Terms like ‘Data Science’, ‘Machine Learning’, and ‘Data Analytics’ are so infused and embedded in almost every dimension of lifestyle that imagining a day without these smart technologies is next to impossible. With science and technology propelling the world, the digital medium is flooded with data, opening gates to newer job roles that never existed before.
However, quite often it is witnessed that beginners get confused over similar terms being used interchangeably, like ‘Data Science’ and ‘Data Analytics’. This post will give you a clear idea about what some of the prominent concepts and job roles in Data are, and how they differ from each other!
We will discuss following topics in this blog post:
- What is Data Science?
- Data Scientist Major Responsibilities
- Skills Required to Become a Data Scientist
- Qualification Required to Become a Data Scientist
- What is Machine Learning?
- Machine Learning Expert Major Responsibilities
- Skills Required to Become a Machine Learning Expert
- Qualification Required to Become a Machine Learning Expert
- When to Use Machine Learning Techniques and Deep Learning Techniques?
- What is Data Analytics?
- Data Analyst Major Responsibilities
- Skills Required to Become a Data Analyst
- Qualifications Required to Become a Data Analyst
- Data Analytics Vs Business Analytics
- Data Science vs Data Analytics — Which One Should I Choose?
- How to choose between Data Science and Machine learning?
- Do you need a CS degree to get into Machine Learning?
What is Data Science?
The most popular field that has emerged in the wake of digital disruption is ‘Data Science’. 12 years back no one would have heard of this term, but it is a buzzword for modern technology landscape. Data being oil and fuel of all the operations, companies are making the most of the accessible data that had never been used before. It is giving rise to the technologies that can analyze that unprecedented, voluminous, and variable data, to make critical business decisions in a better way.
As per The Emerging Jobs Report U.S. 2020 by LinkedIn, Data Scientist is one of the most trending jobs in U.S. that has been recorded with 37% Annual Growth.
So, if we take the above description literally, than Data Science can be defined as, “A field of technology that deals with exploring, modeling, and analyzing the big data to get meaningful insights from them that can solve a crucial business problem”.
As rightly said by Weslley Moura (Data Scientist), “In a broad perspective, data science is related to the usage of data to solve real problems. Somehow, data science projects aim to extract knowledge from data and let people or systems take advantage of this knowledge.”
For an instance, US Presidential Election 2020, has been one of the most talked about subject, only second to Coronavirus, of this year. Data Science has always been trying to make predictions about who will win the election, like in the year 2016, Data Science made predictions about the winner between the two candidates.
Similarly, the Winner of US Presidential Election 2020 can be predicted using regression model on the basis of data collected from various channels, such as social media.
This is just a tiny example among many other large-scale enterprise-wide applications that are not only helping create intelligent technologies, but also attracting aspirants from various platforms to build a career in Data Science.
Let’s take a look at the skills that are required to become a Data Scientist in the following section!
Data Scientist Major Responsibilities
The primary focus of Data Science is to acquire data, verify the data, and derive insights from it. Some of the major responsibilities that a Data Scientist is required to perform are:
- Predictive Modeling: Using Data and Statistics to predict the outcomes with the help of data models. These models are used for predicting various activities, events, phenomenon, etc. It is also called Predictive Analytics.
- Machine Learning and Deep Learning: Machine Learning is a subset of Artificial Intelligence that seeks to educate the machines without human intervention through structured data. Deep Learning is a further subset of machine learning which primarily deals with artificial neural network which is nothing but multiple layers of algorithms.
The picture at you right, is a snapshot of Major Responsibilities that Amazon looks for in a candidate for Data Scientist position.
Skills Required to Become a Data Scientist
Data Scientists are the professionals who have to acquire a diverse set of knowledge and are required to have a command of both business and technology. One does not become a Data Scientist overnight. In order to become a Data Scientist, you need to go through a series of stages acquiring various skills along the way. Some of those skills are completely different from that of a Data Scientist, and some are common.
If you look at the above Venn diagram, you will able to see, the Data Science is the field which has shared attributes from Business Knowledge, Computer Science, Mathematics & Statistics. Moreover, Data Analytics is a domain that is just adjacent to Data Analytics, which is sharing equal proportion of Domain Knowledge and Computer Science.
It implies that Data Science and Data Analytics are quite similar in broader perspective. This can easily confuse a beginner. Hence, it is of utmost importance to demarcate the unique features of Data Science from the lot of common ones.
Let’s know them in broader aspect:
- Programming: A Data Scientist is expected to have good knowledge of either Python or R along with good hands-on experience on the libraries and packages.
- Data Visualization: Data Scientists are required to create dashboards and draw reports draw conclusions from them. So they need to master some of the popular Data visualization tools like Tableau, Power BI, JMP, etc. If you have mastered R, then plotting graphs using various Data Visualization Packages like ggplot2 will serve the purpose.
- Database: Having proficiency in database technologies is very much important for a Data Scientist to succeed in the career. Technologies like Teradata, SQL, Oracle, or MySQL or other RDBMS are highly in-demand in this domain.
- Statistics and Mathematics: For a Data Scientist it is utmost important to have statistical knowledge. Data Scientists are quite often asked to employ statistical techniques to make major business decisions. They are also asked to collect data from various sources and apply statistical techniques to model, analyze and interpret data. Mathematics is also an important skill when it comes to understanding the computations. Any Data Scientist must be good with numbers.
- Presentation and Communication Skills: A Data Scientist must have the ability to present the information in away that can explain the most complexinsight in the easiest way possible. They should be able to communicate the information in a tailored manner that can solve the queries of various teams and the business as a whole.
- Business Thinking: A Data Scientist must be able to able to participate in strategic planning. The candidate should be able to maintain relationship with various key partners on a global and Regional Level, for Customer Service, Business Intelligence, New Product Development, etc.
- Problem Solving Ability: A data Scientist’s one of the biggest ability is to be able to find a solution to a business problem. In this matter the candidate’s ability to think out of the box, creativity, and flexibility goes a long way.
- Data-driven Decision Making: A Data Scientist needs to find a solution based on verified data and insights. These solutions should be accompanied with the ways to find the solution, tools and techniques used in the process, ways to communicate the outcomes in the most effective manner. In common words, this trait of a Data Scientist is called Critical Thinking.
Qualification Required to Become a Data Scientist
There can be ways to acquire the qualifications that are necessary to become a Data Scientist. They are:
- The picture at the right hand side denotes the Basic Qualifications that are required to get hired in Amazon, as a Data Scientist on a Mid-Senior Level.
- Machine Learning Experiments: A Machine Learning Expert has to undertake various experiments and tests and run them. Fine tune the test results and implement them.
- Train and Retain the System: One of the primary responsibilities of a Machine Learning Exert is to develop models that are capable of learning continually from a stream a data. It is based on humans’ ability to acquire knowledge, fine-tune the learning and transfer knowledge.
- Perform Statistical Analysis: Performing statistical operations is another important responsibility of a Machine Learning Expert. A Machine Learning Expert is required to select the appropriate datasets and data representation methods to run statistical analysis and fine-tune the test results.
- Extend ML Frameworks: A Machine Learning Expert has to work towards extending the existing ML libraries and frameworks to keep up with the changing industry requirements.
- Computer Science: An aspiring Machine Learning Expert must have a sound understanding of the fundamentals of Computer Science, concepts, architecture, data structure, etc. The candidate should have good understanding of stack, queue, binary trees, algorithms, etc.
- Machine Learning Algorithms: Being a Machine Learning Expert you have to use the Machine Learning Algorithms to solve various problems. For that you need to know how to use the libraries, packages, and the APIs that can be implemented on various Machine Learning Platforms like TensorFlow, Apache Spark MLib, etc.
- Programming: As part of programming, it is said that Machine learning programs can be written in any programming language and the machine learning libraries are also available in different programming languages.
- Software Designing: As a Machine Learning Expert, a candidate is expected to design systems that can be integrated with other software components. Hence a hands-on experience in Web APIs, Static and Dynamic Libraries, etc., are highly important. For this, the aspirant must be good at requirement analysis, use-case and test case development, documentation and testing.
- Statistics and Mathematics: Machine learning algorithms are often built on Statistical models and hence, a sound knowledge of the various statistical methodologies like, Correlation, Regression, Time–Series Analysis, etc., will help the candidate create a solution model in an efficient way.
- Communication and Presentation Skills: Any Machine Learning Expert has to be good in communication and presentation. They are expected to write and convey the most complex technical insights in the most understandable manner. They should have presentation skills also which will help them address broader audiences.
- Teamwork: A Machine Learning expert works in a team that requires coordination and cooperation.Maintaining a team spirit and being in constant contact with product designers, managers, testers, and software developers help run the project smoothly.
- Time Management: At times for Machine Learning Expert deadlines become real tight, and meeting the schedules may cause the candidate to miss out on minute but critical details. In order to avoid such mistakes, it is highly important that the candidate is able to handle the work pressure and is able to manage the time.
- Leadership: After a certain point of time, it is highly advisable that the Machine Learning Expert develops leadership traits and grows skills like problem solving, brainstorming, guiding, etc.Hence, it is highly important for the candidate to have that knack.
- Master’s Degree in Computer Science or Related Field: A Machine Learning Expert needs to acquire a Masters Degree in Computer Science or related field, to stay ahead in the competition. The Master’s Degree in Computer Science will help the candidate to acquire the understanding of high-level programming, which is highly important for Machine Learning professionals.
- Experience in Working in Software Development: The most important aspect is to gain exhaustive experience in Software Development. This further lays path for career in Machine Learning.
Learn the Top 7 Data Science Skills necessary to become a Data Scientist!
What is Machine Learning?
Machine Learning is probably one of the most misunderstood subjects across the platform of technology. Most of the times, Machine Learning is misunderstood with Artificial Intelligence. However, with growing popularity of Machine Learning and its uses, more and more technologies are deploying this concept and thriving in the market.
As per FinancesOnline, the total funding gained by machine learning applications across the globe only in the first quarter of 2019 alone is $28.5 Billion.
More and more companies are leveraging Machine Learning Applications and Platforms to get ahead of the cut-throat competition that is increasing at an exponential speed.
“Machine learning is a field that deals with educating the machines to make them intelligent.”
Nothing can be a better go to example of Machine Learning Application, like Google Search. Any time you type something on Google; it processes your natural language, interprets the intent of your search, matches the words with its lexicon and fetches the pages that are indexed with that word.
And this is no revelation that Google is thriving in the search market. With voice assistance gadgets coupled with search features are redefining the concept of smart devices. The above example can clearly show how Google interprets the mistakes in spelling and fetches the correct result. It is Machine Learning and Deep Learning Algorithms that make every single search possible.
This is how Google implements Machine Learning Algorithms! Read Now!
Quite often Beginners misunderstand Machine Learning with Artificial Intelligence, while both are very different in nature. Artificial Intelligent is more of an umbrella term that automates any operation with the help of human-like cognition. On the Other hand, Machine Learning is about educating machine to make them smarter so that they can make decisions like humans.
Machine Learning Expert Major Responsibilities
Machine Learning Experts are tech nerds who have in-depth knowledge and hands-on experience on real-life projects. Machine Learning is a subset of Artificial Intelligence which further magnifies into Deep Learning.
Following points will give you a clear idea about the major responsibilities to be fulfilled by a Machine Learning Exert!
The picture at the right is the Job Description for a Machine Learning Expert from PayPal.
Skills Required to Become a Machine Learning Expert
Many a times, it has been observed that Artificial Intelligence, Machine Learning, and Deep Learning, and Data Science, confuse the candidates. Therefore, as a consequence of unclear borderline, aspirants are not able to know what kind of skills they need to acquire to become a Machine Learning Expert.
Following Venn diagram will clear this fact that Machine Learning is quite different from Data Science. However, there are some shared attributes between these two domains.
Let’s understand it in a better way with the unique and common skills to be fulfilled by Machine Learning Expert!
Some of the technical and non-technical skills that any Machine Learning Expert is expected to show are discussed below!
C and C++ are mostly used for low-level programming like operating systems, but if suitable packages are made available, Machine Learning programs can be written. On the other hand, R and Python are the first choice for any machine learning geek as there are numerous packages are available for them.
These Top 25 Machine Learning Interview Questions are sure to land you on high-paying job!
Qualification Required to Become a Machine Learning Expert
In order to become a successful Machine Learning Exert, a candidate has to earn following qualifications:
Following image is the glimpse of Required Qualifications for a Machine Learning Expert in PayPal. These will give you a clear idea about what are the pre-requisites to apply for this role.
When to Use Machine Learning Techniques and Deep Learning Techniques?
Deep Learning is actually a subset of Machine Learning that seeks to make the machine learn from the past outputs and determine the most effective output in the similar way.
Following are the times when you should consider choosing Machine Learning or Dee Learning!
- 1. Machine Learning is used when the project requires predicting or finding the trend. On the other hand, Deep Learning is used when the project requires performing distinctive operations like identifying objects in the images, enhancing signals and images, etc.
2. Machine Learning should be used when you need quick results. On the other hand, Deep Learning Model should be used when you have plenty of time.
3. Machine Learning should be chosen when the dataset is short, limited and structured in nature. On the other hand, Deep Learning should be chosen when the dataset is quite bulky.
4. Machine Learning should be chosen when you just have Desktop Computer. Deep Learning requires better computational power.
What is Data Analytics?
As we head towards the middle of 21st century, the digital disruption is taking the world by storm developing high-end technologies that have revolutionized the market in a major way. With digital medium exploding with data all around, companies have realized that that we sitting on a gold mine of data. The only way to survive this head-to-head competition is to develop a unique factor that can give a competitive edge over others.
“Data Analytics is a field of technology that deals with analyzing raw data to find meaningful information.”
For an instance, when you hear someone say that the sale of a particular product or service, in the Q1 is X% higher than that of the Q2, that is actually an analysis made on the sales data from the months of Q1 and Q2.
Data Analyst Major Responsibilities
A recent market study by Market Reports World shows that the Data Analytics Market is expected to grow at a CAGR of 30.08% from 2020 to 2023, which would equate to $77.6 billion.
Data Analytics allows companies to dig the data so that meaningful patterns can be drawn and insights can be extracted to use them in favor of business. However, like Machine Learning, Data Analytics is also an area which is highly misunderstood and not clearly depicted, which can be really confusing to anyone who wants to become a Data Analyst.
Let’s find out what are the major responsibilities that a Data Analyst has to perform!
- Acquiring data from primary and secondary sources and organizing them in a particular format.
- Maintaining the data systems and databases to fix the errors.
- Applying statistical tools to draw insights, trends, and patterns.
- Preparing reports and communicating them in an effective manner.
- Documenting the finding and inferences that can help the stakeholders understand the process of analysis.
The image at the right is a snapshot of the Basic Responsibilities to be fulfilled by a Data Analyst in Amazon.
Skills Required to Become a Data Analyst
Data Analysts have to perform multiple operations starting from collecting data to exploring them to modeling them as well. All these operations require a great deal of knowledge and practice, as a minute error may change the outcome entirely.
As discussed above, Data Analyst and Data Scientist are the terms that are easy to be misunderstood. Since, we have discussed the basic nature of difference in the previous section, so we will directly go on to the unique and common skills to be honed by a Data Analyst.
Therefore a Data Analyst has to possess following skills to master this field:
- SQL (Structured Query Language): SQL is one of the most important skills for a Data Analyst. Data Analysts need SQL for various purposes like storing the data, fetching the data, relating multiple datasets, updating the data, etc. SQL is highly preferred Database Language and is a must have for any Data Analyst.
- Microsoft Excel: After SQL, Microsoft Excel is another important tool that a Data Analyst must know. Microsoft Excel helps Analysts to handle smaller datasets and perform multiple functions as join, loops, macros, etc., to get the desired output.
- R or Python: As the trend going on in the IT industry, anyone who wishes to analyze the data has to master R or Python. For a Data Analyst, market is changing a with each passing day requiring complex datasets to be dealt with and analyzed, which is not possible with Excel. R and Python not only allow the analysts to wrangle the data, but also help them visualize the insights using various Data Visualization packages.
- Data Visualization: Let alone analyzing the data is not sufficient as visualizing the outcomes and drawing conclusions based on them is also important. Data visualization tools like Tableau, Power BI, QlikView, etc., help Data Analysts comprehend the outcomes.
- Communication and Presentation Skills: These are necessary skills that any professional from any background must have. Communication and presentation skills are important for a Data Analyst as they need to convey the information to various teams and to the management, and conveying that in an efficient manner.
- Teamwork: Data Analysts work with Data Scientists, web developers and many other different people to get the job done. Therefore, coordinating and being an active participant in team activities is highly important for a Data Analyst.
- Problem-Solving: The data Analyst must have an eye for details. An ideal Data Analyst candidatewill be able to perform the root-cause analysis. She must be a critical thinker and should be able to think out-of-the-box.
- Business Thinking: In order to solve a business roblem A Data Analyst must know the details about the business and the industry. A Data Analyst can also leverage these knowledge to enhance his performance.
Qualification Required to become a Data Analyst
In order for you to become a Data Analyst, you have to gain following qualifications:
- Earn Qualification: A Data Analyst needs to acquire either a Bachelor’s Degree in Business related fields. A regular bachelor’s degree may not be of much help as the candidate needs to showcase a specialization. However, if the candidate earns a Master’s degree in Business, it will serve the purpose.
It can also be done by going for online training courses, as these are being highly recommended by the companies itself.
- Gain Work Experience: Merely earning a degree will not land you the job as a Data Analyst. You have to gain work experience in corporate will give you good on the job training as well as will provide you in-depth business knowledge.
The picture at the right side is a snippet of Preferred Qualifications for a Data Analyst, posted by Amazon.
Data Analytics Vs Business Analytics
Business Analysts are the professionals who explore and analyze the raw data to make Informed Business Decisions. Unlike Data Analyst, the roles of a Business Analyst include:
- Analyzing and evaluating business processes to determine the metrics
- Preparing a report and communicate the reports to various teams and stakeholders.
- Recommending various strategic plans, procedures, and improvements to the management.
While Business analyst utilize the data to help the organizations make crucial decisions, Data Analysts facilitate the organization with insights that they can use on their own.
Data Science Vs Machine Learning Vs Data Analytics
Now that you have gotten a fair idea of Data Science, Machine Learning, and Data Analytics and the skills they require, let’s take a comparative look at all of them here, to help you make a decision in a better way!
The above table gives you a quick glance at the career prospect in each field and gives you a career perspective as well.
As the saying goes by Weslley Moura (Data Scientist), “Data science is helping the world to make sense of data. Entities from different sectors are taking advantage of data science to help them to interpret data and extract meaningful insights from their business. Sometimes, data science can also help to create smart agents to automate business processes by the usage of data.”
Some examples include: implementation of chat bots to automate customer support, improvement of text translation, application of image and video caption for computer vision, fraud identification in real time and many other use cases.
Data Science vs Data Analytics — Which One Should I Choose?
Quite often this we have come across this question where beginners and sometimes even experienced professionals ask “How to choose between Data Science and Data Analytics?”. Well, to start-off, we would like to say that Yes, it is confusing, especially to people who are new to this domain. Both the profiles sound similar and working tools also indicate the same. Hence, it is highly important to know the better fit here!
So, we will do a quick comparison and see who is fit for what.
|Fit for Data Science||Fit for Data Analytics|
|For professionals who are good at problem-solving.||For professionals who are good at computation.|
|Suited for candidates with strong Programming and Data Visualization skills.||Suited for candidates with strong Database and Programming skills.|
|Better suited for people who have worked as BI engineers, business analysts, IT application engineers, Architects, and Data analysts.||Better suited for people who have worked as database administrators, data warehousing professionals, QA engineers, and associates in Sales, Marketing, etc.|
Despite the difference, Both Data Science and Data Analytics are equally challenging jobs and rewarding too. Take a look!
As per LinkedIn, the average Salary of a Data Scientist is $1,05,000 in U.S, in which the highest paycheck is drawn from the Entertainment Industry followed by Software and IT. The top 3 Locations where Data Scientists are thriving are San Francisco Bay Area, Greater Seattle Area, and Los Angeles Metropolitan Area.
This clearly indicates why all the companies are grabbing skilled Data Scientists with both hands with top-notch package in return. Some of the top companies paying the highest paychecks to the Data Scientists are Airbnb, Facebook, Apple, Google, etc.
As far as Data Analytics is concerned, the Average Salary of a Data Analyst is $61,000 in U.S. -Says LinkedIn.
As per the same report, the top 3 industries that are paying highest salaries to the Data Analysts are Entertainment, followed by Hardware & Networking, and Software & IT Services around San Francisco Bay Area, New York City Metropolitan Area, and Greater Seattle Area among all.
Clearly, Data Analytics is also a domain in IT which is creating a pool of jobs for aspirants from various academic backgrounds. Data Analyst is basically a prior stage of becoming a Data Scientist. Or in other words, in order to become a Data Scientist, the candidate must have an exhaustive experience as a Data Analyst.
So, if you are someone who is willing to start-off your career in Data, Data Analyst will be a better otion as Data Analysts work closely with data Scientists. Hence, gaining an experience in Data Analytics will help in moving forward in career!
How to choose between Data Science and Machine learning?
As discussed in precious section, Data Science is the hot cake in the industry. Every aspirant wants to become one, and every company wants to hire one. But what is the career scope in Machine Learning? Does it provide good career opportunity to the aspirants? Let’s find out here!
The Global Machine Learning Market is expected to expand at 42.08% CAGR during the forecast period 2018–2024. – Market Research Future
With the invent of Cloud services, the amount of unstructured data has grown, which has caused the companies to deploy Machine Learning Solutions, making it being adopted even more widely. Tech biggies like Amazon, Google, Microsoft, etc., are investing heavily into such Machine Learning and Artificial Intelligence technologies.
Credit: Market Research Future
As per Linked Salary Report, the average Salary of a Machine Learning Expert is $1,25,000 in United States. The to 3 industries offering highest salary packages to the candidates are Consumer Goods, hardware & Networking, Software & IT. The top 3 locations hiring Machine Learning Exerts in highest packages are San Francisco Bay Area, Greater Seattle Area, and New York City Metropolitan Area.
It is clear that After Data Science, Machine Learning is the new craze among the tech companies, and hence they are hiring more and more number of Machine Learning Experts. Hence, anyone who has been wanting to become an Artificial Intelligence Professional or a Machine Learning Expert, now is the time for you to start planning and preparing for it.
Do you need a CS degree to get into Machine Learning?
Most often than not it is found that aspirants worry about the degree which they should pursue or whether their degree is acceptable in the domain of Machine Learning or not!
To answer this question, we would say that Machine Learning is a technical domain and getting into this is not that straight. No, it necessarily does not mean that people from other qualifications cannot get into this area. Of course they can. But a Machine Learning deals with huge volumes of unstructured data, analyzing which requires the candidate to be proficient in programming languages, computer basics, data structures, computations and a lot of things which can be easily be acquired if you have a CS degree.
However, if you do not have a CS degree, you can learn all those skills through any online training course. Look for the course that you want to pick up and add the knowledge to your Machine Learning skill-set.
Simpliv is one such destination, where you can acquire all the skills without going to multiple places. One platform, multiple courses suited to different needs, and convenient prices make Simpliv, a trustworthy, and loved Online Training Centre across the globe.