Data scientists are in high demand in organizations. Ever since the smart device and computers have evolved, they produce a lot of data. This large amount of data is known as “Big data”. Big data will be of no use unless some meaningful information is generated from it. So data scientists figure out the insights from these data and ways to put these into use.
In 2019, 2.9 million data science job openings were required, and looking at the trend, 2020 will only see an increase. According to a report, in 2020, the job requirements for data science and analytics are projected to boom to by 364,000 openings to 2,720,000. No wonder professionals are willing to explore their opportunities in this domain by taking up data science training.
Willing to know more about this exciting field? Read on and you will understand how to get started.
What is Data Science?
Since data science has an attractive career opportunity, let’s know in detail what data science is all about. Data science consists of a set of practices and skills, which aims at analyzing the data to take out advanced solutions to business problems. Data science comprises the practices and algorithms of statistics, business modeling, machine learning, programming, and databases. Most importantly, data science combines past and current data to predict future outcomes and performances. Data science has vast applications in the industries of healthcare, travel, marketing, sales, credit & Insurance, automation, social media. It is known to boost business performance.
How to get started in Data Science?
The popular job roles that data science offers are data analyst, data architect, data administrator, data scientist, business analytics, data/analytics manager, business intelligence manager.
According to Payscale, the average salary of a data scientist in India is ₹707,763. Of course, it varies depending upon the company and its requirements.
The skills required to become a data scientist are the following:
One important aspect of Data Science is to learn programming languages. The most commonly used languages for this purpose are Python and R. Most data scientists prefer python because it is easier to learn and supports multiple libraries. Various libraries such as sci-kit, NumPy, pandas, etc are mostly used. Thus the person must be well-versed with python and its applications. Python contributes to data analysis whereby it figures out which data is relevant enough and which are not. Python acts as an effective tool to take out insights. Data analysis is performed with the help of the following tools SAS, Jupyter, R Studio, MATLAB, Excel, RapidMiner.
A Data Scientist must be well aware of the databases used commonly. It is an important part because the data scientist has to work on the data and learn to manage it. Most importantly, the ways to extract data and put it into use. The skills that an individual must know are ETL, SQL, Apache Spark, Hadoop. The tools required for this purpose are AWS Redshift, Informatica/Talend.
Statistics is one of the basic skills you must be aware of. Statistics concepts are important for the formulation of algorithms for machine learning and data analysis.so, learn statistics well enough to perform all the required operations.
Prototyping in Data Science is concerned with the initial release of the application to the people. Here the challenge faced was what data to reflect in the application and whatnot, as data science always solves problems to the complex which are not clear to some people.
Machine learning is the backbone of Data Science because its algorithms are useful to find new patterns in the data or to find the factors responsible for a particular cause. So, the individual must know very well the machine learning concepts and algorithms. The algorithms of ML, python, statistics, algebra are skills required. The tools that are used for ML are Azure ML studio, spark MLib, Mahout.
Data Visualization is a process of conveying information with the help of graphs, images, text. So the libraries of python and R are also used for Data Visualization. The other tools used for this purpose are Tableau, Cognos, Raw.
So, The top certifications for data science are the following:
- Dell EMC Proven Professional Certification Program
Dell EMC offers data science associate certification and an advanced level. It ensures that the person has all the necessary abilities required for data science. It is valid for two years.
- Microsoft Certified Solutions Expert (MCSE)
The MCSE offers data scientist certifications on the basis of two areas: one that focuses on business applications and another that focuses on data management and analytics. Also, the certification is valid for three years.
- IBM Data Science Professional Certificate
This is a beginner level certification that will help you learn skills of machine learning, databases, python, building models, Data Visualization, etc. It has 9 courses in it.
- Data Science Certificate – Harvard Extension School
Harvard offers data science certification for a combination of sub courses under it. The criteria are to pass in each one of the courses. It requires you to earn a certificate only when you have completed the course from Harvard itself. The certificate degree is valid for the lifetime
- Amazon AWS Big Data Certification
Amazon certifies professionals who have experience of working on the AWS environment. The AWS Big Data Certification ensures that an individual can manage a complex amount of data and will help you learn advanced tools of the AWS environment. The certificate is valid for 2 years.
With the vast increase of data, the demand for data scientists is also rising. As you know, Data Science offers good career opportunities for people with attractive salary packages. So, check out some courses in Data Science and start upgrading your career. It will be an added advantage for freshers to enter the job market with data science skills, as it promises growth as well. Those professionals who are willing to switch their career into a challenging and thought-provoking work, data science is for them.
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