Learn how to become Data Scientist

Data Scientist

Today, the data science field represents as one of the most rapidly growing and profitable career paths. Companies all over the world employ data scientists for various reasons like discovering the user or product gaps, understanding the customer pain points, assessing the potential growth opportunities, etc. but, due to comprehensive knowledge requirements, and professional and academic training/experience, companies struggle to find such professionals. Data visualization is one of the key tools that a data scientist uses to formulate, represent, and draw conclusions or the trends which they identify during their daily work routine. A data scientist specializes in performing data analysis. This is done within the realm of deploying and building predictive models that often include deep learning protocols and machine learning. S/he must also have a meta-level knowledge of which models are best suited for the data that is being assessed.

This learner’s guide will give you a fundamental overview of data scientists and the other opportunities in this emerging field. Also, we have curated for you a list of steps that will teach you how to become a data scientist.

What is a data scientist?

A data scientist is a professional with expertise in numerous disciplines. S/he is a connoisseur who interprets and extracts data to align and strengthen with the overall goals of a business. They are known to convert data from a raw state into a cleaner and more interpretable manner. Using various casual, predictive, descriptive, and inferential models, data scientist professionals can anticipate and explore problems and then work to form a solution that is based on a multitude of factors. Data scientists are part computer scientists and part mathematicians.  Their skillset includes both the Information Technology and Business sectors and that is why they are in great demand.

What does a data scientist do?

Extracting meaning from various types of data (e.g. structured, unstructured and semi-structured) that will flow in an organization is the main role of a data scientist. His/her typical job includes preparing the data for analysis, extracting data from the database, testing, and building a statistical model or, creating reports that comprise data visualization. There is a data science cycle which a data scientist follows:

  • Data collection
  • Data preparation
  • EDA (Exploratory Data Analysis)
  • Interpreting, and evaluating EDA
  • Building of models
  • Testing the models
  • Deploying the model, and
  • Optimizing the model

This cycle is iterative, which means that a data scientist will in an ‘evaluation mode’ throughout this entire process.

Steps to become a data scientist

Following are the steps that you should follow on your pathway to become a data scientist:

  • Step 1: Preparation

You can start preparing to become a data scientist professional even before stepping into the university or the campus. You can become proficient in various programming languages used in data science like Java, Python, and R. Refreshing your knowledge in statistics and mathematics is also recommended. This will help you in getting a head start. In fact, if you have already established skills before entering college, then, this will enhance your learning rate. In addition to this, early exposure to data science knowledge will help you determine whether this career pathway is fit for you or not.

  • Step 2: Completion of studies

The most popular majors for data science are mathematics, Information Technology, statistics, and computer science, or even data science. Continue to learn database architecture, programming languages, and you can also add SQL/MySQL to the ‘to-do list.’ In the university itself, you can start building professional connections and networks. Look for various internship opportunities, ask the advisors and professors for constant guidance. Some companies also accept STEM degrees such as engineering, physics, and biotechnology. As a data scientist, you must have a deep understanding of industry-accepted data management programs and how computing and distributed storage work in relation to model building and predictive analytics. According to reports, almost 44% of data scientists have also earned a Ph.D. degree. ‘Bootcamps’ are also available that will speed up the learning process.

  • Step 3: Choose a specification in master’s and get entry-level jobs

Various career pathways can bring a rewarding and lucrative career. You can start working after the Undergraduate degree that will lead you to jobs like data visualization specialist, market research analyst, and management analyst. After that, you can get your hands on a master’s degree in fields like data engineering, statistics, and algorithm development. System-specific certifications and training in data-related fields might also help you in getting entry-level jobs.

  • Step 4: Get your hands on that certification

Earning a certificate will not only improve your skills but will also make you a more marketable candidate. Some potential certifications include Cloudera Certified Professional: Data Scientist, Certified Applications Professional, SAS Certified Predictive Modeler, EMC: Data science associate. 

  • Step 5: Get hired

Once you have completed your education, the data science landscape is open for you. Data science is a specialty. Career forums, recruiting networks, and job boards exist exclusively for them. Additional expertise and education will always benefit you in this field. Coupling robust technical skills along with leadership and project management experience will surely make you a desirable candidate.

  • Step 6: Keep the gateway to learning always open

In the ever-evolving field of data science, staying relevant will make you reach heights. Continuing your education against the shifts in the career markets can prove to be tricky. However, a career-oriented data scientist will evolve and learn along with the industry standards. S/he should continue to look for educational and professional development by attending various workshops, conferences, and boot camps.

Key responsibilities

Below mentioned are some typical responsibilities that a data scientist has in a firm:

  • Collecting a large amount of data and converting it into a more presentable manner that can easily be analyzed
  • To combat business-related challenges, problem-solving skills are required when you are working with data-driven techniques and tools
  • Using a variety of programming languages for analysis
  • Working with typical analytical techniques and tools
  • Communicating the findings and offering relatable advice through effective comprehensive reports and data visualizations
  • Identifying various trends and patterns in data
  • Providing a structured plan to execute the improvements
  • To anticipate future demands and to predict analysts
  • Contribute towards modeling standards, data mining architectures, reporting, and data analysis methodologies
  • To solve problems and create analytical tools, inventing new algorithms
  • To recommend budget-friendly changes in existing strategies and procedures

Skills Set

As a data scientist, you must have the following skills:

  • Business savviness: S/he must understand the business sector for which they are working. Complex problems can only be given a solution when they align their expertise with business objectives/logic.
  • Experience and fluency: Familiarity with coding/computer programs is highly recommended. S/he must have fluency in MATLAB R, SPSS, SAS, Python, JAVA, C/C++, SQL/NoSQL, Hadoop platform databases.
  • Communication skills: A data scientist must be an effective communicator. They should be able to translate their technical findings in a fluent and clear manner. A data scientist must empower an organization to make decisions by repressing their verifiable and robust information.
  • Expert technical skills: Comprehensive knowledge required in statistics, math, machine learning tools and techniques, data cleaning and munging, data mining, report techniques, data visualization, and unstructured data techniques.

Nothing in life is easy, not even data science. But you can always stay motivated and oriented towards what you want. If you are consistently learning, building projects, and sharing them, you will gain expertise and can get your hands on any job that you like. There are many paths to this career, however, the above-mentioned steps would give you a general understanding of how to become a data scientist by following a simple path. The only prerequisite is that you must come from a background with technical skills in statistics and programming. Yes, mathematics is also essential but, most data scientist jobs require statistics above anything else. So, don’t let anything hinder your commitment, just get your hands on the right resources and take the first step towards this massively evolving field.

Know more about educational training here.

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