are hired to analyze a progressively large quantity of company’s data and handle software solutions such as Google Analytics which allows its users to analyze company’s marketing approaches without any programming skills. These professionals also need to have sound knowledge of statistics.

7. Operations Analyst: 

Operations analysts are those who are involved in probing and reforming a business’s in-house processes. Not all operations analyst role requires the data skills, but in a lot of situations, being capable of cleaning, analyzing, as well as visualizing data makes it significant for determining the smooth working of the company.

8. Data science internship: 

If you are in search of a data science role where you will experience on-the-job learning as an entry-level role, then joining as an intern can help you learn as well as get a hands-on experience on the applied part of learning. So, an internship is a great option if you are a fresher or a student and wants to give a kick-start in this domain. The internship in this field varies–some are paid internships while others are unpaid and typically run for a short time period–usually, 3 to 6 months and there is no guarantee of permanent employment in the end. Once you gain momentum in this domain by gathering experience, an intern can become a data analyst and then senior data analyst or data science engineer, and finally reach the level of a data scientist as well.

Data Scientist - Job listing in different websites

Data Scientist - Job listing in different websites

Source

Freelance data analysts or data scientists:

So far, you have seen various roles associated with data science that are full-time career options. If you are not looking for a full-time professional job option, then as an alternative you can opt for a freelance role also. It is not always that companies hire full-time data science analysts and data scientists. Companies that grow new interests in this domain hire data scientists and assign them a few freelance projects before committing to permanent job roles in their companies. Again, freelance data scientists are also essential in situations where companies with permanent or established data science team of experts need extra help for saving time or meeting the deadline. These are all potential clients who need a helping hand of freelance data scientists.

Till now, you have encountered all the possible ways that can lead to a data scientist career in corporate grounds. There is another broader branch of data science that belongs exclusively from an academic point of view and can trail your career to data scientist. So, from the academic point of view, you can also choose your career option as a data science professor or lecturer in universities or institutes and train yourself to acquire the position of a data scientist.

Doing research in this particular domain can also help academic scholars get a good salary package from their respective universities. These researchers can take their data science work to the next level and patent their research methodologies. These researchers also have the opportunity to switch roles from academic research and scientist role to corporate research and development profile. There is also an opportunity to join the different developers’ community of data science tools or technologies or otherwise library design programs.

The role of data scientists does not end here. There are various other fields where these experts are spreading their influence and applications. Board members of different sports like cricket, football, hockey, etc. are also hiring data scientists or data analysts who can calculate the efficiency of players and analyze their gameplay and scores as well as predict other such relevant data. In the field of medical science, different data related to patients or research need adequate analysis before operating them to a functional level.

Here is the list of workplaces or opportunities where different data science experts can set up their career path with a job based on their work taste and interests –

i. Handling servers and data warehouses: Data Engineers, Database Developers, Data Analysts.

ii. Data mining and statistical analytics department: Data Scientists, Business Analysts, data science statisticians.

iii. Cloud computing and distributed computing: Data Architect (Cloud computing specialist), Cloud Data Engineers, Platform Engineers.

iv. Database management and architecture: DBA, Data Specialist.

v. Business Intelligence and Strategy-making department: BI Data Engineer, BI Analyst, Quantitative Analysts, Data Strategist, Data Scientists.

vi. ML / Cognitive Computing Department: Machine Learning Engineer, AI Specialist, Cognitive Developer and Analysts, Data Science researchers, Data Scientists.

vii. Data Visualization and Presentation unit: Data Analysts, Data Visualization Engineers, Operations analysts.

viii. Data analytics (related to logistics, technology, sales, financials, human resources, etc.): Marketing analysts, Operations analysts, Data Scientists.

ix. Sector-specific data science experts (Healthcare, Insurance, sports, etc.): Data Analyst, Business Analyst, Data Scientist, etc.

Once you get a role as a data scientist in any organization or corporate, you will meet with a group of professionals who are from a diverse academic background and other professionals within your team whom you may need to guide or mentor, and lead the team to accomplish the target of the project. In the career path from a simple data science analyst to a data scientist, the working area you will opt for will most likely be interrelated to your academic background, knowledge or interests that you have in parallel fields of learning. Lastly, it can be said that data scientists possess an exceptional blend of technical skills, analytical skills, and business wisdom required for effectual analysis of massive datasets although thinking significantly for ultimately converting raw intelligence into crisp and achieving insight.

Susan

Susan May

Writer, Developer, Explorer

Susan is a gamer, internet scholar and an entrepreneur, specialising in Big Data, Hadoop, Web Development and many other technologies. She is the author of several articles published on Zeolearn and KnowledgeHut blogs. She has gained a lot of experience by working as a freelancer and is now working as a trainer. As a developer, she has spoken at various international tech conferences around the globe about Big Data.


Website : https://www.zeolearn.com

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