How to become a data scientist in 2021?

Data science is one of the most fascinating and promising career options for the current generation. However, getting the tag of a data scientist is not everyone’s cup of tea, as it takes a lot of effort and time to grab data science knowledge and skills.

Moreover, a data science professional has to be updated with the latest market trends as we can see new data science products & feature releases every day over the internet. Hence, it is not easy to be a data scientist.

Our aim is not to demoralize you but to make you aware of all the essential information related to data science technology. This blog is focused on guiding you with the latest data science trends and requirements to be a data scientist.

Here you will get everything related to data science as a career niche including the answers to these questions:

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What is Data Science?

The data science definition can be explained as the modern methodology to gather, filter, and analyze extensive sets of unorganized and unstructured data. Data science is upgrading every day with the introduction of new measures of capturing, analyzing, and using huge data sets for organizational growth.

To unveil new opportunities for their organizations, data scientists must have detailed knowledge of the simple data life cycle spectrum. Moreover, they should be flexible enough to accept the latest trends to maximize returns at every phase of the data life cycle.

Data Science Life Cycle

According to the Berkeley Data Science report, the data science life cycle can be defined in five parts:

Capture

This is the first step in which data gets captured from different sources. There are 4 further processes included in this step:

  • Data Acquisition
  • Data Entry
  • Signal Reception
  • Data Extraction

Maintain

In this step, data scientists maintain and store data for future operations. There are total 5 processes included in this step:

  • Data Warehousing
  • Data Cleansing
  • Data Staging
  • Data Processing
  • Data Architecture

Process

This is the step of data processing, which is executable in 4 parts by a data scientist:

  • Data Mining
  • Clustering/Classification
  • Data Modeling
  • Data Summarization

Analyze

In this step, data scientists analyze the processed data to get valuable insights. This step contains 5 processes:

  • Exploratory/Confirmatory
  • Predictive Analysis
  • Regression
  • Text Mining
  • Qualitative Analysis

Communicate

This is the final step of data science methodology that contains a total number of 4 processes:

  • Data Reporting
  • Data Visualization
  • Business Intelligence
  • Decision Making

What is a Data Scientist?

A data scientist is an individual that performs all the aforementioned data science operations in an organization. These professionals are responsible for capturing, filtering, processing, and analyzing datasets to get business insights.

Data scientists have become an essential pillar of business transformation and expansion in the current market. A data scientist can benefit an organization in terms of improved sales and marketing to grow exponentially.

Data Scientist Job Description

You must read and analyze job descriptions properly before building your data scientist resume. A job description gives a precise idea of your recruiter’s requirements, and you can frame your data science resume accordingly.

In addition, you can pick profile-centric keywords from the job description to add to your resume. These keywords will make your resume technically strong and professionally impressive.

Check out the example of a data scientist job description mentioned below:

We are hiring for a data scientist in California. The perfect match must be proficient in transforming clients’ data into tangible business solutions. He/she should have experience in working with open source and visual tools along with modern and flexible deployment options. The hired professional will work within the domains of business intelligence, data management, advanced modeling, and descriptive analytics.

What does a Data Scientist do?

A data scientist with high-level technical skills develops complex quantitative algorithms for organizing and synthesizing bulk information. This process will help in answering all the business-related questions and driving strategies in the organization.

To be a data scientist, you must have strong statistical, programming, and linear algebra knowledge. Additionally, you you need to be proficient in data warehousing, modeling, and mining to develop and analyze required algorithms.

Apart from this, you must be able to use the following key skills and technical tools to grow as a successful data scientist:

Key Skills Technical Tools
R MapReduce
Python Apache Hadoop
Cloud Computing Apache Spark
D3 NoSQL Database
GitHub Tableau
iPython Notebooks ApachePig

How to Become Data Scientist?

In terms of addressing qualification for data scientist, you need to have a bachelor’s degree in quantitative disciplines such as data science, applied mathematics, statistics, computer science, operations research, engineering, or economics.

Apart from this, you can also get additional certification to make your data science profile more professional. Apart from this, you must also have in-depth practical knowledge of using digital tools including data visualization, open-source, and cloud computing.

Here are some skills that can help you in getting a data science job:

  • SQL
  • Big Data Analytics
  • Python
  • Machine Learning
  • Hadoop
  • Apache Hadoop
  • Data Mining
  • Data Analysis
  • Data Modeling
  • SaaS

Data Scientist Career Path

Data science has diverse career options to choose from. These options depend upon your education level and skill set.

Let us see the 3 main profile titles in a data science career and also the qualities that make them different from each other:

Data Scientist

A data scientist generally sources, manages and analyzes huge unstructured data sets. Moreover, they synthesize and communicate the results to stakeholders for deriving business priorities and expansion plans.

Key Skills

  • Statistical & Mathematical Skills
  • Data Visualization & Storytelling
  • Programming Skills (Python, SaaS, R)
  • SQL
  • Machine Learning
  • Hadoop

Data Engineer

A data engineer ensures smooth data transfer within the organization. These professionals focus on the deployment, development, optimization, and management of data infrastructure and pipelines to transfer data to data scientists to transform it effectively.

Key Skills:

  • NoSQL Databases (Cassandra DB, MongoDB)
  • Frameworks (Apache Hadoop)
  • Programming Languages (Scala, Java)

Data Analyst

These professionals fill the gap between business analysts and data scientists. They introduce the questions that need to be answered by the audience so that he/she can organize and analyze data to take out the flaws in business strategy.

Key Skills:

  • Data Wrangling
  • Data Visualization
  • Statistical & Mathematical Skills
  • Programming Skills (Python, SaaS, R)

Hiration Offerings

Hiration aims to provide all the essential help in getting your desired job. Its AI-powered Online Resume Builder is one of its highlighting features that can build your resume easily and effectively.

Take a look at some additional tools given by Hiration that will help you land your dream job:

Bottom Line

We have elaborated on all the essential information about data science along with the job-winning measures in this blog. With the appropriate education degrees and certificates, you can easily step up to pursue data science as a career.

Furthermore, you will need a well-versed data science resume to get a data scientist job in this highly competitive marketplace. Every detail related to data science can be seen above, however, you can always contact us at team@hiration.com, in case you need any assistance in your job hunting.