A Smarter Way to Learn “Data Science”-The Ultimate Guide for Beginners
A Smarter Way to Learn “Data Science”-The Ultimate Guide for Beginners
Whether you’re new or just need to brush up your skills in data science, we’ll help you know everything about Data Science in detail.
Before stepping into Data Science concept, you must be aware of the several things that will help you add value to your business.
Points you need to consider
Programming Language
So, before you knock up Data Science, Programming Language must be a foremost factor you should take into account. Here, we prefer 3 Programming Languages such as:
- Python
- R
- JAVA
In the new era of technological advancements, Python and R have been considered to be the topmost languages as they include lot of liabraries and with the help of those libraries one can implement several machine learning algorithms.
Machine Learning
While considering Machine Learning, various techniques coming into existence are defined as Supervised, Unsupervised, Reinforcement and much more. Further if you move on to machine learning, you will be facing several problems such as:
- Classification Problems
- Regression Problems
- Reinforcemental Learning
- Deep Learning
- Dimensionality Reduction
Apart from these, Clustering Algorithm is also included in Machine Learning process. All your problems basically revolves around these kind of scenarios discussed above.
Next we move on to TOOLS,
IDE (Integrated Development Environment) is a basic tool used for coding the Python and R Programming languages. There’s another tool called Pycharm in Python Programming Language, Jupiter and Spider also comes in the same. So, if you gear up with Python, make sure you go with one of these tools of IDE and if you’re going with R Programming language, know what’s included in R studio and how to work with it further.
Web Scrapping
The tool is generally used to collect data and for that various libraries are considered such as
- Beautiful Soup
- Scrapy
- URLLIB
All these tools discussed will sort all your problems and help you read data apart in the form of Jason. Basic knowledge of Web Scarpping will surely lead you to grab much information.
What is the Role of Maths in Data Science?
The field keep the hold of the concepts drawn from Mathematics as the results achieved from specific processes might be useful for the following problem such as forecasting company profits. In data Science, the understanding of various notions of Statistics and Probability Theory are said to be the key for implementing such algorithms. When we talk about Maths here, the whole slot of algorithms basically revolves around Statistics, Linear Alg, and Differential Calculus.
Data Visualization
Data Visualisation basically includes
- Tableau
- Power BI
All these tools differ from each other and help you do different visualization stuff. Apart from this, you have Python and R having different libraries that might plot bond which let you do a lot of visualisation with respect to your code that you’re basically writing.
Data Analysis
The most important stage helps in doing
- Feature Engineering
- Data Wrangling
- Exploratory Data Analysis
Another scenario comes Deployment!
In this particular deployment, you can use different tools such as
- AWS
- Azure
Now, all the components that have been discussed above, you need to follow atleast one-one thing from these. If we talk about Programming Language, you must be aware of all the algorithm so, learn, grab and understand the maths behind them. You can start it by solving them, implementing them. However, based on Data Scientist, you will be using different techniques. So, before getting into machine learning algorithm, we might use particular libraries stating Tableu, Power BI and many more.
While implementing, you need not to study all these concepts separately, gear up or give a start with some used case, for example, you can take a house prediction. What will you require for that will be- data! So, get started with used case, apply the used case and try understand what that case is all about. Further, applying to all these techniques, you can even start with Data Visualisation Technique or Data Analysis Technique. With this you can come on to the Machine Learning process, where you can detect various problems, such as regression problem, deep learning or classification problem. Then make a move towards IDE (Integrated Development Environment) where you will be applying different codes.
Summing Up
Even though you are working on some different domain name, if you give an idea that you can solve this particular problem with the help of machine learning that will be a very great use for the company people. May be your manager or Team Leader appreciate you for the same as you are trying your best to solve the particular problem in an effective manner through the machine learning technique itself. What you basically need to do is, try to do a reverse engineering, follow the part discussed above and analyze results accordingly by implementing these steps. However, you need to study, work and implement those specific tools, algorithms and solve problems in order to define your work in no time and proceed further with the flow.