How can I Become Data Scientist?

There was a time when 5 Exabytes of data were created during the end of civilization in 2003, but now, a similar amount of data is created every two days. If you stepped yourself in reading this blog then you must be aware of the fact given by Harvard Business Review that Data Scientist is the Sexiest Job of the 21st century. Truly, Data Scientists are the angels of the 21st century. It played its role in sectors where even medical science was not able to find solutions. 

Before proceeding, you must be aware of what Data Scientist is and what does a Data Scientist do? A Data Scientist is the one who is responsible for designing new processes and algorithms for data modeling, structuring predictive analysis on the data as per the requirement of the company. The major difference between a Data Analyst and a Data Scientist is that Data Scientist use hard-core coding to design data model processes instead of using the existing ones to get the answers from data that is a Data Analyst.

“The reason behind the high pay and high demand of a Data Scientist is because of the high level of skills but a low supply of Data Scientists in the industry.”

What is the educational requirement to become a Data Scientist?

There are numerous paths to be followed to achieve the role of a Data Scientist. But point to be noted here is most of the career paths of Data Science pass through a four-year bachelor degree program which is the minimum requirement. Going further for a Masters and Ph.D. will be a plus. A bachelor’s degree in Data Science is the direct gateway to become a data scientist because the skills like data collection, data analysis and interpreting a large amount of data can be learnt during your degree only.

The other career path to become a data scientist is to get a technical degree, for example, a degree in Computer Science, Statistics, Mathematics, Economics, etc. After the completion of your degree, you can earn various skills like coding, data handling, problem-solving, analytics, etc. Based upon this, you will be able to get an entry-level job in Data Science and later complete your specialization in Data Science.

What skills are required to become a Data Scientist?

  • Programming Languages

The skill of coding is mandatory for a data scientist because being a programmer or coder, it becomes easy to understand and study data to draw a useful conclusion by using various algorithms as per the requirement. Generally, the following three programming languages are working as a boon in the field of data science and you are required to become a magician in these languages. Let us take a glance over all three programming languages and their use in data science.


  • Python

          The use of Python in data science is very relevant because of its easy readability and capability for statistical analysis. Python is loaded with many different packages for machine learning, data analytics, deep learning, and data visualization that make it best             suitable for data science.

 

  • R Programming

If you want to utilize R programming for data science, then it will also be a good decision. R is utilized to solve almost any problem in data science with the help of packages, let’s say e1071, rpart, etc.

 

  • Java

In the race of Machine Learning, Java is not far behind because it is one of the oldest, reliable, and secure programming languages. To implement Java in Machine Learning, you have many libraries to collaborate. Some of the important ones are:

      • ADAMS (Advanced Data Mining and Machine Learning Systems)
      • JavaML
      • Apache Mahaut
      • DeepLearning4j
      • WEKA

So generally, Python and R programming are the commonly used languages for data science. 

 

  • IDE

Integrated Development Environment (IDE) consists of a compiler, interpreter, and debugger that can be accessed making use of GUIs. Following is the list of commonly used IDE for Data Science.

  • PyCharm

It is a Python IDE using which we can create creative code with the help of classes, objects, and keywords, auto-indentation, code formatting, customizable codes, etc.

 

  • JuPyter

JuPyter or IPython is an open-source IDE that supports multiple languages like Python, R, Julia, Scala, etc. It also supports the integration of Big Data tools like Apache Spark. Using this, we can use libraries like Pandas, scikit-learn, ggplot2, dplyr, etc.

 

  • Google Colaboratory

It is basically a JuPyter notebook IDE that requires no setup to use and it runs entirely on the cloud. As this is the research project of Google, Google is used before Colaboratory.

 

  • Spyder

Spyder is the abbreviation for Scientific Python Development Environment. It is also an open-source IDE that integrates NumPY, Scipy, Matplotlib, and other scientific libraries.

 

  • R-Studio

It is an R programming language based IDE that includes a console that has direct code execution function.

 

  • Web Scraping

Web scraping is a technique of mechanizing the data extraction in a fast and quick manner. Web scraping in data science is used to extract data from any website (no matter how large the data is!) on your computer.

  • Beautiful Soup

Beautiful Soup is a Python library that provides Python idioms for searching, navigating, and modifying a parse tree. This library is used to perform web scraping in Python 3.

 

  • Scrapy

It is another framework of Python that is used for web scraping on a large scale. It provides various tools using which web scraping can be done in the structure and format you required.

 

  • URLLIB

It is a package of Python that is used           to open URLs. With the collection of several modules such as urllib.request (for opening and reading URLs), urllib.error (module that defines the exception classes), urllib.parse (module to define a standard interface to break URL), urllib.robotparser and so on… we can fetch a URL on the website that published robots.txt file.

 

  • Data Analysis

Data analysis in data science is a process of inspecting, cleansing, transforming, and modeling data with the objective of finding useful information including conclusion and the support of decision-making.

  • Feature Engineering

Feature engineering in data science refers to building features for each label whilst filtering data. This is used in data mining techniques.

 

  • Data Wrangling

Data wrangling means cleaning, restructuring, and augmenting raw data variables in a usable manner. While performing data analysis in data science, organizing and cleaning data before analysis is the first step to perform.

 

  • EDA

EDA is the abbreviation of Exploratory Data Analysis that refers to the process of performing first-steps investigation on data to discover patterns and spot anomalies.

 

  • Data Visualization

Data visualization in data science is used to communicate data or information with the use of visual objects. The combo of graphics can be points, charts, lines, bars, etc. Data science makes use of the following data visualization techniques or tools:

  • Tableau

It is an awesome data visualization tool that is used by Business Intelligence (BI) professionals to create data insights and visualizations in an impactful and creative way.

 

  • Power BI

The Power BI is another business intelligence tool used by data scientists that is powered by Microsoft. It helps in data analysis and data visualization.

 

  • Matpolib, ggplot, Seaborn

These are the Python libraries used to visualize data in an astonishing way. Each library provides a flair and useful particular visualization task.

 

  • Mathematics

Data Science and Mathematics are related to each other. Familiarity with Probability and Statistics are the required necessities of Data Science. Both of these are separate but complicated terms of Mathematics

  • Statistics

In easy words, Statistics is used to perform technical analysis of the data with the use of Mathematical techniques. Statistical methods are used to make estimation for further analysis. These are dependent upon the theory of Probability that is used to make predictions. As a beginner Data Scientist, you are required to have knowledge and understanding of-

      • Statistical features (including mean, median, bias, variance, percentiles, etc.)
      • Probability distribution (including Uniform distribution, Gaussian or Normal distribution, Poisson distribution, etc.)
      • Over and Under Sampling that helps to balance datasets (Over sampling in case of insufficient data and Under sampling in case of over presentation of majority class)
      • Dimensionality reduction (PCA to create vector representation)
      • Bayesian Statistics (use of mathematical tools to update random events)  
  • Linear Algebra

The primary objective of using Linear Algebra in data science is to find values of ‘X’ and ‘Y’ that will be the solution of a set of several linear equations.

 

  • Differential Calculus

Differential calculus in data science is used to perform data analysis in several chunks and then analyzing how it changes.

 

  • Machine Learning

If you are directly or indirectly related to the field of Computer Science then Machine Learning might not be the new term for you. The capability of Machine Learning is to learn to perform a task without the use of any programming. But, the question here is, if programming is not used then how are the tasks performed? The answer to this question is that various tasks in Machine Learning are performed by training machines using various models of Machine Learning with the help of data and algorithms. So, simply, algorithms are the key takeaways here. You are, therefore, required to have a keen understanding of Machine Learning algorithms, such as-

  • Classification

Classification of Machine Learning includes the concepts of Supervised and Unsupervised learning.

- Supervised Learning is where data is labeled deals with marked data or the information you are feeding to the model. Your software learns by making predictions about the output and afterward contrasting it with the real answer. 

- Unsupervised Learning is where data is not labeled and the target of the model is to make some structure from it. An unsupervised learning can be additionally separated into clustering and association. It is utilized to discover patterns in the data that are particularly helpful in business knowledge to break down client conduct.

 

  • Regression

Linear regression targets a prediction value based on the relationship between dependent and independent variables and forecasting. Ensure that you know how to solve this hypothesis function-

y  = + . x

 

  • Reinforcement Learning

It is the nearest way that people learn, i.e., by experimentation (or trial and error). Here, a performance function is made to tell the model if what it did was getting it closer to its objective or causing it to go the other way. In light of this input, the model learns and afterward makes another estimate, this keeps on occurring, and every new guess is better.

 

  • Deep Learning

It is the sub-category or subset of Machine Learning. It was concerned and inspired by a structured brain called artificial neural networks (ANN). There are several layers of algorithms where the interpretation of data can be fed.

 

  • Dimensionality Reduction

It is a process of reducing random variables by obtaining a set of principal variables. When a few algorithms do not perform well in large dimensions, in this case, the reduction of dimensions is done with the use of dimensionality reduction.

 

  • Clustering

It is a technique of involving a group or a cluster of data points in the same groups. It is a collection of objects based upon the similarity and dissimilarity between them. Ensure to get the understanding of clustering algorithms (like K-means clustering) and Clustering methods (density-based, hierarchical-based, partitioning, and grid-based).

The best thing in Machine Learning is that the algorithms are implemented using R and Python programming languages. Getting expertise in Machine Learning algorithms is achieved by the type of data you work upon and the tasks you are trying to automate.

 

  • Natural Language Processing

NLP or Natural Language Processing in data science refers to the study of computer programming to process and analyze a large amount of data. NLP, in simple words, is used to make computers or machines communicate in a human-understandable manner.

  • NLTK

NLTK refers to the Natural Language Processing Toolkit. It is a suite of various programs and libraries that are used for symbolic and statistical NLP for the language written in English in the Python programming language.

 

  • SpaCy

SpaCy is an open-source library for advanced-level NLP that is written in Python and Cython. It is said to be an “industrial-strength NLP in Python”.

 

  • Text Classification

Text classification is the process of assigning tags or categories to text based upon its content. It is said to be one of the primary level tasks in NLP. It consists of topic labeling, spam detection, sentiment analysis, etc.

 

  • Image Processing

Image Processing in data science is the unstructured data (image) and its analysis that has its applications in various perspectives. It is said to be a medium of sharing ideas, codes, and concepts.

  • OpenCV

OpenCV is referred to as the Open Computer Vision library. It is a library of various programming functions that are aimed at computer vision. It performs all the operations related to images.

 

  • Pillow

Pillow (.pil) is a Python Imaging library that has several standard procedures for the manipulation of images.

 

  • Scikit Image

It is a collection of algorithms that are used for image processing. It is integrated with the algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more.

 

  • Deploy

Deployment is a technique using which an application model is used for the prediction of new data. The purpose of using the deploy model in Data Science is to increase knowledge and make that knowledge usable for the customer in an organized and presentable manner. We can use two main models for the deployment in Data Science-

  • AWS

Data Scientists make use of AWS deploy models in data science to dig deeper into mathematics, science, and statistics behind data.

 

  • Azure

Microsoft’s Azure is used to gain insights from complex and large data sets used in data science. It is used as a composite of data storage and data analytics.

What are the common skills of a Data Scientist?


  • Communication Skills

Data Scientist is a high-tech job, so you cannot ignore communication skills in this career. You should have extraordinary communication Skills so as to turn into a specialist Data Scientist! That is on the grounds that while you comprehend the data better than any other individual, you have to make an interpretation of your information discoveries into evaluated data insights for a non-technical team to assist in decision making. This can likewise include data narrating! So you must have the option to introduce your data in a narrating position with solid outcomes and qualities so others can comprehend what you are stating. Because in the long run, the data analysis is less significant than the actionable insights that can be acquired from the data which will, thus, lead to the growth of the business.

 

  • Analytical skills

It is a fundamental prerequisite for any individual working with data. In any case, if the presence of mind may get the job done at the passage level, your analytical reasoning ought to be additionally backed up by statistical background and knowledge on data structures and algorithms.

 

  • Problem Solving

When you ace new technology, it is enticing to utilize it everywhere. However, while it is critical to know ongoing patterns and tools, the objective of Data Science is to take care of explicit issues by extracting knowledge from data. A decent data scientist initially comprehends the issue, then characterizes the necessities for the solution for the issue, and at exactly that point chooses which tools and techniques are the best fit for the project. Remember that stakeholders will never be captivated by the tools you use, just by the viability of your solution!

 

  • Domain Knowledge

Data Scientists are required to understand the business problem and opt for the appropriate solution for the problem. They should be capable of interpreting the outcome of their models and iterate quickly to reach the final model. An eye on detail is what is required!

How hard is it to learn Data Science?

Data Science is used in three perspectives:-


The use of Data Science is as per the above-mentioned three perspectives but the ideation is a composite of many technologies. It is made out of various technologies like Mathematics (i.e., statistics, calculus, etc.), database management, data visualization, software engineering, domain knowledge, and so on. As I would see it, this might be the major reason why individuals who jump into an entry-level career in Data Science feel totally lost. The vast majority are the people with no idea where to begin since you are required to have a keen knowledge of different sectors and different technologies and that depends upon your academic background and the skills you attain. Fortunately, you don't need to stress a lot over it. Once you take your first step into the career of data science, all the angels of programming will help you open up your career path and make you a clear direction.

What you will be doing Data Scientist?

The primary task of Data Scientist in every industry is to extract structures, unstructured, and semi-structured data for the enterprise. Below are the iterative tasks of a data scientist. 

  • Data collection
  • Data preparation
  • Exploratory data analysis (EDA)
  • Evaluating and interpreting EDA results
  • Model building
  • Model testing
  • Model deployment
  • Model optimization

Data Scientists are required to start over and carefully choose which chunks of data can be utilized and then go back to the collection of additional data. Therefore, firms can benefit from making the best decisions to drive their business growth and profitability. This is the reason why data scientists are mandatory to have a combination of skills including research design.

Data Science – a never stop learning

Staying updated and relevant is significant to the ever-developing field of data science. In this era of technological development, progressing in education will be a leading-edge in getting the job of a data scientist. Though it doesn’t mean that you collect so many degrees, but gaining the skill set is what is required. The field of data science IS factually and mechanically an engaged profession because it is always evolving around learning and developing with the business. Precede your career path to become a data scientist and look for your educational and professional development participating through bootcamps, meetups, webinars, conferences or quality training. So, develop your toolbox of Data Science and step forward towards your career in mastering Data Science.

Last, but not least, Data Science is among those developing careers that work best when you are a part of a broad network of colleagues and peers. When you’re connected with people who share a similar mindset and share your interests, queries, concerns, experiences, and goals you can truly thrive. Remember, Like Minded grouping always helps you achieve your goals.

Let us now discuss some frequently asked questions that you must be having if you want to explore your career in Data Science.

FAQs on becoming Data Scientist

·        How can I decide which data science profession I want to pursue?

The decision of which data science profession you want to pursue really depends upon your skills, goals, and career path. Some data scientists enjoy working in data mining while some like to play with Machine Learning tools. There are some data scientists who work with Big Data platforms like Hadoop, Hive, and Pig. The educational path you choose initially exposes the wide range of knowledge and that will make you realize the area you are good at, about your passion and potential career that is right for you.

 

·        What are the industries looking for data scientists?

If you look around yourself, you will find that every industry employs the power of data and all small and big firms need a data scientist. The only variation is their use of data. Skilled data scientists are utilized by-

·        Health-care industries

·        Ecommerce

·        Banking and Finance

·        Social networks

·        Science-based industries

·        Druggists, etc.

 

·        Do data scientists hire by the government?

Indeed, data scientists are hired by governments to tackle their sensitive data. Government sectors also have a dedicated department for data scientists.

 

Thanks for reading this article. I hope you really enjoyed it and now you are clear about your career goals. So, go ahead and utilize this resourceful information in the journey of becoming a data scientist.

 

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