Data science is one of the fastest growing jobs of this century. A data scientist is somebody who is inquisitive, who can analyze the data and can find insights. A data scientist is fascinated by numbers, will work at developing new algorithms and spot the new patterns by looking at the data that otherwise would stay hidden. Descriptive analytics, Inquisitive analytics, Predictive and Prescriptive analytics are all part of data analysis which is worth knowing for data scientists.
Let’s dig into the importance of data: Aadhaar which is the world’s largest biometric database and the first online biometric-based identity system in the world. The large depository of data holds innumerable benefits, some of them are:
– Eliminating middlemen role, bureaucratic discretion to check corruption and delay in payments using DBT model.
– Eliminating fake beneficiaries and duplication, ushering targeted spending, thus saving public money wastage.
Now as we know that data is really important but equally important is data driven analysis which can help in getting meaningful insights about an organization for which the analysis is performed. There are multiple tools which a data scientist should be proficient in to get insights out of the data. Also, different companies and groups use data analysis tools that vary significantly. Listed below are some commonly used tools
Any aspiring data analyst needs to have a know-how of EXCEL. Learning Excel can be the first step towards getting into the world of data analysis. Excel has various functions, visualizations, arrays, etc. which help in generating quick insights from the data.
Some of the data analysis activities that we can perform using Excel are creating pivot tables, generating charts, cleaning data by removing duplicates, ‘what if’ analysis and basic statistics.
Whether you are new to Excel or a pro, Excel is an essential tool that would help you in finding meaningful conclusions based on data.
Python is a full-fledged programming language which can be used when data analysis tasks need to be integrated with web apps or if statistics code needs to be incorporated into a production database. Python supports libraries like NumPy /SciPy which help in scientific computing. Another library pandas is helpful in data manipulation. These libraries if installed makes Python usable for data analysis.
R is an open source programming language and software environment for statistical computing. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques, and is highly extensible. If you have some experience in data analysis and want to diversify your skillset, R would be a language that you must think to get an expertise on.
When there is a large amount of data it cannot be just stored in spreadsheets. It requires a database to be stored in. Most commonly used database is SQL database. SQL i.e. “Structured Query Language” is a language which is used for accessing, cleaning, and analyzing data that’s stored in databases. SQL has a variety of functions that allow its users to read, manipulate, and change data. It’s easy to learn and an important language to learn to get into data analysis as it is used by most of the organizations to fetch the results from the database.
Visualization tools like Tableau, Power BI
One of the best ways to get the idea of patterns and observations is to visualize data. By analyzing data visually it’s also possible to uncover insights that wouldn’t be apparent from looking at statistics alone.
There are lot of data visualization tools available that aid in visual representation of a data set series.
Tableau Public is one of the most popular visualization tool which supports a wide variety of charts, graphs, maps and another graphics. It is a completely free tool and the charts made with it can be easily embedded in any web page.
Power BI is another data visualization tool that helps to connect, import, shape, and transform data for business intelligence (BI)
Apart from all these tools there are innumerable tools which can be used for analyzing data. Some of the popular tools prevalent in the industry which you should try your hands on are Rapid Miner, Openrefine, Qlikview, Trifacta, Knime, Orange Wrapper, Google fusion, Talend. The tools which you would need to learn differs from organization to organization. It doesn’t mean you have to know all the tools to become a data scientist. However, you should be a pro in some of the tools and have a little bit know how of others.
Now that you’re familiar with the basics, it’s time to dive in and learn some data science skillsets. If you want to enhance your data analysis skills, you can enroll for the Data Science orientation course of Microsoft offered on Millionlights website. This online course would enable you to learn the basics of data analysis, data visualization, basic introduction to statistics, etc. from the team at Microsoft with a series of short, lecture-based videos, complete with discussions and hands-on labs.