Few jobs have been surrounded by as much hyperbole as has Data Scientist. The Harvard Business Review referred to as “The Sexiest Job of the 21st Century.”

How Data Science Ranks?

Regardless of where you stand on the matter of Data Science sexiness, it’s simply impossible to ignore the continuing importance of data, and our ability to analyze, organize, and contextualize it.

So, the role is here to stay, but unquestionably, the specifics of what a Data Scientist does will evolve. With technologies like Machine Learning becoming ever-more commonplace, and emerging fields like Deep Learning gaining significant traction amongst researchers and engineers—and the companies that hire them—Data Scientists continue to ride the crest of an incredible wave of innovation and technological progress.

How Data Science Ranks

Critical skills for a Data Scientist

Data scientists can be considered well paid, but they earn those healthy paychecks. Success as a data scientist is likely to require a mastery of both hard and soft skills. You may be required to execute a complex database query, but also interface comfortably with data users and producers throughout your organization. Here’s a rundown of the primary areas in which a would-be data scientist should aspire to excel:

Data-Driven Problem Solving

A data scientist is likely to know how to productively approach a problem. This means identifying a situation’s salient features, figuring out how to frame a question that will yield the desired answer, deciding what approximations make sense, and consulting the right co-workers at the appropriate junctures of the analytic process. All of that in addition to knowing which data science methods to apply to the problem at hand.


Data scientist use a variety of programming languages and software packages to flexibly and efficiently extract, clean, analyze, and visualize data. An aspiring data scientist will want to be familiar with at least these five:

  • Python is one of trending Programming Language used for Data Analytics. The Data Analytics and Data Processing libraries have been developed for Python, however, the likes of Bank of America and Facebook are using Python for data science. The high-level programming language is a powerful, fast, friendly, open and easy to learn.
  • R was once confined almost exclusively to academia, but social networking services, financial institutions, and media outlets now use this programming language and software environment for statistical analysis, data visualization, and predictive modeling.
  • SQL, or Structured Query Language, is a special-purpose programming language for managing data held in relational database management systems. Some of what you can do with SQL—data insertion, queries, updating and deleting, schema creation and modification, and data access control—you can also accomplish with R, Python, or even Excel, but writing your own SQL code could be more efficient and yield reproducible scripts.
  • Seattle-based software company Tableau offers a suite of products that complement data science standbys such as R and Python. Tableau may not be the best tool for cleaning or reshaping data, and its relational model doesn’t allow for procedural computations or offline algorithms, but it is great for data exploration and interactive analysis.
  • Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. Hadoop offers computing power, flexibility, fault-tolerance and scalability.


The software runs all the necessary statistical tests these days, but a data scientist may still have to possess the statistical sensibility to know which test to run when and how to interpret the results. A solid understanding of multivariable calculus and linear algebra, which form the basis of many data analysis techniques, is likely to allow a data scientist to build in-house implementations of analysis routines as needed.


Pictures often communicate more effectively than either numbers or words so it would behave as a data scientist to be able to present data in a visually compelling way. This requires you to not only master data visualization tools but also familiarize yourself with the principles of visualizing data effectively.


Data scientists must be able to report technical findings such that they are comprehensible to non-technical colleagues, whether corner-office executives or associates in the marketing department. Make your data-driven story not just comprehensible but compelling.

The New Definition Of “Job”

Today’s employment landscape is indeed changing, with new types of the job being created every day, and job tenure shortening measurably. The very definition of “job” is being redefined in real-time to incorporate technology, mobility, flexibility, and global connectivity. In light of this transformation, it’s critical that one has a clear understanding of the skills required to succeed.

Today’s economy is leaning more toward analytics—companies have been collecting data for many years. According to LinkedIn, there is a huge demand for people who can mine and interpret data.

Fortunately, as data has multiplied, so has the ability to collect, organize, and analyze it. Data storage is cheaper than ever, processing power is more massive than ever, and tools are more accessible than ever to mine the zettabytes of available data for business intelligence. In recent years, data analysis has done everything from predict stock prices to prevent house fires.

Research from the International Data Corporation suggests the digital universe—the amount of data produced and copied globally—more than doubles in size every two years. By 2020, it will have grown by a factor of 10 over where it stood in 2013.

Organizations that know how to collect and analyze large datasets can use this knowledge to identify and solve problems, improve business strategies, and minimize risk. This is the current state of the art in the field of analytics. Over time, however, experts predict that the amount of data available to organizations may actually become a potential barrier to progress, as the ability to collect it outpaces the means to sort and process it. Along with organizations continued to drive to extract ever more useful information from new and old datasets, such projections have put data expertise in high demand.

To solve this problem, some operations are done on Data to optimize-

Trending career options in Data Analytics available are Data Analysts, Data Architects, Data Scientists and Data Engineers.

Data Analyst

Data analysts collect, process and perform statistical analyses of data. Their skills may not be as advanced as data scientists (e.g. they may not be able to create new algorithms), but their goals are the same – to discover how data can be used to answer questions and solve problems.

Depending on their level of expertise, data analysts may:

  • Work with IT teams, management and/or data scientists to determine the organizational goal
  • Mine data from primary and secondary sources
  • Clean and prune data to discard irrelevant information
  • Analyze and interpret results using standard statistical tools and techniques
  • Pinpoint trends, correlations and patterns in complicated data sets
  • Identify new opportunities for process improvement
  • Provide concise data reports and clear data visualizations for management
  • Design, create and maintain relational databases and data systems
  • Triage code problems and data-related issues

Data analysts are sometimes called “junior data scientists” or “data scientists in training.” Instead of being free to create their own big data projects, they may be limited to tackling specific business tasks using existing tools, systems and data sets.

However, there are plenty of companies who don’t make a clear distinction between the two roles. In some cases, a data analyst/scientist could be writing queries or addressing standard requests in the morning and building custom solutions or experimenting with relational databases, Hadoop and NoSQL in the afternoon.

What Kind of Skills Will I Need?

Technical Skills

  • Statistical methods and packages (e.g. SPSS)
  • R and/or SAS languages
  • Data warehousing and business intelligence platforms
  • SQL databases and database querying languages
  • Programming (e.g. XML, JavaScript or ETL frameworks)
  • Database design
  • Data mining
  • Data cleaning and munging
  • Data visualization and reporting techniques
  • Working knowledge of Hadoop & MapReduce
  • Machine learning techniques

Business Skills

  • Analytic Problem-Solving: Employing best practices to analyze large amounts of data while maintaining intense attention to detail.
  • Effective Communication: Using reports and presentations to explain complex technical ideas and methods to an audience of laymen.
  • Creative Thinking: Questioning established business practices and brainstorming new approaches to data analysis.
  • Industry Knowledge: Understanding what drives your chosen industry and how data can contribute to the success of a company/organization strategy.

Data Architect

Data Architects create blueprints for data management systems. After assessing a company’s potential data sources (internal and external), architects design a plan to integrate, centralize, protect and maintain them. This allows employees to access critical information in the right place, at the right time.

A data Architect may be required to:

  • Collaborate with IT teams and management to devise a data strategy that addresses industry requirements
  • Build an inventory of data needed to implement the architecture
  • Research new opportunities for data acquisition
  • Identify and evaluate current data management technologies
  • Create a fluid, end-to-end vision for how data will flow through an organization
  • Develop data models for database structures
  • Design, document, construct and deploy database architectures and applications (e.g. large relational databases)
  • Integrate technical functionality (e.g. scalability, security, performance, data recovery, reliability, etc.)
  • Implement measures to ensure data accuracy and accessibility
  • Constantly monitor, refine and report on the performance of data management systems
  • Meld new systems with existing warehouse structures
  • Produce and enforce database development standards
  • Maintain a corporate repository of all data architecture artifacts and procedures

Some companies need data architects who are ninjas in data modeling techniques; others may want experts in data warehousing, ETL tools, SQL databases or data administration. Data architects are likely to be senior-level employees with plenty of years in business intelligence under their belts.

What Kind of Skills Will I Need?

Technical Skills

  • Application server software (e.g. Oracle)
  • Database management system software (e.g. Microsoft SQL Server)
  • User interface and query software (e.g. IBM DB2)
  • Enterprise application integration software (e.g. XML)
  • Development environment software
  • Backup/archival software
  • Agile methodologies and ERP implementation
  • Predictive modeling, NLP and text analysis
  • Data modeling tools
  • Data mining
  • UML
  • ETL tools
  • Python, C/C++ Java, Perl
  • UNIX, Linux, Solaris and MS Windows
  • Hadoop and NoSQL databases
  • Machine learning
  • Data visualization

Business Skills

  • Analytical Problem-Solving: Approaching high-level data challenges with a clear eye on what is important; employing the right approach/methods to make the maximum use of time and human resources.
  • Effective Communication: Carefully listening to management, data analysts and relevant staff to come up with the best data design; explaining complex concepts to non-technical colleagues.
  • Expert Management: Effectively directing and advising a team of data modelers, data engineers, database administrators and junior architects.
  • Industry Knowledge: Understanding the way your chosen industry functions and how data are collected, analyzed and utilized; maintaining flexibility in the face of big data developments.

Data Scientist

Some companies treat the titles of “Data Scientist” and “Data Analyst” as synonymous. But there’s really a distinction between the two in terms of skill set and experience. Though data scientists and data analysts have the same mission in an organization—to glean insights from the massive pool of data available—a data scientist’s work requires more sophisticated skills to tackle a higher volume and velocity of data.

As such, a data scientist is someone who can do undirected research and tackle open-ended problems and questions. Data scientists typically have advanced degrees in a quantitative field, like computer science, physics, statistics, or applied mathematics, and they have the knowledge to invent new algorithms to solve data problems.

“A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician” – Josh Wills

On any given day, a data scientist’s responsibilities may include:

  • Conduct undirected research and frame open-ended industry questions
  • Extract huge volumes of data from multiple internal and external sources
  • Employ sophisticated analytics programs, machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling
  • Thoroughly clean and prune data to discard irrelevant information
  • Explore and examine data from a variety of angles to determine hidden weaknesses, trends and/or opportunities
  • Devise data-driven solutions to the most pressing challenges
  • Invent new algorithms to solve problems and build new tools to automate work
  • Communicate predictions and findings to management and IT departments through effective data visualizations and reports
  • Recommend cost-effective changes to existing procedures and strategies

Every company will have a different take on job tasks. Some treat their data scientists as data analysts or combine their duties with data engineers others need top-level analytics experts skilled in intense machine learning and data visualizations.

As data scientists achieve new levels of experience or change jobs, their responsibilities invariably change. For example, a person working alone in a mid-size company may spend a good portion of the day in data cleaning and munging. A high-level employee in a business that offers data-based services may be asked to structure big data projects or create new products.

What Kind of Skills Will I Need?

Technical Skills

  • Math (e.g. linear algebra, calculus and probability)
  • Statistics (e.g. hypothesis testing and summary statistics)
  • Machine learning tools and techniques (e.g. k-nearest neighbors, random forests, ensemble methods, etc.)
  • Software engineering skills (e.g. distributed computing, algorithms and data structures)
  • Data mining
  • Data cleaning and munging
  • Data visualization (e.g. ggplot and d3.js) and reporting techniques
  • Unstructured data techniques
  • R and/or SAS languages
  • SQL databases and database querying languages
  • Python (most common), C/C++ Java, Perl
  • Big data platforms like Hadoop, Hive & Pig
  • Cloud tools like Amazon S3

Business Skills

  • Analytic Problem-Solving: Approaching high-level challenges with a clear eye on what is important; employing the right approach/methods to make the maximum use of time and human resources.
  • Effective Communication: Detailing your techniques and discoveries to technical and non-technical audiences in a language they can understand.
  • Intellectual Curiosity: Exploring new territories and finding creative and unusual ways to solve problems.
  • Industry Knowledge: Understanding the way your chosen industry functions and how data are collected, analyzed and utilized.

Data Engineer

Data engineers build massive reservoirs for big data. They develop, construct, test and maintain architectures such as databases and large-scale data processing systems. Once continuous pipelines are installed to – and from – these huge “pools” of filtered information, data scientists can pull relevant datasets for their analyses.

In his/her role as a hardcore builder, a data engineer may be required to:

  • Design, construct, install, test and maintain highly scalable data management systems
  • Ensure systems meet business requirements and industry practices
  • Build high-performance algorithms, prototypes, predictive models and proof of concepts
  • Research opportunities for data acquisition and new uses for existing data
  • Develop dataset processes for data modeling, mining and production
  • Integrate new data management technologies and software engineering tools into existing structures
  • Create custom software components (e.g. specialized UDFs) and analytics applications
  • Employ a variety of languages and tools (e.g. scripting languages) to marry systems together
  • Install and update disaster recovery procedures
  • Recommend ways to improve data reliability, efficiency and quality
  • Collaborate with data architects, modelers and IT team members on project goals

Data engineers may work closely with data architects (to determine what data management systems are appropriate) and data scientists (to determine which data are needed for analysis). They often wrestle with problems associated with database integration and messy, unstructured data sets. Their ultimate aim is to provide clean, usable data to whoever may require it.

What Kind of Skills Will I Need?

Technical Skills

  • Statistical analysis and modeling
  • Database architectures
  • Hadoop-based technologies (e.g. MapReduce, Hive and Pig)
  • SQL-based technologies (e.g. PostgreSQL and MySQL)
  • NoSQL technologies (e.g. Cassandra and MongoDB)
  • Data modeling tools
  • Python, C/C++ Java, Perl
  • Mat Lab, SAS, R
  • Data warehousing solutions
  • Predictive modeling, NLP and text analysis
  • Machine learning
  • Data mining
  • UNIX, Linux, Solaris and MS Windows

Business Skills

  • Creative Problem-Solving: Approaching data organization challenges with a clear eye on what is important; employing the right approach/methods to make the maximum use of time and human resources.
  • Effective Collaboration: Carefully listening to management, data scientists and data architects to establish their needs.
  • Intellectual Curiosity: Exploring new territories and finding creative and unusual ways to solve data management problems.
  • Industry Knowledge: Understanding the way your chosen industry functions and how data can be collected, analyzed and utilized; maintaining flexibility in the face of big data developments.

The job of a Data Analyst can be defined as exploring ways in which data can be used to answer business questions and solve problems an organization is facing.

As companies are expanding and multiplying, the need for data analysts has never been higher. If you’re someone who loves numbers, problem-solving and communicating your knowledge with others, then a career as a data analyst could be a perfect choice. By obtaining a university degree, learning important analytical skills, and gaining valuable work experience, you’ll be on your way to becoming a successful data analyst.

They’re required to collect, process and analyze data for a variety of business concerns ranging from product pricing to employee productivity – anything requiring data to make better business decisions. It’s no surprise in the next five years 59% of organizations plan to increase the number of positions requiring data analysis skills.

Accelerating Your Education

Accelerating your Education

1. Earn a Bachelor’s Degree

Earn a Bachelors Degree

In addition to increasing your earning potential and opportunities for advancement, earning your bachelor’s degree in data analytics also puts you right at the center of turning raw data into usable information. Most entry-level data analyst jobs require at least a bachelor’s degree. To become a data analyst, you’ll want to earn a degree in a subject such as mathematics, statistics, economics, marketing, finance, or computer science.

2. Sign-up for the courses that target specific subject


Data Science Goal at TechLearn

If you want to learn Data Analysis course from scratch, you can sign-up on particular websites that provide a complete guide to Data Analysis course along with Certification. One of them is TechLearn.live that offers career Goals along with unlimited live brand stories and real-data projects

There also might be workshops that you can attend in your area.

Skill Requirements for Data Analyst

Skills required for Data Scientists

If you intend to become a successful data analyst, you must start by ensuring you get a good background in mathematics, technology, business intelligence, data mining, and statistics. You also need to possess a couple of analytics skills which include:

1. Master Algebra

Master Algebra for Machine Learning algorithms

Numbers are what a data analyst works with every day, so you want to make sure you’re comfortable with math. Having a firm understanding of algebra is important; you should know how to do things such as interpret and graph different functions as well as work through real-life word problems. Take the online Data Analytics course from EduGrad that covers all the aspects required for Data Analyst.

Knowing multivariable calculus and linear algebra will help as well.

2. Understand Statistics

To become a full-fledged data analyst, a thorough grounding in statistics is essential, being good at statistics will help you understand algorithms deeply and understand when they should be used. Brush up on applied statistics, linear algebra, real analysis, graph theory and numerical analysis. Linear algebra comes into play with regression, understanding data structures and prepares data for prescriptive and predictive data modeling.

3. Practice your Programming Skills regularly

Programming skills of a Data Analyst

While you don’t need to be an expert at coding or programming to start off as a data analyst, you should be comfortable doing it on a small level. Start by learning how to use programs such as Python, R, and Java first, and then work your way up to others.

  • SQL programming is another that is common among data analysts.
  • You can take courses online to learn coding and programming.

4. Learn Basic concepts in Excel

Basic concepts of excel

Data analysts collect, process and perform statistical analyses of data. You’ll be organizing data and calculating numbers as a data analyst, so you need to be comfortable using Excel.

You cannot separate Excel form data analysis as it plays a very important role in the process. Make sure you know your way around the numerous functions available on Excel to be successful in data analysis profession.

5. Learn about Machine Learning

Machine Learning Algorithms

Machine Learning is turning out to be an essential skill that data professionals need to have. In ML, regression, classification, and segmentation are the broad learning areas where analysts should focus.

The importance of MI is explained by the fact that it allows devices to act independently and to collect accurate and efficient best user experience. This technology is used for real-time product targeting, visual search, sizing & styling, conversational commerce, location-based marketing & analytics, integrated online & in-store analytics, and predictive merchandising. Machine intelligence is going to be more prominently used in the healthcare, financial, and e-commerce sectors.

6. Grow-up your communication skills

Data Analytics job requires you to be an excellent communicator. In other words, you must be able to facilitate meetings, make the right requests and be an active listener in order to assimilate new information. Your communication proficiency should also cut across different digital platforms such as the internet, conference calls, SMS among others. The nature of this job requires you to spend a significant amount of time relating to management, users, developers, and clients. Also, understand how to use tools such as ggplot and matplotlib to illustrate your findings.

Gain Work Experience

Experience required to become Data Scientist

1. Look for the Industries that need Data Analysts

Data is expanding day by day for any Organization. Companies are demanding Data Analysts in order to handle this excessive data. Focus your job search on industries that tend to need data analysts more than others. Marketing firms, tech companies, and financial institutions all tend to hire data analysts to help them interpret data and explain it in understandable terms. Do some research on the organization you have selected.

2. Apply for Internship as Data Analyst

An internship is a very first step to get into any organization. Data Analysts are sometimes called “junior Data Scientists” or “Data Scientists in training.” Instead of being free to create their own big data projects, they may be limited to tackling specific business tasks using existing tools, systems, and data sets.

However, there are plenty of companies who don’t make a clear distinction between the two roles. In some cases, a Data Analyst could be writing queries or addressing standard requests and building custom solutions or experimenting with relational databases, Hadoop and NoSQL. Depending on the industry, initially, you’ll need to be familiar with Python, R, or SQL programming — knowing all three is even better.

3. Aim for entry – level Jobs

As a fresher having not enough experience, one should go for entry-level jobs rather than at a higher position. Entry-level jobs will allow you to gain valuable knowledge and experience that you’ll need for higher level data analyst jobs. Entry-level jobs still pay very well, and companies are always looking for people to fill positions such as Statistical Data Analyst or Business Analyst.

Entry level jobs will most likely require a bachelor’s degree, but not a master’s or doctoral degree.

Interviewing for the Job

Data Science Job interview tips

Prepare a formal Curriculum Vitae highlighting your Qualifications and Project work. Your Resume is going to be your first impression to the recruiter of any organization. But before that, as told above, do some research about the organization product and background to build inner confidence.

Attend the Interview confidently and get hired in a good company.

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