‘’Data scientists are a new breed of analytical data expert who have the technical skills to solve complex problems – and the curiosity to explore what problems need to be solved.’’

Data scientists understand the cruciality of what data can do, they are well aware of how significant a solution can be curated from the data. Did you know that with valuable data in hand, data scientists can easily find solutions as well as create solutions which will be a more profitable and successful solution for any businesses when they apply it.

What started as data analysis and statistics, had now turned data scientist into a valuable asset in the field of data science which any brand wouldn’t mind spending large amounts of money to retrieve powerful solutions from such experts.

But how did data scientist become a demand?

Statistics and its models are well known in the field of data science. From starting as a data analysis and statistics approaches, data science today includes more than just these factors. With the businesses and industries growing digital, data science is now practising approaches which include machine learning, artificial intelligence and internet of things. 

Data is no longer a solution which would require just identifying it, it now holds the essential factor to solve and create expert solutions which could turn the way a business grows and conduct its lead capture activities. From resolving prospects behaviour pattern, businesses understood that with a lump sum of data available, chances are innovative solutions can be retrieved which will revert in good results or outcomes.

For instance, say if the data you have in hand can help you find an innovative solution on how you can conduct lead generation activities in a shorter period, wouldn’t you invest your focus and amount on such an asset? That is exactly the power data science and the ones who conduct it, data scientist holds. 

So which areas exactly can you find data scientists functioning the most?

conduct it, data scientist holds. 

So which areas exactly can you find data scientists functioning the most?

WHAT AREAS DO DATA SCIENTISTS SPECIALIZE IN?

Data scientists specialize in six essential areas of data science:

1 . Investigations- With the help of vital tools, data scientists collect information which will help them in conducting their research and solving complex problems with better clarity

2. Selection of right models and methods of data retrieval- Not all methods will go hand in and with the wok data scientists conducts. Hence with their knowledge and the growing time frame, data scientists will acknowledge which tool will be most suited for different workforce situations

3 .Working with brands- To help a brand grow better, data scientists will indulge with them to gain as many insights as required for them to conduct their activities. With continuous engagement, data scientists will be able to collect and analyze relevant data which matters

4. Incorporating the right tools- Data scientists deal with multiple projects. Each of those has a huge data situation in hand. In order to scatter through the heap of information and find the relevant data, data scientists make use of essential tools or software which can help them to analyze their work more easily 
5. More theory form- The world of data science revolves around huge heaps of data in theory form. To assist such a process data scientists to have countless applications to cater to
6. Evaluation of essential tools- A tool six years back may have work wonders then but may not be effective in the current market. Hence data scientists revolve around the use of the latest tools to measure the right effectiveness which will benefit their work better. 

7 . Having an interest in data scientist doesn’t completely accommodate all the requirements needed to become one, hence acquiring the right skills can benefit many potential students like you to walk into the career field with the right direction.

“Data really powers everything that we do.” – By Jeff Weiner, CEO of LinkedIn

1. KNOWLEDGE OF R PROGRAMMING 

Potential learners looking to seek a career in this field must have a good knowledge of R programming. This is one of the most widely used programming tools ‘’In fact, 43 percent of data scientists are using R to solve statistical problems.’’ The main agenda for using R is because it is responsible for solving any kind of problem occurring in data science. 

2. EAGER TO LEARN MORE

Data scientists deal with data and as mentioned earlier the market keeps fluctuating. So with the change in time, the data changes. To become a great data scientist, analyzing data shouldn't just be your limitation. You must enhance your knowledge each day. Keep reading and updating yourself with new information because when you are aware of what’s happening it becomes easier for you to analyze the solutions to many situations.

3. HAVING AN UNDERSTANDING OF THE INDUSTRY

Collecting and analyzing data without first identifying the purpose of doing does not seem right. A main role for the data scientists in the making is to first understand what industry they are indulging in, what is the status, what had the industry worked before, what is happening in the industry and also keep in mind that for every problem that you are identifying or every solution you are creating ensure that it serves a purpose for what is happening. It should be beneficial to those who you are helping. 

4. ENHANCED COMMUNICATION SKILLS

Another skill that an upcoming data scientist should master is their communication skills. Collecting data and then analyzing them or finding solutions for complex issues are the job responsibilities of data scientists but there is also another responsibility which data scientists share too, the art of conveying the technical knowledge into user-friendly information. A data scientist must know how to convert the technical words into a story that is easily understandable and interpreted by non-technical users.

5. HAVING A GOOD TEAM SPIRIT

Data scientists don’t operate singly. They have to engage more with the individuals who are working on the same projects as for them. Sometimes when it comes to helping a brand grow, a data scientist needs to indulge with every member of the team so that they can receive all the insights needed to shape their workflow better. Hence data scientists must know how to work in a team and have the right engagement with everyone so that at the end of the day the contribution is not just witnessed by them alone but of the whole team. 

6. THINKING FROM THE POINT OF VIEW OF A BUSINESS

You could have a brilliant knowledge on the skills displayed above, but if you don’t understand how you can cultivate them from a business front, the efforts you put it is useless. Any data scientists need to understand how their current actions can benefit a business or how can one move of theirs make an impact in the way a business can receive profitable returns. This factor is important as it helps to create a framework of what needs to be achieved.

7. CATERING INTO THE DEEPEST DATA PROVIDED

Do you know what makes a data scientist to really stand out from the crowd? The fact that they can see few data solutions even when its not visible in the first view. Data scientists can dig in deeper and identify how a particular data can actually help a business. Many times not everyone will be able to identify that, they will only spot the top layers, but for a data scientist, they dig in through the deepest layers, ‘’perceiving patterns where none are observable on the surface and knowing the presence of where the value lies in the unexplored pile of data bits.’’

Apart from these basic skills, it is also necessary for the upcoming data scientists like you to develop the right technical skills which can help to conduct efficient data activities.

1. PYTHON CODES

Python is considered as one of the most practiced coding languages in data science. ‘’ 40 percent of respondents surveyed use Python as their major programming language.’’ It is considered as one of the finest codes and is used for every step in the data science process. From carrying multiple different formats of data to creating valuable datasets, having a piece of good knowledge in such a code is a plus point. 

2. HADOOP 

‘’3490 LinkedIn data science jobs ranked Apache Hadoop as the second most important skill for a data scientist with 49% rating.’’ Hadoop may not be used often but it emphasizes its usage on the critical situations in data science. For instance, say the data being stored is too much for the memory capacity being held or if you have to transfer data to different servers Hadoop can be quite helpful here. It plays a great role in conducting data filtration, exploration, sampling and any kind of summarization. Having a piece of knowledge about this tool can be helpful.

3. UNSTRUCTURED DATA

Unstructured data isn’t easy as it includes activities that cannot fit into any database table. But cracking this can win you a great advantage while interpreting data. The reason why unstructured data is in a complex form is that all the data is just dumped together in one corner, because of this it becomes extremely difficult to crack it. Unstructured data is essential when it comes to concluding. Hence understanding how it can be managed and organized can help upcoming data scientists to earn a good skill in their career.

4. VISUALIZATION OF DATA

Another important factor for data scientists as mentioned earlier in the process of converting all the data into a visual form. Not many users are knowledgeable about the coding language or the words hence it is important that the data scientists know exactly how to create a visual form of the data curated so that when any user reads it they can understand what is being conveyed to them. Work on storytelling and chart/graphic creation for displaying better technical language.

5. KEEPING UP WITH AI (ARTIFICIAL INTELLIGENCE)

Today many companies are using the help of AI or machine learning to engage and conduct better business activities with their prospects. With AI growing, there are still many data scientists who are still lacking in such knowledge. Learning and implementing machine learning or AI tactics can help majorly in data science activities as it helps to solve complex issues that form a prediction based on the organization's outcome results. AI is growing and it is wiser for you to learn more in-depth about it and how you can implement it better with your workflow. 

6. SQL

SQL stands for structured query language and it is a vital process data scientists should know about. SQL helps to conduct essential functions such as add, delete, extraction of data as well as help to conduct analytical. Modifying database patterns is also one of its key qualities. processes. With SQL, data scientists should be able to write down as well as execute any complex queries in it. It is vital to learn this process as it has a lot of benefits, a few being:

Reducing the time required to solve difficult issues

Lets you access data, communicate with it and work on making it better

Enhances a data scientist profile for future growth

Helps to understand relational databases much better

“Learning from data is virtually universally useful. Master it and you will be welcomed anywhere.” – By John Elder, Elder Research

Understanding the requirements needed to become a data scientists, now let’s move on to understanding what your role actually does.

At Nudge, they had explained this question by interviewing their own inhouse data scientist, Dr. Zoe Katsimitsoulia. 

Source: Nudge

When asked about her education qualification, Dr. Zoe stated,

'’I started with an undergrad at McMaster (Hamilton) in biochemistry, with a specialization in molecular biology & genetic engineering, but also ended up taking a few courses in computer science. I ended up completing a Masters at the University of Liverpool, which eventually lead to a PhD at Oxford in computational biology. I then continued my work with a post-doc at Columbia University in computational biology. ‘’

She continued to state,

’'The programs I’ve looked at have been quite variable in their definition of data science as reflected in their curriculum and in such a short time frame can only provide a superficial treatment of the field at best. This could lead to problems in the future, as the demand increases for this role in business – and you have a workforce with entry level skills.’’

Answering the question about the meaning of data science, Dr. Zoe claimed,

‘’Data science currently is really a continuum being defined by businesses trying to optimize and drive improvements.Data science can come into play when we have complex systems generating lots of data we wish to take advantage of. That means more than just analyzing the data. It means building models using state-of-the-art algorithms to explain or predict behaviour. These models need to be testable and this is where the scientific process comes in. So the major difference between data science and data analysis, is the math and then mentality.’’

Giving an example to the context above she explained,

’I may have a hypothesis that by using profile information I can build a program that recommends content based on explicit and implicit interests that is better than a random selection. There may be multiple variables to consider, and the data or lack of data may create challenges. However you can model this system of people, profiles, interests and content using probabilistic algorithms, clustering techniques, and collaborative or content based filtering approaches, to name a few options.’’

Continuing her interview she replied to the following questions which provided more clarity on data science and scientists:

1 . What Types of Businesses Need Data Scientists?

‘’Well really every business can take advantage of it. There is however this big expectation on the data scientist to transform a business, but really it is much more of a collaboration between the data scientist and the business leaders.’’

2. So Where Do You Find Data Scientists?

‘’A lot of scientists have jumped ship and left academia like I did. The reason (for me) is that in business you experience much more immediate feedback than in research – and that can be very rewarding. Also the money is better :).’’

3. What Do You Think the Future Will Be for Data Science?

‘’ I think there is a lot of great work being done in fundamental research in academia, but also business has driven a lot of the progress and innovation in the field. Companies like Google, Yahoo, Facebook and Linkedin have huge amounts of data they can apply science to, to help both themselves and their users. Ultimately I believe the discipline needs to be better defined, and have more distinction between different types of data scientists. This will really help generate more specific roles, with the needed skills that add real value to businesses.’’

STEP 1: IDENTIFY THE PROBLEM

Before a data scientist can reveal a solution, it first needs to identify what the problem is actually about. For instance, say if you want to solve the problem of capturing only qualified leads from the bunch of leads you get, you need to explain your data scientist how it affects your business. When data scientists completely understand what the issue is, that is when they will be able to create a direction which will help them solve that problem.

STEP 2: COLLECT VALUABLE DATA TO ASSIST THE PROBLEM

Once the problem has been identified, data scientist will move forward to collecting the data which will help solve this issue. Since the data available is huge, data scientists will scrape through and take only those data which matters in this process. Incase if the data is already available, data scientist will check if the information is sufficient or it requires some more resources. 

STEP 3: BEGIN THE DATA PROCESS

When you receive data, not all that is there in your hand will be of added value. There will be many data which is lost, corrupted, bad or not useful. Hence before a data scientist can even begin to analyse the results and create or identify a solution, it is necessary to have clear data in hand which will be efficient while analysing the solution.

STEP 4: GET DEEPER WITH THE DATA IN FRONT OF YOU

 Once the clean data is right in front of you, it’s time to dig in deeper with it. Exploring through the data you need to find the cords which can help to assist you in creating the solution required. You will be able to create graphs, and representations of the possible approaches which can work to solve the problems

STEP 5: CONDUCT DATA SCIENTISTS ACTIVITIES (ANALYSIS)

Here is when the processes effectiveness is shown. Data scientists will be able to place all their approaches and start creating predictions and possible outcomes to ensure that the problem being solved has a reasonable solution.

STEP 6: REVEAL THE OUTCOME

Lastly, the main feature here is to convey all the technical solutions into an easily understandable manner. For data scientists, you must be able to convey the solution in the form of storytelling where the other user listening to it should be able to understand what you are speaking. For instance, if you are going to speak in a technical contact a user may not be able to quite catch their attention to understand what you are speaking. But if you are able to explain the process in a story form, the user will be able to understand the problem and why the solution you are suggesting is beneficial for them and the problem.

To conduct the above process, data scientists should have the right knowledge to commence it which is exactly why the right educational background can add an extra benefit for you to understand and function as a data scientist.

To be data scientists, you need to have a bachelor’s degree in either Computer Science, Social science, Physical science, and statistics. The top 3 fields which many of your peers specialize in order to analyze data are mathematics and statistics, engineering and computer science. 

Once you have completed your bachelor's, you have to pursue a Master’s degree in either Data science, mathematics or any other field which relates to the profession. Apart from this, you can take up courses which can enhance your knowledge much better.

If you want to give data scientists as your career shot, you can try out the below free courses which can help to enhance your skills as well as provide you with the right insights needed to walk towards this journey.

1. TO LEARN PYTHON AND SQL

Python and SQL are two such coding languages which are the most crucial when it comes to becoming a data scientist, Codecademy teaches such codes for free so that individuals can you can understand the value of such a learning and apply it during your data scientists career process. This course is absolutely free which is the best part and it teaches you the basics which frames your foundation for the subject better. A duration of 7 hours each will be dedicated for SQL learning and Python, a 25 hours duration will be applied. 

2. LEARNING ABOUT DATA SCIENCE WITH PYTHON INCLUDED

You have decided that data interests you and that you want to take data scientists seriously then Udemy has a free course which can spark the interest in your decision much more. From learning about the basics of data science to understanding how python functions and also receiving insights on the type of jobs the role of data scientists offers, udemy has it all covered for you that too for free. Get this course which has 12 lectures with a dedicated hour of precisely 2 hours, 30 minutes.

3. GET A BETTER UNDERSTANDING OF LINEAR ALGEBRA

Skillshare is sharing a video which will help you understand another essential factor in data science, Linear Algebra. Get a detailed insights on the lesson with a 44 videos consisting of exceptional information which will help you to understand the subject right from the basics. The course costs $15 a month and the 44 videos are a total coverage of 6 hours, 51 minutes. 

4. GET INSIGHTS ON MACHINE LEARNING 

Don’t have much time on your plate but are still engrossed into learning about machine learning? Well Udemy has a quick solution for you. Udemy is offering a 3 hours course which covers significant topics such as how AI and machine learning play a massive role in data science. The course cost is $150 and consists of 41 lectures with a total time of 3 hours.

5. LEARN MACHINE LEARNING FROM THE EXPERT

Apart from the above, there is also another website that can help you to learn everything you need to know about machine learning such as what it means, how it works and why it is needed in data science and much more with coursera. To preach such valuable information, the Coursera Co-founder itself will be talking to you. The course is free to audit and for certificates, $79 needs to be paid. The course duration is for 7 hours a week for a set of 11 weeks.

6. GET INFORMATION ABOUT THE DATA SCIENCE PATHWAYS

Codecademy has a course which can help you to understand how the world of data science operates. Their course if so effective that you can easily learn how you can apply the practices in real life. Developed by data scientist, the course covers all the relevant factors needed to become a data scientists today. The course costs $19.99 a month and and is self paced.

7. GET SPECIALISED WITH DATA SCIENCE

Coursera is offering a specialised course in data science where it details out everything you need to know about the world of data science. From framing the right questions to developing the soft skills required, this course will convert a beginner into an advanced data science professional with this course. The cost of this course is $ 49 a month and has 10 courses where the duration is between 3-6 months.

8. DATA SCIENCE PROGRAMMING COURSE

Udacity is offering a ‘nano degree’ course where it gives users the ability to share such a platform by learning from experts other similar like minds. Get solutions to any of your queries and always stay on the direction. The course price is $50 a month with the course duration being 10 hours a week over 3 months.

Getting in data science isn’t difficult when you have the right guidance supporting you. To ensure that you don’t miss out on anything, ensure that you prevent the below mistakes from happening so that the path towards data science as your career is smooth.

1. PUTTING YOUR FOCUS MORE ON THEORY

Yes there is theory too in data science for instance studying machine learning and algorithms and much more. Constantly occupying your mind in theory will not only get you bored but also pull your focus and attention away. Hence it is necessary that while you study the theory also enforce a pinch of practicality in it. For instance you could understand how the theory you just studied applies in data science or you could create real examples to enhance your studying better.

2. CREATING MULTIPLE ALGORITHMS FROM SCRATCH

It is a great thing that you are creating algorithms from scratch but do you know what's more important. The way you apply it. Creating algorithms and then working on it can be tiresome and time consuming hence there is already machine learning libraries and cloud based solutions who can do it on your behalf. What you can do is create algorithms for learning and strain your focus more on how you can apply the right algorithms to fix the puzzle.

3. GETTING TOO EXCITED ABOUT THE FUTURE CREATIONS

Being excited is great, but in data science that excitement can cost you to miss out on a lot of things. For instance, you may think you could develop a solution which can help to speed the process of lead generation, but did you know that there are also other algorithms which can help the same process to be conducted in a much more eunique and efficient manner than yours? This is exactly why you must be aware of all the approaches and practices being applied.

Ensure that your knowledge isn't just limited to one thing, explore the data you have, identify and find solutions which can help to frame solutions much better.

4. CREATING A TECHNICAL RESUME

A resume stands out for you from the crowd. It contain all the information which signifies why you should be chosen from the crowd and if applying for a data scientists work role, all you do is create a resume with high technical terminologies, chances are you may not get the job. Data science is a technical based process but when you list it out on your resume you need to speak more about what you did with the technical terms being used, how were you able to provide a solution.

For Instance if you used machine learning, explain why you used it, for what purpose, what did you achieve from it, what is the problem. Data scientists can be considered a sherpoies who can enhance a business for better, and if you don’t state this matter, don’t be surprised with your job interview outcomes.

5. NOT CONSIDERING COMMUNICATION SKILLS AS IMPORTANT

Communication skills matter rin data science and many believe that if they don't practice or they avoid it is alright. But the fact is it's not alright. Communication skills are important in data science because when you identify a problem and create a solution you need to explain to a user how it can benefit them. If you are going to use technical jargon to explain the process it could be difficult to even apply the solution.

Hence a data scientist must learn to speak well and convey its solution being offered by them easily and understandably. You can try to practice it with you peers by explaining them the solutions you create or the algorithms you made in an easy and non technical manner, structure out how you can explain your findings in a presentable manner and also you could continuously practice how you speak and break down the technical terms into easily understandable terms for users to understand.

Keeping the above essential information in hand, you need to also keep in mind about the other set of factors which can help make a difference before you can even enter in the data science field.

1 . Data science is diverse and there is alot to contribute in it. Data science is applicable to help businesses, research centres and so much more that it is necessary that you know which one you want to get into. Data science is huge and has several factors under it as explained in the above sentences, so it is always important that you keep in mind what matters.

2. It’s alright if you don’t know a few things during your data scientists period. Always be eager to learn and understand the different ways you can use the data available with you for a good purpose. There are many approaches and practices which require your attention and can help you out. Keep exploring your role and the data which you manage to spark better solutions
3. You don’t have to master every data science tools the world has. Not every tool is being used. It depends on the company you work in. Every company would use different tools which you can easily familiarise yourself as you will be working there. 
4. It’s always great to have basic knowledge whether it's your projects, the industry you work in or even the basic tools. Having a brief knowledge can help you understand the workflows better and help carve your thinking towards the creation of an enhanced approach.
5. Develop critical thinking as it can play a huge part in your data science career. You could have all the right tools but when you are not able to identify the purpose of what you are doing or the impact of your actions, you might not be able to deliver much. Hence increase your critical thinking capabilities and work towards finding out the purpose of all your actions
6. Always remember that a good data scientists isn’t just one who has a lot of knowledge or only has knowledge on certain tools, a good data scientist is one who knows how to combine both of these assets to create or identify an exceptional solution.
7. Data science has a lot of scope which is why you need to carve your footsteps towards the right direction. Which type of data scientist you aim to function in mostly?

1. DATA ANALYST

JOB PROFILE:

1 . Extracting data from SQL databases

2. Enhancing your skill from Excel
3. Converting data into visualizations (basic)
4. Monitor A/B testing
5. DATA ENGINEER

JOB PROFILE:

1 . Work derived more from the analysis basis

2. Requires engineering skills
3. Requires software skills
4. Deals with data infrastructure
5. MACHINE LEARNING ENGINEER

JOB PROFILE:

1 . Focuses more on individuals who want to move towards the academic route

2. Has good knowledge of mathematics, statistics, and physics 
3. Mostly hired by data-driven companies
4. DATA SCIENCE GENERALIST

JOB PROFILE:

1 . Conduct analysis

2. Create data visuals
3. Perform production code

‘’The demand for data science is enormous and businesses are putting huge time and money into Data Scientists.’’

Data science will be growing and developing in multiple numbers. Data will be emphasised more and will soon be dependent for any business successes. Even with the rise in technology, new methods will evolve the way a data scientists can retrieve, view and manage data. 

‘’Data science is the core of the business because all the operations related to the business depend on the data science from statistics to decision making companies are using data science and its story not ends here. Now data science changes into machine learning and big data.’’