“Data scientists are involved with gathering data, massaging it into a tractable form, making it tell its story, and presenting that story to others.” – By Mike Loukides, Vice President of O’Reilly Media

Data scientists are individuals who use who collect, analyze and then produce quality information from the piles of data being available to them. Data scientists use several tools to find relevant data such as data mining, web scraping/data harvesting, and many others. Their study is based on the relevant data in their hand from which they help to create predictable solutions that can help a brand to take measures before it can take place.

For instance, as you are well aware of the business, no trend remains constant, it is either replaced or modified. With the changing trends comes the change in tastes of prospects as well. Today your product might appeal great with your prospects but 5 years from now when technology is riper and trends are getting much more versatile, how will you be able to keep up with the change?

Change isn’t easy, which is why having a sneak peek of predictable information can help brands like yours to succeed better. Say your prospects want to see more additional and unique features in your solution or they want you to automate your solution more easily, all these notes can help your brand to improvise on the solution being sold by you so that when the change in trends happen, you have a solution which is exactly what the prospects expect.

Data scientists do more than just collect and create predictable solutions. They also have their hands on creating algorithms to solve complex problems. Their main agenda is to create solutions that can help a brand to grow better. They are constantly indulging with the data they have and if you are keen on research activities and find yourself to increase the learning knowledge on problems and wish to enhance solutions for the benefit of brands or other relevant sources, data scientists could be the right fit for you. 

“The job of the data scientist is to ask the right questions. If I ask a question like ‘how many clicks did this link get?’ which is something we look at all the time, that’s not a data science question. It’s an analytics question. If I ask a question like, ‘based on the previous history of links on this publisher’s site, can I predict how many people from France will read this in the next three hours?’ that’s more of a data science question.” ―Hilary Mason, Founder, Fast Forward Labs

“Some of the best theorizing comes after collecting data because then you become aware of another reality.” – By Robert J. Shiller, Economics Nobel Prize Winner

Data is important. It is the key to a brand's success. Many brands today depend heavily on the use of data to monitor, learn and understand how to prospect's attention can be captured easily.

“Consumer data will be the biggest differentiator in the next two to three years. Whoever unlocks the reams of data and uses it strategically will win.” – By Angela Ahrendts.

And with such an agenda, data scientists have a wider scope of growing twice the numbers in the years to come.

Many industries depend on this profession solely, from detecting fraud prevention, human resources, marketing, IT, travel sector to economic sectors, medicine, machine, and others, data scientists is a valuable treasure that can not help a brand grow better but also change the way a brand engages with prospects.

Today’s prospects are well aware of what is being sold in the market if you are going to continue selling them your sale pitches don’t be surprised by the lower number in lead conversions.

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.

1 . Machine learning - Retrieving accuracy in all theoretical data

2. Signal processing - Improving and analyzing the digital signals

3. Data mining - Finding data which can be used to create predictable solutions 

4. Databases - These are the places where data is stored so that the information can be further used to be analyzed.

5. Data engineering - It is the act of viewing data through different sides so that impactful insights can be retrieved

6. Visualization - Imprinting the theory of information into a visual form for better understanding

7. Data preparation - Curating and segregating data in a form which is easily understandable to any kinds of readers

8. Predictive modeling activities - Incorporating mathematics and statistics to test the processes by creating it in a chart format so that a likely outcome can be curated

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.

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.

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

1. TO SPECIALIZE IN THE SUBJECT DATA SCIENCE 

View the course given by John Hopkins University. The course covers the complete information with reference to data science. It guides you through every step right from the basics until you reach the last stage of the data science process. Before the completion of the course, you will be asked to put all the knowledge you have retrieved via the course in practical form. You will be undertaking a project where you will have to create an action with the help of real-world data. 

2. TO GET AN EXPERT OPINION ON DATA SCIENCE

IBM has created an exceptional course where the data science individuals who have been working in this field share their insights about the industry. This is a great course because the students will be receiving live insights from the people who have the experience and know what is required for the upcoming data scientist like you to inherit in. There is a personal communication taking place from experienced peers who can guide and help you to take the right direction towards the data science field. 

3. TO GET DETAILED INPUTS ABOUT DATA SCIENCE

The National Research University Higher School of Economics has created an impactful course which doesn’t just guide you on data science but also lets you compete in data science competitions. This activity provides students like you with a piece of more practical knowledge and lets you take charge during the process. Its main agenda is to help build the right skills in various sectors. It relies more on teaching practical knowledge for better understanding.

4. TO START WITH THE BASICS OF THE DATA SCIENTIST WORLD

If you are interested in learning about data scientists don’t worry if you do not know about it because this course has been curated from instructors to name a few Dr. Ana Bell, Prof John Guttag who will guide beginners on what data science is and how the most essential program Python can be used. The main aim here is to help students to write the programs so that they can solve minute problems easily. When it comes to teaching the Python program, it uses 25 programming languages. 

5. TO APPLY DATA SCIENCE IN REAL LIFE

For those serious about taking data science as their career can view the course by IBM where its stresses on educating students about Python, data visualization, data analysis as well as get impactful insights on how you can break down any data problems. The great thing here is that even if you don’t have much knowledge of the topic you don't need to take this course.

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

2. 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

3. 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