Data science is a field that uses scientific methods, techniques, and systems to draw out knowledge and information from unorganized data. Today, a PhD dissertation writing service is going to tell you how to become a successful data scientist but before I begin let me give you an example. When you are bored at home and you feel like watching a TV show or movie then you get on Netflix, you scroll through all the TV shows, movies and series that are available and then you are recommended the latest season of deadwood because you saw something similar earlier.
There is no denying that we all enjoy watching shows on Netflix but do you even realize how much data science is used along with Netflix or YouTube. Because it analyzes user’s behavior from hundreds of TV shows and movies to make the best recommendations for you and everyone watching around the globe. They measure and analyze user’s engagement and retention on various shows. Currently, they are applying advance methods or techniques that include the pause, rewind and fast forward, that is when you see something cool and you want to watch it again. When you watch any content, some-days you feel like watching comedy shows and another day you want to watch crime thrillers and so on.
It also includes when and why you leave the content when you stop watching it, why did you stop? What time that was? What time did you watch which content? Because we know different age groups have different time patterns. For example, children will watch their favorite movies or cartoons after school, while some adults will watch it at night after work, some will watch after office hours in the evening. So it analyzes the searches, what time is it done and from which place or location it is being done and which device is being used. For a data scientist, it is very important to know this domain. The database is the collection of information that is organized. Let us talk about the skills you should possess to become a successful data scientist:
- Programing Language:
To be a successful data scientist you should at least know one programming language and know how to complete documentation of program. If you want to learn the programming languages, you need to make sure to do practice daily. We would take python at number one, and then java. But you should prefer “python” or “R” because they have a lot of libraries. With the help of those libraries, you can implement various machine learning algorithms.
- Machine Learning:
The next is machine learning. When we consider machine learning, there are various techniques like supervised or unsupervised machine learning. And in reinforcement machine learning techniques and many more so in that you’ll be having problems like classification problem, regression problem, reinforcement learning. As you know deep learning is a subset of machine learning, so we can mention deep learning under this, the same is the case with dimensionality reduction. Apart from this we also have a clustering algorithm. So most of your problem statements will revolve around this kind of scenario only. And you be using machine learning or deep learning for this purpose.
- IDE (Integrated Development Environment):
Then you need to learn some IDE (Integrated Development Environment) like what kind of editor you will be used for coding this Python and R programming languages. So for python, just for an example, Pycharm, and Jupiter. And you also have something, called a spider. And for R programming you have R studio or for Python VA studio where you can debug or write.
- Web Scrapping:
Web scraping is something that you may not need to know that much extensively. But obviously, there are some scenarios where you may need to use it to collect data. And for that, there are various libraries too. And you may also be using another called URL Lib this will help you in reading.
- Mathematics:
You should have basic knowledge of Mathematics. Specifically, you should know about statistics, Linear algebra, and differential calculus. Because most of the algorithms are based on these basic concepts. These five skills are considered basic for data science. But there are many other skills you should have which may include Data visualization. It includes tableau and Power BI, you have different libraries for that like Matplotlib or Seaborn. Last but not least is data analysis, which includes features of engineering and data wrangling.
