How to Get into Data Science as a Career

Data science is a modern domain of Information technology. Data Science emphasizes various fields associated with it such as statistics formulas, methods, graphs, computer science, information technology, etc. It is a vast field that requires the amalgamation of different fields associated with it. In general terms data science deals in extracting useful information or data patterns from structured or unstructured data sets and then utilizing it for the target application and business operations. The use of STEM(science, technology, engineering, maths) technology is the base of data science which enables their use in hybrid applications.

Technologies required to learn Data science

• Big data technologies
• Python/R programming
• AI(Artificial intelligence)
• Hadoop
• Data lakes
• Data mining
• Database Knowledge
• Data analyzing
• Data visualization

How is data science for a career?

Data science is the most lucrative and demanding job in the information technology field in 21 century. You can reach heights in your data science career. Work with top-notch companies, professionals and deal in analyzing data sets useful for the company. You get to learn the updated business intelligence technology which is going to dominate the future.

Perks of data science as a career

• You can explore the huge world of data mining, application, etc
• Attractive figures salary which will be enough for your needs and hobby.
• Financial stability
• You can work remotely or through the office.
• Proficient knowledge of data science field, business, and others
• Chance to get connect with experts in data science and other fields across the industry.

How to get a job in Data Science?

Now, the question arises, can anybody get into this field?. Or What are the requirements to become a data scientist and ace our career. The requirement is simple: You just need to gear up yourself to get drowned in the deep ocean of data science. You essentially don’t need any degree in data science-related courses to get into companies, until and unless the company has not mentioned the point “degree required” in the recruitment document. Many big companies such as Google etc focus on respective skills or certification rather than stressing on 3-4 year degrees. Skills, proper and updated knowledge is a must for thriving your career in data science. Following the below mentioned few points, anyone can easily get into their dream company dealing in data science.

1. Person can easily gain the required knowledge and certification from online tech education platforms and multiple other websites dealing in programming, data science, etc or you can get a degree in data science from a university or college.

2. Cover the advanced topics, libraries, frameworks such as Apache Hadoop, etc.

3. Practice more and more. Getting through and learning concepts alone will not help in reaching the peak. The application of concepts learned and their proper analysis will surely help in getting a job in data science.

4. After working hard, Relax, you have covered almost all points required to be learned in data science. Now you can confidently apply for a job in data science.

How can we apply for data science internships before applying for a job?

Again The answer is very simple and clear. Internship before a job will help you immensely in gaining short experience of how things work in reality. You will get a chance to work in the data science field along with the guidance of top professionals, colleagues, mentors, teams, and many more people surrounding you in a professional and ethical environment. Look for online websites. Match the data science profile requirement with your skills or you can pitch the reason for your application to the respected portal. Keep applying, you will surely land an offer in your hand.

Other unnoticed advantages of data science internships are as follows:

1. You get the extra learning of how to play a major role in projects and talk to managers, CEOs, etc.
2. You get to form a professional connection with a master’s in data science. Also, you can ensure a strong professional network game.
3. It improves your overall productivity, soft skills required to boom up your career.

How to apply for data science jobs?

After learning how to apply for data science internships. Now we will get to know how to apply for data science jobs 

1. Create your portfolio or resume most attractively, highlighting your objective, skills, achievements, project undertaken. The more clear and well-structured resume surely scores brownie points and is noticed easily by recruiters.

2. Search website or platform providing data science jobs across the locations. Few websites which do provide the same facility such as analytics job, Big data jobs, Datanami, Internshala, Hired, etc.

3. Analyse in detail the requirement of the target or select company. Read the doc or job specification properly. At last, Apply for selected jobs matching up with your skills and company’s requirement. 

4. Prepare yourself for the interview, brush up on data science concepts, read out all their policies, company’s background, work methodology, and other information which might help you during the time of the interview.

Are artificial intelligence, data science, machine learning jobs related?

Data science and artificial intelligence fall under the same domain. Data science deals with forecasting business graphs with the help of analyzing data and extracting useful information from it and then visualizing it. However Artificial intelligence deals with training machines, robots to behave with human competency. Neural networks, deep learning, and natural language processing are the technologies used in artificial intelligence.

Machine learning is a subset of artificial intelligence, it mainly focuses on making machines learn through experiments, trials, observance, and analysis. Though these three are connected you have to learn extra or different technologies to perform well in your respective fields. These technologies have potential scope in various fields such as Agriculture, Health sector, Manufacturing industries, Real estate, education, Defence, Transportation, etc. There are many roles and responsibilities associated with these fields such as 

• Data science engineer
• Data analyst
• Big Data engineer 
• Statistician
• Machine learning/Data Science/Artificial intelligence Researcher
• Business Intelligence developer
• AI engineer