Data Science for Non-Techies: Your Step-by-Step Guide

Data Science for Non-Techies: Your Step-by-Step Guide

Not tech-savvy? It is still possible to learn how to do data science! The field of data science is rapidly growing, but where do you begin? In this comprehensive guide, you’ll learn how to complete the entire process from start to finish.

Also Read: Google Analytics Basics for Beginners

Source: Simplilearn

Table of Contents

But first, let’s define Data Science.

The field of Data Science involves the analysis, interpretation, and prediction of data derived from various sources by utilizing scientific methods and processes. To identify patterns in data, machine learning algorithms, mathematics, statistics, programming, and domain expertise are heavily used. Big data is often used to uncover insights and draw meaningful conclusions from large datasets. It is the ultimate goal of data science to use the power of data analysis to gain knowledge that is valuable to individuals and organizations.

Definition: An interdisciplinary field of science, data science focuses on extracting knowledge and insights from structured and unstructured data in various forms. In addition to statistics, machine learning algorithms, domain expertise, and other technologies, data science can be used to improve decisions and create predictive models. By exploring large datasets, data scientists can uncover patterns and trends that might otherwise go unnoticed.

Insights derived from data science can range from simple reports on what happened to more complex models predicting future outcomes. Also, data science is used in healthcare and finance to identify correlations between variables and outliers.

Source: TEDx Talks

How Is Data Science Used in Business and Industry?

Data Science is used in a variety of industries to allow companies to make decisions and optimize their processes. From finance, marketing and sales to healthcare, customer service, and more, data science is transforming the way businesses operate and giving them an edge over the competition.

By using predictive analytics, natural language processing, machine learning, and other data science techniques, companies can uncover meaningful insights about their customers and operations that can help them improve efficiency and maximize revenue.

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What are the Benefits of Working with a Data Scientist?

Working with a Data Scientist can open up numerous benefits to a company. Data Scientists can uncover meaningful insights that can help inform strategic decisions, drive better pricing and marketing strategies, improve customer experience, optimize production processes and more.

They also can develop custom models and algorithms that can automate decision-making and streamline processes. In addition, keeping up to date with the most current data science techniques enables companies to stay ahead of the competition by leveraging cutting-edge analytics tools and methods.

How Can Companies Get Started with Data Science?

Companies new to data science should start by gaining a basic understanding of the various tools, technologies, and processes used within the data science field. As part of this initial exploration process, teams should identify any areas where they are lacking in terms of resources or skills needed to pursue data-driven initiatives. Companies should then create an analytics roadmap which outlines how they can transition from basic reporting towards more advanced data-driven decision-making processes.

Additionally, working with a qualified Data Scientist who has knowledge and experience in the field is essential for ensuring that a company’s data challenges and objectives are addressed effectively.

What Are the Challenges in Applying Data Science to Real-World Problems?

One of the biggest challenges in applying data science to real-world problems is that the data available may be inadequate or incomplete. Data scientists must be able to work with the “messiness” of real-world data, which can often include missing values, outliers, and irregularities in format.

Additionally, data scientists must be mindful that the insights they develop from the data cannot always guarantee a result in the context of a live environment. As a result, companies should take caution to ensure that decisions based on such insights are thoroughly tested before launch.

Source: TEDx Talks

Data Science for Non-Techies: Your Step-by-Step Guide

If you’ve ever wanted to dip your toes into the fascinating field of data science, now is the perfect time to do it! Whether you’re a tech whiz or just starting, this comprehensive guide will walk you through the entire process from start to finish.

The process of Data Science is not an event. By using data to gain a deeper understanding of the world, one can better understand different things. In my opinion, this is when you have a model or hypothesis of a problem and try to validate it with your data.

In data science, insights and trends are uncovered by uncovering hidden patterns. Translating data into a story is what it is all about. To generate insight, use storytelling. With these insights, you can make strategic decisions for a company or institution.

An area of data science is the process and systems for extracting data from various forms, whether they are unstructured or structured. A data scientist studies data. In the same way that biology is the study of biology, physical sciences are the study of physical reactions. The data is real, the data has real properties, and if we are to work with them, we must study the data.

A data scientist uses data and some science to solve problems. It came up in the 1980s and 1990s when some professors were looking into the statistics curriculum, and they thought data science would be a better name.

How does Data Science work?

In my opinion, data science is the process of working with data to answer questions. To put it simply, it’s more about data than science. It is the exercise of going through data, analyzing it, and trying to get some answers from it that constitutes data science if you have data, curiosity, and you are working with it, manipulating it, and exploring it.

Having access to so much data makes data science relevant today. In the past, we worried about a lack of data. We are now drowning in data. Algorithms were not available in the past, but now they are available. Previously, software was expensive, but now it’s free and open source. Data storage used to be prohibitively expensive; now we can store gazillions of datasets for a fraction of the cost.

As a result, the tools to work with data, the availability of data, and the ability to store and analyze data are all cheap, widely available, and ubiquitous. Being a data scientist has never been more exciting.

Source: TEDx Talks

Your Step-by-Step Guide:

Step #1: Understand the Basics of Data Science.

Data science is a multidisciplinary field that involves extracting, organizing, and analyzing large amounts of data. It’s used to discover new insights, create better products and services, and make informed decisions for any industry. To get started with data science, it’s important to understand the basics such as data collection methods, cleaning procedures, and analysis techniques. Additionally, an understanding of statistics and programming languages will come in handy when working with data sets.

Step #2: Learn Mathematics for Data Science.

To understand data science, you’ll need to learn the fundamentals of mathematics. This includes topics such as probability and distributions, linear algebra, calculus, optimization algorithms, and more. Being able to think mathematically makes analyzing data easier and allows you to come up with more accurate conclusions. With a good knowledge of math concepts in hand, you’ll be on your way to understanding the complexities of data science.

Step #3: Get Familiar with Technical Tools and Programming Languages.

Before you can get started with data science, you’ll need to learn about various software and programming languages. Popular terms like Python, R, Java and SQL are used to manipulate data and develop data models. Additionally, tools like Microsoft Excel and Tableau help you explore and visualize your data more efficiently. With an understanding of the different kinds of tools available, as well as a knowledge of the essential programming languages, you will be able to effectively undertake your first data science project.

Step #4: Acquire the Necessary Statistical Knowledge.

Understanding basic statistics is important for any data science project. You should become familiar with concepts like mean, median, mode, standard deviation and correlations to interpret your data correctly. Additionally, you should understand which tests are suitable for certain kinds of data as well as the assumptions to watch out for when analyzing data. With the right statistical knowledge in hand, you will be able to extract meaningful insights from your data more accurately.

Step #5: Experiment with Machine Learning Algorithms.

With the right statistical knowledge in hand, you are now ready to explore machine learning algorithms! Machine learning is an area of AI that allows computers to learn and make predictions without being explicitly programmed. The most commonly used algorithms are linear regression, logistic regression, support vector machines, decision trees and boosting methods. For a beginner data scientist, applying different machine learning algorithms to your data can help inform the selection of the best model for a given dataset.

Step #6: Understand Basic Statistics.

To be an effective data scientist, it is important to first have a basic understanding of statistics. This includes knowing how to interpret and use statistical terms such as mean, median, mode, variance, standard deviation, percentiles and more. Familiarity with concepts such as probability distribution and hypothesis testing can also help you understand the deeper implications of data. Having a strong background in these fundamentals will help you effectively analyze data and draw meaningful conclusions from it.

Step #7: Acquire and Manipulate Your Data.

Once you have a good working knowledge of statistical analysis, the next step to starting your data science career is acquiring and manipulating data. This involves collecting data from various sources and then transforming it into a structure that can support more meaningful analysis. Techniques such as data cleaning, normalization, imputation, and sampling can help make sure that the dataset accurately reflects the question you’re trying to answer. Knowing how to extract and reformat data in these ways sets you up for success when it comes to performing meaningful research projects.

Step #8: Leverage Powerful Tools.

Data scientists rely on a variety of powerful tools to help them manipulate data, measure performance and generate visuals. Some popular programming languages for data science include Python, R and Julia. Machine learning advancements such as natural language processing, speech recognition and computer vision can help extract insights from complex datasets.

Visualization tools like Tableau are also essential to creating informative presentations that communicate results. Taking the time to become familiar with the most useful methods and technology will give you an edge in your data science journey.

Step #9: Communicate Insights Effectively with Visualization and Reporting.

Reports are one of the most important aspects of data science, as they enable data scientists to share their insights with co-workers or management. Writing effective reports requires understanding how to structure and display data so it’s easy to digest and interpret.

Data visualization can be a powerful way to communicate insights from raw data, helping your audience identify patterns and trends quickly. While there is no universal approach to creating successful visualizations, any aspiring data scientist needs to become familiar with popular software like Tableau or Microsoft Power BI to help them present their research more effectively.

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5 Must-Have Skills Every Data Scientist Should Know

Data science requires mastery of a wide range of skills. Learn what you need to know to stay ahead with this essential guide to data science skills!

Data science is one of the most in-demand skill sets today. If you want to stay competitive in this rapidly growing field, then you’ll need to be familiar with everything from coding languages to machine learning technologies. This guide will make sure you’ve got the skills needed for data science success!

Skill #1: Math and Statistics Knowledge.

To succeed as a data scientist, it’s essential to have a deep understanding of mathematics and statistics principles. Basic skills like calculus, linear algebra, probability theory and algorithms will help you work through problems more efficiently and effectively. You should also be familiar with machine learning techniques, such as decision trees, supervised/unsupervised learning, natural language processing (NLP), and deep learning.

Skill #2: Understanding of Machine Learning Algorithms.

In addition to being able to understand basic mathematics and statistics principles, data scientists need to have a strong understanding of machine learning algorithms. This includes supervised learning algorithms such as decision trees and random forests, unsupervised learning techniques such as clustering and dimensionality reduction, natural language processing (NLP) for working with text data, and deep learning for training neural networks. Being able to properly use both established and non-standard techniques can help data scientists make the best use of their time, leading to faster insights and predictive models.

Skill #3: Strong Programming Proficiency.

As a data scientist, you need to have a strong ability to write efficient computer code in the language of your choice. It doesn’t matter which one you pick – some data scientists specialize in Python, R, Java and other languages. Being able to quickly write efficient code based on the task at hand is essential for a data scientist – and having proficient debugging skills can also be extremely helpful.

Skill #4: Ability to Extract, Analyze, and Visualize Data

Data scientists must have the ability to quickly and accurately process large amounts of data efficiently. This means being able to extract, clean, and normalize data from different sources, interpret it correctly and effectively analyze it. Furthermore, a data scientist also needs to be able to effectively visualize the analyzed results through effective visualizations such as graphs, plots, or maps that can easily inform decision-makers.

Skill #5: Problem-Solving and Critical Thinking Abilities.

As a data scientist, you will be responsible for breaking down complex problems and figuring out solutions by forming hypotheses and then testing them. You should have the desire to challenge assumptions and dig deeper into data sets to identify patterns or inconsistencies that may lead to discoveries. It also helps if you are knowledgeable in various problem-solving techniques and processes, understand where they can be applied, and know how best to use them to reach successful outcomes.

Source: Simplilearn

What Certifications and Qualifications Are Needed to Become a Data Scientist?

Discover what qualifications and certifications are required to become a data scientist. Learn about how these qualifications can set you apart from the competition!

Data Science?

Data science is a rapidly growing field, and it’s important to have the right qualifications to stand out from the crowd. From undergraduate degrees to industry certifications, there are a wide variety of ways to demonstrate your data science knowledge and skills. Read on to find out what qualifications you need for a career in data science.

What Certifications and Qualifications Are Needed to Become a Data Scientist?

Certifications and Qualifications #1: Obtain a Bachelor’s Degree.

Getting a bachelor’s degree is the foundation of any data science career. While it isn’t always needed, having an educational background in mathematics, computer science, statistics or information technology can signal to potential employers that you are well-versed in the areas required for data science work. A course in machine learning and artificial intelligence can also be beneficial. Alternatively, you can qualify with relevant work experience and support from industry certifications.

Certifications and Qualifications #2: Get Industry Certifications and Qualifications.

Data science certifications and qualifications can be a great way to demonstrate your commitment to the industry and potential employers. Some of the most well-known certifications for data scientists include the Certified Analytics Professional (CAP), SAS Certified Data Scientist, and IBM Professional Data Science credentials. Taking courses on different software platforms (such as Python, R, and SQL) may also be recommended for aspiring data scientists who want to get ahead in this competitive field.

Certifications and Qualifications #3: Join a Professional Data Scientist Society.

Joining a professional data scientist society such as the American Statistical Association (ASA) or the International Society for Computational Biology (ISCB) can be extremely beneficial in your career. These societies are great places to network with other professionals and stay up-to-date on trends, research, and job opportunities in the field. Some of these organizations even offer certifications or further credentials that may set you apart from the competition and boost your chances of securing a job.

Certifications and Qualifications #4: Gain Practical Experience by Contributing to Projects & Working in Groups.

In addition to technical qualifications, building up practical experience is a great way to become a data scientist. Working on projects or joining groups in the data science community can help you gain familiarity with tools and techniques used in the field. Many online coding communities offer open-source projects, allowing anyone to join the project and contribute. In addition to gaining technical experience, working in teams allows you to develop skills such as communication and collaboration—both of which are essential for working as a data scientist.

Certifications and Qualifications #5: Pursue Advanced Education and Training Opportunities for Data Scientists.

Data science is an ever-evolving field, and to stay competitive, having certifications and qualifications related to data science on your resume can set you apart from the competition. Many universities now offer master’s programs specifically in data science, allowing you to gain the specialized knowledge and skills needed for a successful career. However, if you do not have the time or opportunity to pursue these expensive degree programs, there are many online courses available that provide more practical hands-on experience with coding and data analysis. Such courses focus on teaching students real-world skills that can be applied directly to their data scientist role.

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Want to know why Data Science has been dubbed the sexiest profession of the 21st century?

As a result of reading this guide, you will be able to answer this question, gain a thorough understanding of Data Science, and what data scientists do, and learn about careers within the field.

It has been a tradition since ancient times to discover insights and trends in data. Ancient Egyptians used census data to increase tax collection efficiency and predicted floods of the Nile river accurately. Since then, people who use data to derive insights and predict outcomes have carved out a distinct field. The field is known as data science.

Today, we use Data Science to find patterns in data and make meaningful predictions and conclusions based on them. Data scientists have even been called “the sexiest job of the 21st century” by Harvard Business Review. Unstructured data is managed, mined, cleaned, and prepared for use by data scientists. Data is then used to deliver results that impact business outcomes.

Source: Jay Feng

Data Science FAQs

Define Data Science?

Data Science is one of the hottest fields in technology, integrating elements of computer science, mathematics, statistics, and other disciplines to analyze vast amounts of data. By uncovering deeper insights from data, Data Science is helping to revolutionize many industries and build new avenues for analytics.

What is Data Science?

Data Science is the process of extracting valuable insights and patterns from a large set of data. It involves constructing algorithms, tools, and processes to explore large amounts of data to uncover new information. This can be done through a variety of methods such as predictive analytics, natural language processing, machine learning, and data mining. Through these processes, data scientists can gain insights that can inform decision-making and allow companies to become more efficient and successful.

Data science salary

A Data Scientist in India typically earns around 4.0 Lakhs per year (33.3k per month) as a starting salary. The minimum experience required to become a Data Scientist is one year.

Data science masters

Graduates of master’s programs in data science typically earn high salaries. Whether students come into a master’s degree program in data science with a bachelor’s degree in a related field or are looking to change careers completely, obtaining an advanced degree typically leads to higher wages.

Data science jobs

The following is an example of a Data Scientist job profile: Job Description

Data science for all

To become a data scientist, you generally need a solid background in mathematics and computer science, as well as experience working with large datasets. Furthermore, it is often helpful to have experience with machine learning and statistical modelling.

Whether you are a newcomer or a working professional, data science courses are open to everyone. To enrol in a regular or part-time data science course, you must have a graduate degree. A data science course does not require coding experience.

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Data science certificate

For more information, please refer to this IBM Professional Certificate. HERE

Data science Bootcamp

Data science Bootcamp is an intensive and immersive program that prepares you for a career in the field. The programs can take only a few months, but they’ll prepare you for some of the most in-demand jobs in the country.

Check out this guide for more information.

Data science with R

In R, users can explore, model, and visualize data using objects, operators, and functions. The R programming language is used to analyze data. The R programming language is used for handling, storing, and analyzing data in data science. In addition to being able to analyze data, it can be used to model statistics.

To learn more, check out this guide: R Programming for DS. HERE

Data science internship/ data science internships


Data science degree/ Data science course/ Data science online masters

What is an online data science degree? Read Here

Data science with python/ Python with data science

Data Science with Python Tutorial, Read Here.

Degree- Data science PhD

PhD in Data Science, Apply online here.

Data science vs data analytics

Machine learning and algorithms are used by data scientists to improve the use of data for business purposes. In addition to collecting, storing, and maintaining data, data analysts analyze the results of that data.

Data science major

You will learn how to collect and manage data responsibly and how to use it to make informed decisions as a data science major. It is an interdisciplinary degree program that incorporates computer science, statistics, mathematics, business, and social sciences.

Data science python

Python offers a great approach to object-oriented programming because it’s an open-source, interpreted language. For various data science projects/applications, it is one of the best languages to use.

Data science interview questions

A few examples are as follows:

  1. What are the differences between supervised and unsupervised learning?
  2. How is logistic regression done?
  3. Explain the steps in making a decision tree.
  4. How do you build a random forest model?
  5. How can you avoid overfitting your model?

You can find out more with the help of the PDF attached below:

Data science definition

The goal of data science is to extract meaningful insights from data. A multidisciplinary approach that combines mathematical, statistical, artificial intelligence, and computer engineering principles and practices.

Data science master’s programs

With the industry’s expansion, the need for management is also increasing, which is why an MBA in Data Science is a good idea.

How much does data science make?

In the future, data science offers tremendous advancement opportunities. The demand for Data Scientists is high, salaries are competitive, and perks are plentiful, which is why LinkedIn has named it the “most promising career” and Glassdoor the “best job in America.”.

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Data science jobs entry level/ Data science Berkeley/ Data science career/ Data science entry-level jobs/ Data science as a career/ Data science training

You can learn more about these courses offered by Coursera by clicking here.

Data science resume

Take a look at this guide: 9 Data Scientist Resume Examples for 2023

Who is a data scientist?

Data Scientists work with large amounts of data to better understand and interpret it. Data scientists analyze, interpret, visualize, and integrate data; develop sophisticated algorithms; build machine learning models, and create customized dashboards and reports. They are experts in a variety of programming languages such as Python, R, SQL, Scala, Java, etc.

Data Scientist Tasks?

Data Scientist tasks include creating predictive models from large data sets using various methods such as linear regression, decision trees, neural networks or support vector machines; building automated reports for large datasets; analyzing big data to find patterns; designing experiments to extract insights from data; developing machine learning algorithms for large-scale applications such as natural language processing or computer vision applications; applying supervised and unsupervised learning techniques on structured or unstructured datasets; developing modern distributed computing architectures for scalable analytical solutions. Additionally, they must be able to communicate the results of their analyses clearly both orally and in writing.

Data Science Book [PDF]


We are in the midst of an ever-evolving field filled with opportunities for discovery and development in data science. Almost anyone can become a successful data scientist if they have the right tools, knowledge, and dedication. This beginner’s guide will teach you the basics of data science and provide you with a foundation for further learning.

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