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Home / Blog / Guide on How to Become a Data Scientist

Guide on How to Become a Data Scientist

21 Jan 2020

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How to become a data scientist

Guide on How to Become a Data Scientist

We are living in an era of big data. Everyone has connected to the world just a click ahead. We all are accessible, discoverable, connectable data goons. 10 years ago tracing someone’s location or searching for a lost phone was a thread and data science has changed it into a limitless luxury. Now, we can trace someone’s live location, can order food, can book our flights and even search for an online partner. This has become possible just because of the expeditious development in information technology so is of data science.

Let’s take an example;

A few years back Indian government was having a national population register on the name of data comprising all the information of all Indian citizens, its collection, processing, analysis and handling the datasheets was a tough task while now we are heading towards digital India. By enforcing the Aadhar card government has created such a big data set and was having almost all our information which has now limited under the right of privacy.

Just a 10 digit code of a person can now give all your details about your address, phone number, date of birth, bank details, driving license, electric and water connection bills, etc. to the government and it has become easier just because of data science.

Now from the government to private companies over colleges to multiplexes, all are having our contact details. Just by putting our mobile number anyone can access our details and even can track us. Whatever we are ordering online, eating at the restaurant, and traveling are being recorded by certain mobile applications. Each step is creating data. It would not be metaphoric if we say in the current era we are eating and roaming with data.

For making it simple I am going to divide my blog into some major parts.

  • Data and its type
  • Data science
  • The demand for a data handler
  • Required technical skills
  • Required non-technical skills
  • Ethical issues
  • Conclusion

1. Data and its type

Data is a plural word derived from a Latin word “Datum” which was first used in English during the 1640s. Data is everything; a fact, character, symbol, expression, equation, result, image, music or anything that can be used to derive any useful information. In short, data is the least abstract facts used to derive more abstract information. Statistically, data can be divided into categorical and numerical according to the kind eg. Nominal-ordinal and interval-ratio etc. In data science, we generally consider data as structured and unstructured.

 2. Data science

Data science is the most evolving branch of science and a blend of computational techniques. Earlier data science word was used for computer science while in 1974 a computer science engineer Peter Neur coined a new term datalogy for data science. Data science is a multidisciplinary science used to create conclusions by using certain algorithms or scientific technique to quote knowledge insights from raw data. Initially, data science was considered as a skill for a computer techie but now it has become a mandatory skill as all the industrial sectors are zeroing on data transformation. Data science is deploying the doors for all the sectors from aeronautics to deep-sea plate’s interventions, industrial processes to human DNA database, complex mathematical calculations to simple child games and animals to microorganisms, etc.

3. The demand for a data handler

In the era of data transformation where all the sectors start utilizing data as a strength the requirements of data analysts, specialists and scientists are increasing. Data analyst or specialist has become one of the most respected or sexiest jobs of the current decade. In the retro period when we were using small data sets for calculations and computations while at now we have stepped into the world of big data. Thus storage, handling, mining and analysis all have become crucial factors. All sorts of data mining and handling can-not be started on low data maturity point because we have lots of ethical issues so we require proper data tackling skills to develop a sustainable data-efficient environment for the cumulative benefits. According to a study, around 88% of the data analysts or scientists are at least a Master’s and 46% are having Ph.Ds.

It is an undeniable fact that world gas becomes a digital global web according to a study out of 7.7 billion population of the world around 4.48 billion people are using the internet. Another data says around 2.45 billion people are using Facebook and 2 billion people are using youtube. Such a large number of users generate a huge amount of data which requires a lot of advancements and skills for storage, handling, and analysis. Now we are producing data analysts on a higher rate but previously Mckinkey had predicted that by 2018 we will be facing a 50 % gap between their demand and supply.

 4. Required technical skills

An authentic and considerably skilled data handler or scientist must know all the processes ranging from data mining to interpretation. A data techie must know data cleansing, data analysis, data modeling, prototyping and most importantly data visualization. Data sciences demand some basic computer languages and programming for the ease of computing e.g. R programming, rDBMS analysis, SQL database handling, machine learning and artificial intelligence (A.I.), Hadoop platform, apache-spark, and most basic Python coding.

R programming is one of the basic programming languages which is easy to understand and work on. It is designed in such a way that we can use it to solve almost all kinds of statistical problems we generally encounter in data science. The relational database management system is a relational database model developed in a structured matrix which makes it easy to work as it defines the data into rows and columns which are known as entity and tuples. Many rDBMS systems offer the use of SQL for maintaining the database. SQL refers to the structured query language which is a domain-specific data management or retrieval language which is used describing and accessing the data in the relational database, allows the users to create, and drop databases and set permissions on their use and define, store, view and manipulate the data. Machine learning has become the world’s most influential technology which is still greek and Latin to a lot of data scientists. It is artificial intelligence (A.I.) tool that works on developing computer programs in such a way so that they can learn and work accordingly without human assistance and interventions. Machine learning is a faster and most accurate language which can carry a humongous amount of data. Cognitive approaches have proved that combining machine learning with artificial intelligence makes it more effective and efficient. Hadoop platform plays a different kind of role whenever we stuck with some low memory issue which generally occurs when we work on the large volume of data. In such cases, the Hadoop platform transfers the data to other computers or servers. Other than this it is also useful in exploration, filtration, sampling, and summarization of data. Apache spark is a unified open-source cluster computing framework which is the advanced version of the Hadoop platform because it works similarly to Hadoop but in a better way. Apache Spark is faster to Hadoop and caches its memory. Its major advantage is that it prevents data loss during processing so it is getting more attention in the big data industry. Python is the most common, interpreted and general-purpose programming language which is widely accepted due to its versatility. In python, we can solve almost all the problems we face in data science and besides it importing data from SQL into our code is also easy here.

All the above tools or skills are required for a data or a big data handler to work effectively in order to utilize the raw data provided.

5. Required non-technical skills

Computer science and data science techie are generally equipped with a lot of languages, tools, and statistical techniques but in contrast to all these technical skills, some major non-technical skill is also required which are intellectual and inquisitive demeanor, business insight, teamwork and communication skills.

Inquisitive demeanor is the intellectual property of instinct of a person which directly related to the curiosity and creativity of the same. For example, you are having a large set of data but you are not able to identify how to mine it for getting something new which solely depends upon your acumen and intellect. Business insight is also required because being a data analyst you must know the problems your company dealing with and that knowledge will draw your attention towards its solution not only to increase the business of the company but also to increase the understanding of the problem. Teamwork is one of the most common soft skills required in almost all kinds of jobs but in data sciences, it has become more specific because as a data scientist you can’t work alone. A data scientist needs to work with customers, server developers, product designers, and advertisement persons, etc. to analyze the demand, economy and brand value of the company. A data scientist is required to be connected to almost all the hierarchy from planning to customers for giving better facts. After collecting, cleaning, analyzing the data set one must interpret the results in an easily understandable way so that everyone can understand and grasp the knowledge he wants to impart. So, good communication skills are required for communicating the results in a better way.

6. Ethical issues

We are living in the digital global era and all of us are enjoying online shopping, online meal ordering, and online railway reservations, etc. but like all, it has increased the luxury and comfort on another hand it has also created a dash in the privacy and security somewhere. We are witnessing a large number of online frauds, hackings, and other misconducts that are occurring because fraudsters can easily access some of our data online or by the third party.

Conclusion

Data is the widest term and working on data for making more abstract information and mining the knowledge is its data. Extracting this information from raw data is the major work of data analysts or data scientists. A lot of languages, tools, and algorithms are being used by the data handlers so it requires several technical and non-technical skills. Data science has numerous advantages so it is gaining popularity at a rapid pace but also has some disadvantages too. Although as far as India is concern some of the disadvantages are nullified bypassing the right to privacy and covering aadhar with multiple layer security but still a lot is to be done. Data scientists are working on it hopefully will cope up with the flaws soon.

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