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How to Become a Data Scientist

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Are you interested to know how to become a data scientist? Do you wish to pursue a career in data science? Read on to find more about data science and its impact in today’s modern world.

What is Data Science?

You might have surely heard about data science and its fascinating job opportunities which compels people to know how to become a data scientist as science and technology continues to change and evolve in today’s era. 

Many upcoming scientists, analysts, engineers, researchers, freelancers and so on have chosen data science as their career profession or as their passion because of its extensive contribution to technology and high pay scales. 

With so many opportunities and scopes that it offers, it’s essential to know what exactly is data science and how it is helping in this competitive world. 

Data science is the scientific processes, techniques, algorithms, systems and applications that collect information or data from various structured and unstructured data. 

Lastly, the data collected is analysed and used in several domains based on its required actions and conclusions. 

It is famously known as a multidisciplinary or interdisciplinary approach. 

As data processing is the core value of data science, it comprises cleansing, aggregating, and manipulating during data preparation. 

Data mining, machine learning and big data are all closely associated with data science as it is full of mathematics and statistics, specialized programming, artificial intelligence, and advanced analytics. 

Data science is even used heavily in business intelligence for accurate insights through storytelling. 

For the execution of accurate results, the data is analysed through a series of patterns which then becomes prediction. 

The predictions are further tested through scientifically designed tests and experiments for accuracy. 

Once the predictions turn out to be accurate, they are shared with other users through the means of data visualization so that they can understand and analyse the patterns. 

All the above tasks are performed by data scientists. 

With that being said, here comes the most important point to understand; what is a data scientist? 

To be specific, a data scientist’s typical job is to extract and analyse data for actionable insights. 

As their job is to collect a large amount of both structured and unstructured data, they are often also called big data wranglers. 

As you are interested in knowing how to become a data scientist, it is essential that you have industry knowledge, contextual understanding as well as scepticism of existing assumptions. 

As we go further in this article, you will learn about the application of data science in the real world, the requirements to be a data scientist, its pros and cons, and so much more. 

Data Science in the real world?

We can see the application of data science in our daily lives. From using mobile phones, laptops, etc to other scientific and technical prospects, data science has become an integral part of our lives. 

Some of the applications of data science in the real world are email spam filtering, auto-complete or auto-detection, autocorrect, virtual assistant, face lock, chatbots, and many more. 

All of these applications are developed with the help of data science, artificial intelligence, machine learning, programming languages like Python, R or JavaScript. 

The reason data science is heavily used in almost all the essential aspects of our lives is that data science helps in collecting and analyzing millions of bytes of the given data within seconds.

Educational Requirements to Become a Data Scientist:

In order to be a data scientist, one must have a bachelor’s degree in data science or any technological field. 

However, to climb above the post of entry-level data scientist, it is required to complete your masters. 

Those data scientists who have already completed their masters and are enjoying their data science career path have highly experienced the impact of a master’s degree as it provides and boosts structure, internships, networking and recognized academic qualifications. 

Apart from this, one shortcut to earn a legitimate data science degree is doing online courses from recognized digital educational platforms like Udacity, Edureka, Data Camp, Coursera, Udemy and many more. 

Skills required to become a Data Scientist?

Since this article focuses on how to become a data scientist, it is essential to know some technical skills to pursue this course. 

In order to become a data scientist, one must need to have a strong background in statistics and mathematics. 

Relevant experience in practical projects and portfolios are essential for gaining insights. 

Apart from these basics, there are some other major requirements in order to be a data scientist like knowing programming languages such as Python and R. 

The reason why knowledge of Python is important is that, with the help of Python, you can do data mining, website construction,  running embedded systems in one go. 

Along with this, Python has a library named pandas which helps in reliable data manipulation, reading, aggregation, and visualization. 

Similarly, despite its complexity compared to Python, knowledge of R is important because it helps in applications like data manipulation, calculation, and graphical display. 

R is also reliable in doing graphical and statistical analyses like linear and non-linear modelling, classical statistical tests, time-series analysis, classification, and clustering.

A business strategy, data visualization, artificial intelligence, machine learning etc are some of the must-have skills because as a data scientist, applications involving ggplot, d3.js, Tableau, supervised vs. unsupervised machine learning, decision trees, logistic regression, etc are highly used in daily lives. 

Apart from technical knowledge, secondary requisite skills are good communication, collaboration, etc which help a lot in business dealings. 

Best Data Science projects for freshers while learning:

In order to establish a successful data science career, it is very important that the company you desire to apply and work for gets impressed by your resume or CV. 

For this purpose, you need to create some useful data science projects that are relevant and appealing to the employees in order to get selected. 

As so many freshers or newcomers are gearing up to know how to become a data scientist and pursue a career in it, there are several real-world data science projects. 

Some of these data science projects are listed below – 

  • Sound classiAnalysis
  • Interest Level in Rental Properties
  • Keyword generation for Google Ads
  • Breast Cancer Classification
  • Employee Computer Access Needs
  • Tweets Classification
  • Sentiment Analysis
  • Detection of the Parkinson Disease
  • Detection of Fake News
  • Prediction Of Next Word
  • Movie Recommender 
  • Customer Segmentation
  • Credit Card Fraud Detection
  • Sign Language Recognition
  • Class of Flower Prediction
  • Colour Detection
  • Loan Prediction
  • Road Traffic Prediction
  • Income Classification
  • Speech Emotion Recognition
  • Celebrity Voice Prediction
  • Store Sales Prediction
  • Chatbot

Which company is best for data scientists?

As this article focuses on how to become a data scientist, you might also be interested in knowing the companies that give the best opportunities for data scientists. 

These companies not only pay a hefty pay scale but also provide an excellent environment for employees to show their skills and knowledge to the best of their abilities. 

They provide various facilities like commuter assistance, stock options, referral program, gym membership, adoption assistance, health savings account, flexible schedule, parental leave, employee stock purchase plan, tuition reimbursement, food provided, etc. 

The average salary for a Data Scientist is $74982 per year in the United States.

Some of the top companies that are best for data scientists are – 

  • IBM 
  • MuSigma
  • Fractal Analytics
  • Bridgei2i Analytics
  • Tiger Analytics
  • LatentView
  • Absolutdata
  • Innovaccer
  • TEG Analytics
  • Teradata
  • Impact Analytics
  • Wipro 
  • Splunk

Who can study Data Science?

Data science can be learned by anyone who is genuinely interested in learning data science. 

Engineers, Marketing Professionals, Software, and IT professionals can learn data science through online platforms like Udemy, Udacity and many more. 

Even people who don’t have a science background can learn data science if they fulfil the prerequisites of having knowledge of programming languages, mathematics, statistics, probability, and so on. 

All these can also be learned with the help of online learning platforms too. 

Roadmap to learn Data Science:

Roadmap to learn data science involves measures like what, how and ways to hone and determine the skills that are essential. 

Thus, a roadmap in data science makes the process of learning data science smooth and convenient in a systematic manner. 

Some of the most common elements associated with the roadmap to learn data science is having knowledge of mathematics, probability, SQL database, statistics, any programming languages that are relevant in today’s era, machine learning, deep learning, NLP and so on. 

All of these elements are necessary for learning data science as it provides an in-depth analysis of the tools and projects you develop using data science. 

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