This story explains about what tools are used in the current market and gives you an idea on the steps to start and finish a analytics project.
I see many students very ambitious to pursue Data Science when they walk into the class. but they are not the same when they walk out.
The education is to share knowledge and elevate interest in students. Rather, education nowadays wants you to know everything and will put forth all the technologies and tools before you and want you to choose from the pile of tools.
But, the truth is most of the students are more confused after attending a Data Science class than before.
Some frequently asked questions by a Data Science student are as follows
- Is it mandatory to learn R or Python ?
- Can’t we pursue Data Science with out a programming language ?
- Should we learn all the tools like SAS, SPSS, SPARK, HADOOP…etc before we graduate to get a job ?
- How do I start my Analysis Project ?
If you are one of the people who’s facing the same problem and are confused like I was at one point, then this story is going to give your souls some air to breathe.😊
Well, I am not going to answer all the above questions individually, instead, I will explain the path to solving a Data Science Problem and It will clear all your doubts.
( How and What )data am I going to analyse ?
It is pretty often that we see people take up a project and give up. This kind of attitude towards any thing will ultimately lead you to no were.
So, From my personal experience, I want to say “Take up something close to you. I mean, always take up a task that drives you, and want to make you do it no matter what.”
The following are some steps that one must follow in the analytics field.
Now you know what to analyse and how to do it. But, you are in such a phase of the story, where you know how to start a war, how to lead it and How to end it, however, you are still unaware of How many soldiers you would need, what weapons to use.
I am just comparing this with a war hoping that you have watched a war-related movie or red a book at some point by now.
In Data Science | Machine Learning | Artificial Intelligence, the weapons are the tools and programming languages. The perfection in a tool or a programming language always depends upon the amount of practice.
No weapon is small or weak, It always depends on the person who wields it.
So, Choose a tool or programming language wisely and work on it, put your time and effort and it will pay you.
The following are the programming languages that are most widely used.
Both the above programming languages are used for Data analysis. “R” is especially used mostly for the statistics…etc and is purely made for analysis purposes. whereas Python is a language that is widely used not only for Data Analysis but also as a scripting language, coding algorithms, writing backend tasks and many more. All the features are acquired by adding some packages that give the Python language or R language some special abilities.
Although many use Python or R for Data operations, some tools are very widely used and are segregated according to the category below.
Data Collection and Ingestion Tools:
Data Cleaning and Mining Tools:
Data Analysis Tools:
Data Exploration and Visualization Tools:
The above are some of the tools that are widely used in the market currently. There are more tools like SAS, SPSS, MiniTab, git…etc
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Ultimately, It comes to one question. What tools to learn? The answer is simple and I have already mentioned it. It’s not about what, It’s about who. You can perform all the tasks by programming and the above tools make it simpler for the people with the non-programming background. #spreadknowledge
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