Data science refers to the process of uncovering patterns and insights hidden in huge volumes of messy data using techniques such as machine learning, data mining, predictive analytics, deep learning, and cognitive computing, among others. Unlike traditional business intelligence and related approaches, data science isn’t confined to structured data, doesn’t require data to be organized into neat rows and tables, and isn’t limited to small data sets. Rather, data science techniques can be applied at scale to massive volumes of semi-structured and unstructured data such as text-based data, machine data, sensor data, and social media data. Thanks to this less restrictive approach to analysing data, data science allows organizations to find answers to questions they didn’t even know to ask, leading to potentially breakthrough insights that drive competitive advantage.
“Data science doesn’t just provide insights. By applying the right models to the right data, data science lets companies identify patterns in massive volumes of data to predict and, ultimately, affect business outcomes. Put another way, data science gives companies the foresight needed to better serve customers, develop more compelling products, and drive operational efficiencies.”
R Programming is an integrated approach to the modern data science techniques. This statistical software is enough to cater to the growing data needs and fulfill the requirements of businesses. R has been in the market for a long time but the reason for its growing popularity is because it is an open-sourced software. There is no deniability that SAS has ruled the market for a long time but today in 2017, the current scenario favors R in comparison to SAS.
The biggest advantage of R over Excel is that R can really handle large datasets which excel cannot handle and owing to the big library of packages, the data analysis or applying various statistical tools becomes a seamless process. Thus, Banking, insurance, retail, Consulting groups, KPOs have started to rely on R and have started trusting this tool for their business analysis.
Below is an image from www.indeed.com/jobtrendsthat is doing a simple comparison between R and the rest of the tools such as Python, SAS and Machine Learning& this can be clearly seen that R is the crowd-puller. Hence, as a part of Initiative, Sankhyiki is training its actuarial students on Excel, VBA, SQL and R Programming.
The Big Question that comes into the mind of lots of people is – “Why R?” Here is the answer to this question. Most of the companies do prefer R because it is an open source software and is able to cater to the needs of Data Science people as well as Actuarial professionals. R has variety of packages that help perform no of tasks swiftly. Lately, R has come up with actuarial packages which makes the analysis and number crunching effective and swift.
Sounds Familiar, we at Sankhyiki have tailored the learning as per the needs of people coming from diversified walks of life. The USP of this program is not only learning the tools but develop a deeper understanding of data, performing Exploratory Data Analysis, Mining,Extracting Insights including forecasting.
Through this course, we make rigorous efforts to prepare the person industry ready (Data Scientist) and once hired by anyanalytics company, he/she can start working from the day 01.
Python is a general-use high-level programming language that bills itself as powerful, fast, friendly, open, and easy to learn. Python "plays well with others" and "runs everywhere".
Conceived in the late 1980s, Python didn't make inroads into data science until recently. For a long time, as Tal Yarkoni of UT Austin says, "you couldn't really do statistics in Python unless you wanted to spend most of your time pulling your hair out."
Now, however, tools for almost every aspect of scientific computing are readily available in Python. (Thanks in part, no doubt, to the $3 million the Defense Advanced Research Projects Agency (DARPA) put toward the development of data analytics and data processing libraries for Python in late 2012.)
Bank of America uses Python to crunch financial data. Facebook turns to the Python Library Pandas for its data analysis because it sees the benefit of using one programming language across multiple applications.
"One of the reasons we like to use Pandas is because we like to stay in the Python ecosystem," Burc Arpat, a quantitative engineering manager at Facebook, told Fast Company in May 2014.