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How To Become A Data Scientist - Complete Roadmap For 2023

Ankit Sahu

a year ago

What Is a Data scientist? How to be a data scientist? Is data science a good career? What is a data scientist’s job? Which skills are required for data scientists? 
Looking for answers to such questions? Keep reading. 

What Is a Data Scientist?

A technological specialist who specializes in data collection, management, manipulation, and analysis is known as a data scientist. 

What Does a Data Scientist Do?

Data scientists comprehend the data, and then use it to exhibit or forecast useful and profitable insights. 
Data scientists spend a lot of their time obtaining and preparing data since they frequently work with enormously massive data sets, or big data, as it is commonly referred to. 
A data scientist evaluates this data to derive useful business insights, feed it to an AI or machine learning project, build and write new software to simplify the data wrangling process, and arrange it in a database, depending on the particulars of the function.

Popular Roles Within Data Science

Here is a breakdown of some of the most popular job roles within the field of data science. 

Data Scientist

A data scientist’s roles and responsibilities: 
  • collecting and managing big data sets 
  • creating software and algorithms to help automate the process of data wrangling 
  • Using EDA to analyze data 
  • Using visualization so that everyone can understand the insights obtained

Data Engineer

A data engineer’s roles and responsibilities: 
  • constructing pipelines and systems for effective data collection 
  • large-scale data structure and organization for analysis 
  • making a company's data available

Data Analyst

A data analyst’s roles and responsibilities: 
  • receiving inquiries and information requests from company decision-makers and stakeholders 
  • gathering pertinent data and organizing it in relational databases so that it is ready for searching and analysis 
  • taking the data and extracting useful and profitable ideas, then showing them for easy understanding
  • presenting the findings and outcomes to the appropriate company staff

Machine Learning Engineer

An ML engineer’s roles and responsibilities: 
  • developing, constructing, and researching artificial intelligence (AI) software that can change its methods in response to fresh data 
  • Automating procedures with AI and machine learning 
  • Designing predictive models with machine learning that can be utilized to gather business information

Database Administrator

A database administrator’s roles and responsibilities: 
  • transforming unstructured data into relational databases to enable effective storage 
  • maintaining the multiple databases' new additions 
  • enhancing consumer and employee accessibility 
  • designing and putting in place security measures to keep data safe and stored legally

Data Architect

A data architect’s roles and responsibilities: 
  • creating large-scale data storage and organizing solutions 
  • deciding which databases should be created and for what uses 
  • creating data collection, production systems and pipelines 

Business Analyst

A business analyst’s roles and responsibilities: 
  • identifying potential improvement areas within a business using data 
  • combining business sense with data analysis to identify inefficient or costly processes and design remedies 
  • show findings and persuade decision-makers to act

How To Become A Data Scientist - 7 Steps

1. Brush Up on Data Science Fundamentals

Starting out small is never a waste of time because it allows you to focus on the basics before moving on to specialized areas of interest. Your energy should be directed at the following:
  • Statistical Analysis: This refers to the analysis of data and the correct and unbiased presentation of the results using statistics. 
  • Programming: Key competencies used in many areas of data science include writing code and creating software. 
  • Data Visualization: Data is presented in tables, graphs, charts, and dashboards so that both technical and non-technical people can quickly and easily understand it. 
  • Machine Learning: Creating software that learns from the information provided to it and uses that information to nimbly alter its operations in response to various circumstances. 

2. Meet the Educational Requirements

Although a college degree is not necessary to become a data scientist, there are some educational prerequisites you need to be aware of. For Example, you should have a mathematics background with good scores in math, you should be well aware of statistics, and you should be familiar with basic computer operations. 
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3. Hone Essential Skills

Technical Skills

  • Programming Languages: A data scientist should be proficient in Python and generally knowledgeable in one or two other languages, such as R or SQL. 
  • Data Mining: Data mining is the process of acquiring pertinent and reliable information from various sectors, as well as frequently from outside sources. 
  • Data Visualization: A crucial talent for data scientists is the ability to use data visualization tools like Tableau, Microsoft Excel, and Google Charts to display data in an understandable manner. 
  • Data Analysis: This is the process of searching through and examining structured data to look for trends and interesting areas that can be turned into useful information and commercial prospects.
  • Statistics: When establishing company plans, statistics help decision-makers differentiate fact from fiction by offering unbiased views based solely on numbers and data.   
  • Machine Learning: After studying the basic theories underlying machine learning, you might wish to specialize in it, which would require continuing your education and working on your own machine learning tasks. 
  • Deep Learning: Machine learning's deep learning subfield aims to create artificial neural networks that mimic how people learn specific types of information.  
  • Natural Language Processing: Natural language processing (NLP), a subfield of machine learning, is a key component of virtual assistants like Apple's Siri and Amazon's Alexa.
  • Algorithms: Algorithms are collections of rules that carry out calculations and solve issues. AI can be used to train them to make automatic decisions. 
  • Data Engineering: Data gathering, wrangling, cleaning, and other preparatory processes are all considered to be data engineering abilities.

Soft Skills

  • Analytical Mindset: Data science requires an analytical mentality since it involves more than just deriving the right conclusions from data sets. It also involves formulating the right questions to ask. 
  • Business Acumen: Many data scientists strive to make their employers' businesses better, whether it means increasing productivity, boosting earnings, developing valuable ideas, or discovering innovative methods to cut costs. Data scientists need to comprehend what a business requires in addition to being able to read the data. 
  • Critical Thinking: Critical thinking is especially helpful during the data cleaning and wrangling process since it helps identify abnormalities and inaccurate values with collections of data.
  • Adaptability and Flexibility: Data may not always provide us with the information we need since it is what it is, not what we wish it to be. Adaptability and flexibility are essential for getting beyond difficulties and using them to your advantage. 
  • Collaboration and Teamwork: Data scientists typically work in teams, therefore they must get along well with one another. In order to gather data and communicate their findings, they must also work in conjunction with other departments. 
  • Problem-Solving: You'll be a valuable member of any team if you put effort into finding solutions and developing your problem-solving abilities.
  • Communication: To keep a motivated team and a successful project, prompt and clear communication is essential whether an issue needs to be discussed, a deadline is to be extended, or a plan needs to be amended. 

4. Familiarize Yourself With the Essential Data Science Tools

Here is a list of common data science tools that you can become acquainted with:
  • Apache Spark
  • Tableau
  • SAS
  • MATLAB
  • Python
  • R
  • BigML

5. Work on Projects

If you lack experience, building a sizable, impressive, and high-effort portfolio is a terrific method to demonstrate your talent and dedication. Since individuality and creativity are highly regarded, you are free to select styles, projects, and timelines. 

6. Build Your Network 

When you initially enter a field, developing your network is a fantastic method to get a foot in the door. It will assist you in strengthening your abilities, meeting new people who share your interests, working on passionate projects, and ultimately finding employment.

7. Pursue a Data Science Internship

To supplement your studies with practical experience, consider applying for a data science internship if you are fortunate enough to be able to work for no money or for a meager wage.

Do You Want to Learn More About Data Science?

Data science has a significant impact on everything from machine learning to data mining in today's IT ecosystem. InsideAIML contains everything you need to make your data science roadmap journey easier if you want to pursue a career in data science. 
The InsideAIML Data Science Bootcamp offers masterclasses led by eminent professors and subject matter experts. The curriculum uses an interactive learning methodology with live sessions from international practitioners and hands-on labs to cover important data science topics like Python programming, R programming, machine learning, deep learning, and data visualization tools.
Glassdoor estimates that the average yearly salary for data scientists is $120,256. The demand for data scientists is growing, and employers are prepared to pay well and provide a secure future. So, check out InsideAIML if this sounds like the kind of career for you, and start moving in that direction. Go to InsideAIML right away!

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