To understand what all does data science encompass, we’ll have to dive a bit deeper from the surface. First of all, let’s keep some of the facts clear that Data Science is not a part of academia nor it is mostly statistics/analytics. The major part of being a data scientist is to extract information from the data, analyze it, and if a problem arises, you build a tool accordingly and solve it. It mainly includes a little programming skill, some statistical readiness, some visualisation techniques and of course a major sense of how a business works.
It is a kind of investigation in which the Data Scientist peeks into the data transmitted in the past, analyses it and then comes up with a tool/model to rid of the problem permanently.
So, here are a few things you have to do if you are thinking to enter the world of data, statistics, and information:
Data cleaning: Typically when you start gathering data and extract all the information you can get, there can be a huge amount of data that is not usable at all. It means, even if you keep all of the correct information with you, it creates a lot of mess. This is the part where you have to sort all the information in such a manner that the resulting combination makes sense. Fixing them and making sure that the problem will be fixed automatically in the future is what the Data Scientist usually does.
Data Analysis: This is not the part where you prepare tables or charts to analyze the situation or the problem. This is where visualization takes place. This is the creative part where you get a chance to show your abilities to detect why the particular problem is arising or how can you prepare a model to solve it universally. Since Data Science is one of the most discussed topics nowadays, people are always trying to find newer solutions to business issues. You will be playing a major role in making decisions more scientific and helping business achieve effective operation.
Modeling: After you have extracted and analyzed all the data, now is the time to evaluate and tweak models. However, there are a lot of tools introduced in the market to solve regular issues faced by business organizations. Still, there is a huge possibility of a problem to arise that only a Data Scientist can foresee. We can say that this step is one of the most complicated one until now but here is the point where Data Scientists can go back to all the data they have put together and work on new features to create an out of the box model. Besides having powerful algorithms to solve any issue, there are times when nothing works. Only a Data Scientist can keep working on it to find out new solutions.
Working with Data Science
Working with Data Science might sound really interesting, but there are certain qualities that you should possess to work in this industry. You should have proper knowledge of statistics and mathematics including various databases and the use of different tools accordingly. A proper skill set of data munging, data cleansing and data transformation is needed. Finally, visualization comes through which you definitely need to have to be a good Data Scientist.
Below is the list of a few Data Science tools you must know from the very beginning:
R Programming: R is a statistical programming language that comes with an array of features specific to data scientists. It is one of the strongest and most prominent languages when it comes to data analytics and machine learning.
SQL: SQL, short for structured query language, is a structured programming for working with relational database management systems. SQL follows a certain format of rows and columns that depict a huge amount of data. While many of the operations are shifting to the NoSQL databases, SQL still manages to be one of the widely used tools for data manipulation and interpretation. SQL is extensively used by database administrators and developers alike.
Python: Python is an object-oriented programming language that is extremely high-level and versatile. Its use cases include a variety of applications, especially in the domain of machine learning and data science. Python comes with a huge set of readymade libraries which makes it a promising choice for data scientists.
Hadoop: Hadoop is used to process huge amounts of data and is one of the most powerful tools for that. Being open-sourced, Hadoop has an extremely vast and active community of developers. It helps you store, computer, deploy real-time analytics among things on big data through its ecosystem of tools.
SAS: SAS is one of the most powerful business analytics and intelligence tools. It is a software suite useful for extracting, analyzing, and reporting data to derive valuable business insights from it. SAS includes a whole set of tools required for working across different steps in converting raw data into actionable insights.
Tableau: Tableau is by far the most powerful data visualization tool. With analytical and reporting capabilities, Tableau is for you even if you don’t have a lot of technical knowledge.
Those were the basics of everything that comes under the umbrella of Data Science — including most of the important tools used in the industry. Also, did you know that Data Science is branded as the sexiest job of the 21st century? Yup.
So, if you feel data science is your calling and all you’re lacking is good supervision, let’s tell you — we’ve got you covered. Come over to Coding Ninjas where our online course on Data Science takes care of everything you need in order to start on the correct track!
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