Machine learning and data science are the hottest trends in the market right now. To keep up with the demands of the technologically advanced generation, most of the businesses are relying on automation of their services to provide quicker and more efficient service to consumers. So, the demand for artificial intelligence and machine learning is increasing exponentially. However, the development of these technologies is not as easy, as they might seem.
What is Data Science?
Data science is nothing but simply an amalgamation of various tools and methods applied in a scientific way to procure meaningful information from raw and unprocessed data. Data analysis is different from normal statistical analysis in the way that it does not just provide us with the current condition of the company, but also provides us the necessary information to predict the future trends and conditions of the business.
Another term that has been enjoying a lot of attention and is often confused with Data Science is the term ‘Business Intelligence‘ or commonly known as BI. There’s a subtle difference between the approach of both methods. BI is generally used to tabulate the data from internal and external sources and compile the results which indicate various questions like the annual revenue earned or even forecast the impact of any future actions on the business profile.
On the other hand, data science is used to create an efficient business strategy for the future by predicting future results and exploiting the numerous consumer patterns obtained by careful data analysis. Apart from data analysis, data science also helps us in stimulating models with various constraints to help us to visualize the most economical way to tackle any problem which may arise in the future.
How to Become a Data Scientist?
Data science course is very attractive right now for candidates who want to do something different and be successful in their careers while doing so. Major firms and enterprises are dishing out huge and attractive salary packages to data scientists. However, it’s not easy at all to be a successful data scientist. There are numerous skills one must master in order to become a successful data scientist and many things to prepare for such as a data science skills assessment or data engineering skills assessment.
Listed below are some skills which will give you a pretty clear idea of what it takes to become a successful data scientist.
1. R Programming
One of the basic and major requirements for candidates aspiring to be a data scientist is a complete knowledge of data analytical tools. There are many tools available for analytic purposes, but ‘R’ is the most important of them. R programming is specifically designed for data analytics course purposes and is widely used by most of the large-scale companies.
The only problem with this is that, one has to give a lot of time and attention to learn R in order to master it. However, thanks to the internet, there are many online courses available for R programming. It should be kept in mind that R is a very difficult language to learn even if you are already mastered in some other programming language but there is nothing a little practice can’t solve.
2. Proper Education
Data science is a very complex matter and requires a certain depth of knowledge to understand it properly. Most of the established and professional data scientists are highly qualified individuals who have at least a Master’s degree or even a Ph.D. So, the right background education and knowledge are necessary to become a decent data scientist.
One should have a Bachelor’s degree in any one of the following subjects: Mathematics, Statistics, Computer Science or Engineering. Out of these, Maths and statistics are the most preferred streams as they provide a basic understanding of how to handle big data. After becoming a graduate one should pursue a master’s degree or a Ph.D. in data science to become efficient at the job. Besides learning, the correct resource materials should be referred for enhancing the knowledge.
3. Hadoop platform
When it comes to data analytics, knowledge of any analytic platform is good. Not necessarily Hadoop, however, it is one of the most popularly used tools for data analytics purposes and is widely preferred by various enterprises. When you are working as a data scientist, certain situations might arise where the amount of data that needs to be processed might exceed the provided system memory.
In such situation, Hadoop comes in handy as it helps in conveying the data to various points. Apart from that, Hadoop can also be used for data filtration, sampling, and exploration. Such use makes this platform very popular among data scientists.
4. Python coding
This is a very common knowledge among coders that python is the most commonly used programming languages along with C++ and Java. A data scientist should have a decent knowledge of coding in python since it is very helpful when it comes to data analysis.
Python is the most versatile programming language available for coders which enables them to actually create datasets. It is also helpful for completing almost every step involved in the data science process and is very helpful when it comes to incorporating SQL tables into the program code. That’s why more than 40% of data scientists are comfortable and prefer using Python over any other programming language. A coding bootcamp can help you in learning how to harness data for betting decision-making.
5. SQL Database/Coding
Ever since the birth of Hadoop, it has become a major part of data science. However, it is still necessary that a candidate aspiring to be a data scientist should have a sound knowledge of SQL coding and programming. SQL allows the user to perform mathematical operations on a database. It is also useful for modifying the structure of a database which helps in performing various analyses.
A data scientist should be fluent in the usage of SQL coding in order to reduce coding time and gain some insight to identify various patterns in the datasets. It helps a data scientist to effectively communicate and work on the data.
6. Machine Learning and AI
Most of the data scientists are not very good when it comes to the area of machine learning, AI, and other related concepts. However, a decent knowledge of machine learning concepts is very helpful for solving various problems related to data science and even helps largely in the predictive analysis by forecasting future outcomes from the collected data.
So, if you want to stand apart in a huge crowd of candidates aspiring to be a data scientist, a working knowledge of machine learning and AI systems concepts can give you that extra needed edge to be distinct and more efficient. Artificial intelligence for customer experience has the potential to empower customers. Since data science often deals with huge datasets, machine learning comes in very handy as it can be used to create models that can process the data and provide the desired results. So, having knowledge of machine learning is very helpful for becoming a data scientist.
7. Apache Spark technology
Apache spark is the new rising platform that is widely being adopted by various firms and enterprises. It is very similar to Hadoop, but the only reason it is fast gaining popularity over Hadoop is the fact that Apache spark works way faster than Hadoop. The main reason behind this is that Hadoop reads and writes the necessary information on the disk which takes a longer time as compared to Apache spark.
Apache spark mainly performs its work by storing its computations in the memory which significantly reduces its process time. The biggest advantage of Apache spark is that, it is very easy to use, can be also used on unstructured data, and also prevents the loss of big data modules.
8. Unstructured Data Operation
The biggest challenge faced by any data scientist is the processing of unstructured data. This is so difficult that often it is referred to as ‘dark analytics’ just due to the sheer complexity of it. Unstructured data is mainly used to refer to different types of data which does not fit into a database and needs special treatment for processing.
Analysis of unstructured data is very helpful in obtaining some decision-making processes. A professional data scientist should be proficient in handling and manipulating unstructured data from any kind of platform. Analysis of unstructured data is one of the most complex challenges for a data scientist. Being efficient in handling that challenge will surely give your career a major boost as a data scientist.
9. Visualization of Data
Data visualization is one of the skills which is very much necessary for the effective representation of the results obtained by data analysis. It simply refers to transforming the results in a format that can be very easily comprehended visually by the people who do not understand programming terms. Data visualization is very important for gaining valuable insights into the obtained results and determining very major decisions that largely affect the business strategy of the company.
So, from the above-listed skills, it is pretty clear what it takes to make a professional data scientist. It involves a lot of hard work and the prospect of doing it might seem scary, but the career opportunities in this field are rising exponentially right now. So, don’t be afraid of a little hard work. Instead, see it as a stepping stone to success.
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- What Does It Take To Become A Data Scientist? - January 10, 2022
Originally posted 2019-12-22 10:45:47. Republished by Blog Post Promoter
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