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According to US Department Statistical Report, the demand for data Science Professionals will grow by 40 percent.
DATA is the new oil. All types of companies ranging from small to large size have started investing in a Data-driven approach and setting up a Data Scientist Team for data mining using Artificial Intelligence (AI) Machine Learning and Big Data. Which has increased the high demands in jobs in Data Science and the big data field? According to a report, there are approximately 50,000 jobs with the skills of Data Science & Machine learning is unfilled and top of that, there  40,000 new job openings being added by 2020 in  Indian market alone.
The Data Science field has created many jobs at various levels, so it is a good idea & time to start a career in the Data Science field.
Data Science can be defined in simple words as Gaining Insights and valuable information out of the data. In other words, Data science is the field of analyzing both structured & non Structured Data which involves methods of recording, storing, cleaning, reporting data to make use of useful information for the business. Data Science is employed of Computer science, Information Technology, Mathematics & statistics, which involve creating programs and algorithms for Data processing. Data Science Incorporate Techniques like Machine Learning, Cluster Analysis, Data mining & Data Visualization.
A Professional manages to work data science tasks are called a data scientist. Data scientists are involved in analyzing complex data using mathematical, statistical knowledge by using different algorithms and Machine learning techniques. Data Scientists are well equipped with computer skills and having command on Data Science tools like R, Python, SQL, etc.
Nearly every company now has the ability to collect data, and the amount of data is growing larger and larger. This has led to a higher demand for employees with specific skills who can effectively organize and analyze this data to gain business insights. In the current world due to digitization huge amount of structured and unstructured data is getting generated which is in Zetta and yottabytes. Which requires smart & high storage and cost-saving mechanisms. As the world entered in the era of Big data, to manage such huge structured and non-structured data there was a challenge for the enterprise until 2010. Data engineers and architects have Built the Big data framework and architectures like Hadoop, to manage and store data efficiently. The hearafter main focus is on gaining meaningful information from the stored Data. Here on Data Scientists are coming to the action of extracting useful information from data using various statistical and machine learning algorithms.
Jobs and responsibilities in the Data Science Field are to retrieve meaningful information by engaging data from various sources and get actionable insights. There are various Jobs and roles under the data science field like Data Analyst, Data Engineer, Database Engineer, Machine learning engineer, Data Scientist, Data Architects, Statistician. Professionals working in the Data Science Stream are jointly responsible to do Data Extraction, Data Analysis and Reporting & Data Visualization basis these reports it helps businesses to take right decisions.
According to Glassdoor’s survey out of 10,000 sample listings posted in Data scientist jobs, Python, R, SQL were the top three skills.
In case you are an aspiring data scientist or a seasoned professional in data science, it might be of interest to you to explore the above-mentioned tools and enhance your productivity by leaps and bounds
Python is one of the most popular general-purpose programming languages. It was created by Guido van Rossum, and released in 1991. It is an interpreted language that means the system needs not to compile in advance to execute code. Python is a widely-used programming language for website development, web application development, Big data Analytics, mathematical and statistical Analysis Like Machine Learning and Deep Learning.
R is a programming language and environment which is widely used for statistical Computing Data Analysis and Data Visualization. It is open-source software that is similar to the S Language. Using the R environment and it's open libraries, users can generate a wide variety of Statistical and graphics techniques like linear, nonlinear modeling, cluster analysis and much more. Which is extensible. R is capable to collect data in real-time, perform Statistical Data Analysis and Data Visualization. Because of its simplicity, R is the most popular programming language among Business Analysts and Data Science Professionals. Another advantage of using R for Statistical Data analysis is its rich packages repository having more than 9100 packages, which easy to download and use for every need of data analytics.
R is a programming language and environment which is widely used for statistical Computing Data Analysis and Data Visualization. It is open-source software that is similar to the S Language. Using the R environment and it's open libraries, users can generate a wide variety of Statistical and graphics techniques like linear, nonlinear modeling, cluster analysis and much more. Which is extensible. R is capable to collect data in real-time, perform Statistical Data Analysis and Data Visualization. Because of its simplicity, R is the most popular programming language among Business Analysts and Data Science Professionals. Another advantage of using R for Statistical Data analysis is its rich packages repository having more than 9100 packages, which easy to download and use for every need of data analytics.
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