If you are looking for a High Paying Quality job profile in IT Industry. Data Scientist comes up on your wish list. When I started searching for How to become a Data Scientist. I got so many informative articles over the internet. These articles were full of information but quite massive and distributed.
So, In this article, I have mentioned the best way to learn data science in a very compact and serial way. If you follow all these tips, You will be a Data scientist in very fewer efforts. You can showcase your data science skills using various data science course with certification programs. Actually, Data science is a combination of so many interrelated fields like Machine Learning, Data visualization, and Programming. It also includes Mathematics and Statistics principles. This combination and its complexity create massiveness and confusion in the reader’s mind.
There are so many varieties of sub-skills under Data Science like Programming language (python vs r), Machine learning algorithms (Supervised Machine Learning Vs Unsupervised Machine learning). Which skill should get higher priority for learning is the major pain area for data science learners. If you are experiencing the same, this is the right place for solving all your problems. We have created a straight road map to assist you in your confusion of How to become a data scientist.
How to become a data scientist – Complete Guide
As per the data scientist job description available across the industry, we can divide the skill set into five major classes. I have arranged these five skills in the order of priority. We have also created a road map in the form of infographics for your better understanding. These five steps are the best way to learn data science.
1. Brush up your Basic Mathematics and Statistics for Data Science –
Probability and statistics are fundamental tools required for every predictive analytics. It is badly used in every corner of data science. Especially Machine Learning Data Mining is the field where you cannot take any step without having a dirty hand in Mathematics and Statistics.
You may find a Free eBook on statistics for data science by just clicking the below link. Probability and statistics interview question for data science is must cover the topic in this section as well for better performance in your job interview. Every Data Scientist’s job description contains a separate column of Probability understanding as a required skill. You can read the article on the best ways to learn Probability for the data scientist. It covers the three phases to learn probability for data science.
2. Learn r Programming | python for data science-
You have to opt for one programming language in every data science project. The possible combination for learning are –
- Learn r Programming
- python for data science
- java for data science.
This is the second step in the series of the best ways to learn data science. If you are doing any data science project, you need data. Data Analysts can use or produce data by external file sources like excel or you have to fetch via some API call using some programming language. Finally, You have to use at least any programming language to accomplish these tasks.I will recommend you to refer our article Why Python for Data Analysis . This article is focusing over python but after reading it you can relate it with other programming language.
If you want to make your hand dirty with python and you are looking for
a short overview type article , Python essentials in 5 minutes will be the best article for you .
3. Learn Applied Machine Learning Algorithms for Data Science
Machine Learning algorithms and Trained tool are essential for data science. There are so many tools available where you can train your machine learning model. This Model you be integrated in your existing Data science project. Data science project. Lets understand it with a example , Suppose we have to create a price prediction algorithms for any financial firm and we have 10 year past data only. We will build the model using some market logic for the prediction of next year . If we some how able to make automatic feed back system in our existing system to add the current real outcome as experience .So next time we will have 11 year training data . In the same way as the time passes our system will be more precise in predictive analytics . This approach is called machine learning where machine start learning it self with its past experiences .
I will suggest you to take over view of Machine Learning Library . This will improve your understanding .
4. Learn Data visualization Tool for Data Science
Data Scientist mine the data and extract some meaningful result out of it .These result could be any pattern , any indicator or something else . To understand the hidden information out of the huge raw data , You have to use some data visualization tool. In fact , We have so many data visualization toll available all around us . Companies from different industries are using these tools very frequent . Some of them are very popular and frequent like-
- Qlik Sense and QlikView
- D3.js
- tableau
If you want to learn more on data visualization libraries over the tools. There are couple of options –
- Matplotlib
- Seaborn
- Dash
- Plotly
5. Learn Big Data Technologies for Data science-
this comes last but quite effective.Specially If you want to become full stack data scientist . There are so many big data tool and technologies . Hadoop is open source framework for Big data . Spark with java and Scala is also quite frequent use framework . There is a complete list of required Big Data Tool in Data science .For Beginner , I will suggest to learn Hadoop first .
Finally , If you learn all these technologies , You can start your career as a Data Scientist .I mean these all skill are essentials for a Data Scientist . Along with this , If you are dealing with text analytics You may use Natural Language Processing . Natural language Processing is NLP in short . NLP as a short and trendy word in field of technology. All big and innovative companies are working on NLP . Facebook and Google also come in these list .
Lets Zoom in Machine Learning Data Mining . Machine Leaning is itself a branch of Artificial Intelligence .Programmers and application Designer are using machine learning data mining , Data science , AI in their existing Application .This Integration are migration their Technology into new era.There are so many tools like Amazon Machine Leaning , Azure ML Studio , Apache singa are in trend.
Anyways , Lets conclude all. Data Scientist is some one who is good at Maths , Programming and Analytics .These three are major branches in their itself . Their combination creates a meaningful data . Unstructured data is majorly available around us . Most Of the time we create a unstructured data unknowingly.For Example the video of our activity is a unstructured data in itself.To handle this a major pain area in field of Data Science . So If you learn unstructured data technologies with data science , You are future ready product.
End notes
I think , We have have done enough discussion over the topic How to become a Data Scientist . If you want to explore more on Machine Learning , You can refer our article What is Machine Learning ? .
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