Free Online Courses to Learn NodeJS

NodeJS is an asynchronous event-driven JavaScript runtime which is open source and cross-platform. It is the fastest growing open source project with more than a billion downloads. It is becoming increasingly popular owing to its traits of being extremely lightweight and flexible for building scalable web apps and APIs. It has found applications in web apps of Netflix, PayPal, LinkedIn and Uber, to name a few.

Here are some free online courses that can help you learn NodeJs

1. Getting started with NodeJS | SimpliLearn: 

Discover Node.js with this free course, designed for beginners. Dive into the JavaScript runtime environment and learn to build applications outside the browser. Master the basics of Node.js architecture, Node Package Manager (NPM), and Express Framework through easy-to-follow steps.

Access the course here.

2. Node JS Certification Course - Master the Fundamentals | Scaler

The Scaler Topics Node JS free course with certification is an online learning program designed for beginners who are interested in learning about server-side web development. This course will introduce you to Node.js, its architecture, and its uses in web development. The course is self-paced and can be taken online from anywhere in the world.

Access the course here.

3. A Beginner's Guide to Node.js | Udemy

This course will help you to get started with building your own app on Node.js

Access the course here.

4. Intro to Node JS & Express | Udemy

Get started on your full-stack developer career by learning the most popular Javascript web framework in the world.

Access the course here.

Free Python Programming Courses From Harvard, MIT, Stanford

Embarking on the journey into the domains of data science or software engineering often starts with mastering Python—a versatile language highly valued in these fields. Yet, sifting through the numerous available resources can feel overwhelming.

To simplify your quest for Python expertise, we've curated a collection of free Python programming courses offered by esteemed universities. These courses not only facilitate learning but also enable you to code comfortably from your own home.

Here are 5 recommended free Python programming courses:

  1. 1. Python for Everybody – University of Michigan

  2. Led by Dr. Charles Severance, this course swiftly immerses learners in Python's features. Covering Python basics, data structures, file operations, regular expressions, network programming, database handling, and data visualization, it caters to a broad spectrum of Python functionalities, including applications like web scraping.

  3. Access the course here.

  4. 2. Principles of Computation with Python – CMU

  5. Carnegie Mellon University’s course introduces Python alongside fundamental computing principles. Exploring iterative and recursive thinking, binary data representation, cellular automata, encryption methods, and computability limits, it offers a well-rounded understanding of Python and core computer science concepts.

  6. Access the course here.

  7. 3. Introduction to Computer Science and Programming with Python – MIT

  8. MIT’s course targets individuals new to programming or computer science fundamentals. Across twelve lectures, it covers computational basics, string manipulation, iteration, recursion, object-oriented programming, testing, debugging, and more, making it accessible even to non-computer science majors.

  9. Access the course here.

  10. 4. Introduction to Programming with Python – Harvard

  11. Tailored for beginners with no prior programming experience, Harvard’s course spans ten weeks and equips learners with fundamental Python skills. Topics covered include functions, variables, loops, exceptions, object-oriented programming, supplemented by accessible lectures, notes, and problem sets.

  12. Access the course here.

  13. 5. Programming Methodology – Stanford

  14. Stanford's CS106A course is designed for Python beginners, covering variables, control flow, lists, file operations, object-oriented programming, and memory management through assignments and comprehensive content.

  15. Access the course here.

While these courses act as gateways to Python proficiency, immersing yourself in these resources and engaging with the materials will empower you to navigate Python programming's intricate landscapes and its diverse applications.

The Best Books to Learn Python for Beginners

If you're new to Python and want to learn it, there are some great books out there. Here are a few:

1. Python Crash Course by Eric Matthes

This book starts from the basics, teaching you about Python's elements, data structures, and more. It then guides you through creating projects like an Alien Invasion game and a web application using Django.

2. Head-First Python, 2nd edition by Paul Barry

This book uses a visually engaging format with lots of examples. It covers topics like lists, modules, errors, and file handling through a project of building a website for a coach. It's more beginner-friendly than some other books.

3. Invent Your Own Computer Games with Python, 4th edition by Al Sweigart

This book focuses on building classic games while teaching Python. It starts with simple scripts, gradually introducing concepts like functions, loops, and dictionaries, through projects like Hangman, Tic-Tac-Toe, and game graphics using PyGame.

4. Think Python: How to Think Like a Computer Scientist, 2nd edition By Allen B. Downey (O’Reilly, 2015)

 How to Think Like a Computer Scientist by Allen B. Downey: This book takes a serious approach to teaching Python. It's more linear and covers a wide range of Python topics, including some advanced areas like operator overloading and analysis of algorithms.

5. Effective Computation in Physics: Field Guide to Research with Python, by Anthony Scopatz, Kathryn D. Huff (O’Reilly, 2015)

Field Guide to Research with Python by Anthony Scopatz and Kathryn D. Huff: This book not only teaches Python but also covers related topics like the command-line, data analysis, debugging, and software deployment. It's great for scientists and engineers.

6. Learn Python 3 the Hard Way, by Zed A. Shaw (Addison-Wesley, 2016)

This book makes you type in all the code, do exercises, and find solutions to problems. It's structured with over fifty exercises, each building on the previous, and covers various Python features. It's a bit challenging but rewarding.

Each of these books has its own style and approach. Depending on your learning preferences and what you want to achieve with Python, one of these might be a good fit for you.


Data Scientist Salaries

How much do data scientists earn?

Data scientists make a lot of money compared to other jobs. In 2022, the median salary for data scientists in the US was about $103,500 per year. That's more than double the typical salary for all workers, which was around $46,310.

Different websites show slightly different average salaries for data scientists:

  • US Bureau of Labor Statistics: $103,500
  • Glassdoor: $117,664
  • Indeed: $124,758
  • Zippia: $106,104
  • Payscale: $99,344

How much you make as a data scientist can depend on a few things:

1. Experience: 

The more years you've worked as a data scientist, the more money you can make. For example:

  • 0-1 years: Around $96,986
  • 1-3 years: Around $108,197
  • 4-6 years: Around $118,101
  • 7-9 years: Around $124,037
  • 10-14 years: Around $131,327
  • 15+ years: Around $140,079

2. Location:

Where you work can affect your salary. For instance, in cities like Los Angeles or New York, data scientists tend to earn more than in other places.

  • Los Angeles, CA: $142,826
  • Boston, MA: $129,770
  • Houston, TX: $117,851
  • Chicago, II: $118,398
  • Washington, DC: $123,311
  • New York, NY: $127,841
  • Philadelphia, PA: $110,645
  • St. Louis, MO: $107,777
  • Columbus, OH: $111,044
  • Minneapolis, MN: $104,707

3. Industry:

The type of industry you work in matters too. Industries like Information Technology or Financial Services usually pay data scientists more.

  • Information Technology: $128,037
  • Retail & Wholesale: $117,664
  • Real Estate: $115,165
  • Financial Services: $115,657
  • Media & Communications: $117,664

4. Education: 

Having a higher education level often leads to higher salaries. Many data scientists have at least a bachelor's degree, but some have master's or even doctoral degrees.

The job outlook for data scientists is really good. The number of data scientist jobs is expected to increase by 35% from 2022 to 2032, creating lots of new job opportunities.

If you want to earn more as a data scientist, consider getting a higher degree in data science or a related field. More education can lead to better-paying positions, especially senior roles that might require a master's or even a PhD.

How to Learn Data Science

Data science has become ubiquitous across various industries, with companies worldwide increasingly relying on it to tackle diverse challenges. This surge has elevated data scientists' positions, offering advantageous prospects in terms of employment and remuneration.

Consequently, a growing number of individuals are drawn to exploring the path of becoming a data scientist. If you're among those intrigued by this field, this article aims to provide a comprehensive guide on venturing into the world of data science.

What Does a Data Scientist Do? 

Fundamentally, these professionals strive to deeply comprehend and analyze data to yield actionable insights.

Within business environments, they collaborate across diverse teams to establish a robust analytics framework. This involves devising strategies to capture, compile, and refine data from diverse sources. After organizing and exploring this data, they construct solutions and communicate findings across the organization.

Data scientists often collaborate with other professionals, including data engineers, analysts, IT architects, application developers, and business stakeholders.

Data Analyst vs Data Scientist: Differentiating Roles

In summary, both roles involve working with data but differ in their scope and responsibilities:

Data analysts typically interpret existing data to offer actionable insights, utilizing simpler models and tools.

Data scientists assume a broader role, often framing the questions needing answers. They employ advanced tools and models, such as machine learning algorithms, to extract insights.

Why Pursue a Career in Data Science? 

Numerous motivations drive individuals toward a career in data science. It's an industry known for its rewarding nature, offering an intellectually stimulating environment. Data scientists must stay abreast of the latest technological advancements, operating within a dynamic setting.

If you possess intellectual curiosity, an analytical mindset, and a passion for working with data and technology, pursuing a career as a data scientist might align with your aspirations.

Moreover, the job market contains ample opportunities for skilled data scientists. Statista forecasts significant growth in the big data market, expecting it to reach $103 billion by 2027 from $70 billion in 2022. Similarly, the US Bureau of Labor Statistics predicts a 36% surge in data science industry jobs between 2021 and 2031, far exceeding the 4% national average.

This burgeoning demand is reflected in the popularity of data science careers, with rankings from various organizations. U.S. News & World Report lists data scientist as the 3rd best job in technology, 6th in STEM jobs, and 6th overall. Similarly, Glassdoor ranks it as the 3rd best job in America for 2022.

If you remain undecided, the average data scientist salary might be enticing, with a median of $102k per year, indicating substantial demand and remuneration.

Qualifications Required for Data Scientists

Debates persist regarding the necessity of a degree to embark on a data science career. While some professionals entered the industry through alternative paths, a university qualification can offer advantages.

Most graduate schemes and jobs seek individuals with degrees in fields such as computer science, data science, mathematics, statistics, engineering, or physics. However, some programs are open to training individuals with any degree to become data scientists.

We'll delve deeper into the essential skills later, but proficiency in programming languages like Python, R, SQL, and/or Julia can be advantageous.

Some individuals opt for self-directed learning, acquiring necessary skills and experience at their pace, impressing employers during interviews.

Skills Essential for Data Scientists

Hard Skills: 

Technical competencies crucial to becoming a proficient data scientist include proficiency in:

  • Python
  • R
  • Statistics and mathematics
  • SQL and NoSQL
  • Data visualization
  • Machine learning
  • Deep learning
  • Natural language processing
  • Big data
  • Cloud computing

Soft Skills: 

These cross-functional skills, often termed 'power skills' or 'human skills,' are indispensable for data scientists. These include:

  • Communication
  • Storytelling
  • Critical thinking
  • Business acumen
  • Problem-solving
  • Teamwork

Average Data Scientist Salary

Data science salary figures vary across sources. For instance, Glassdoor cites an average salary of $117,212 per year based on 18,000 salaries, while PayScale estimates $97,318 annually from 7,280 profiles.

Data science managers typically earn the highest, ranging from $113,000 to $230,000 annually. Data scientists and data engineers often command similar salaries, ranging from $82,000 to $167,000 per year for scientists and $76,000 to $166,000 per year for engineers.

Company size significantly impacts salaries, with larger companies paying a median salary ranging from $90,000 to $110,000 per year for companies with over 1,000 employees compared to around $80,000 for medium-sized companies (26 to 1,000 employees) and $60,000 for small companies and startups (2-25 members).

How to Embark on the Data Science Journey

You might be eager to begin your journey toward becoming a data scientist. Below, we've highlighted eight steps that can guide you from scratch:

1. Master data wrangling, data visualization, and reporting to effectively handle complex datasets and present data impactfully.

2. Develop statistics, math, and machine learning skills, understanding key concepts like calculus and linear algebra crucial for data science.

3. Acquire coding expertise, prioritizing languages like Python, R, SQL, and potentially Julia for handling data and databases effectively.

4. Grasp database fundamentals, particularly SQL, for efficiently managing structured data.

5. Learn to work with big data, utilizing cloud platforms like AWS, Microsoft Azure, and Google Cloud, alongside tools like Apache Spark for analysis and machine learning.

6. Gain practical experience, engage in communities, and work on data science projects to enhance your skills and network.

7. Pursue internships or job applications, showcasing a portfolio of skills and preparing for data scientist interviews.

8. Stay engaged with the data science community, staying updated on industry developments through social media, niche sites, blogs, and podcasts.

Educational Paths for Data Science Careers

Embarking on a career as a data scientist can stem from various educational backgrounds. While a bachelor's degree in data science, computer science, statistics, or related fields serves as an initial step, multiple pathways facilitate skill acquisition:

  • Online courses and certifications: Resources like Udemy & Coursera offer a structured learning approach and recognized certifications.
  • Undergraduate degree: A bachelor's degree in data science, computer science, statistics, or related fields provides a robust foundation.
  • Master's degree: For specialization or advanced skills, a master's degree in data science can be beneficial.
  • Data science bootcamps: Intensive training programs provide quick practical skill acquisition.

Closing Thoughts

The journey toward becoming a data scientist is both exciting and rewarding. Data science permeates numerous sectors, making a data scientist's role more pivotal than ever. Whether your motivations stem from intellectual curiosity, the promise of a lucrative salary, or the desire to leverage data for impactful decisions, a career in data science offers myriad possibilities.

As explored in this comprehensive guide, various pathways lead into this dynamic field. From formal education in computer science or statistics to self-directed learning through online courses, tracks, and certifications like those offered by online learning platforms like Udemy & Coursera.

The data science job market displays robust growth projections not just in terms of opportunities but also in diversified roles and specializations within the field. Whether you envision yourself as a data wrangler, a machine learning engineer, or a business intelligence analyst, the field offers a niche for diverse skill sets.

If you possess analytical skills, enjoy problem-solving, and are captivated by the potential of data-driven insights, now is an opportune moment to embark on a journey toward becoming a data scientist.

Computer Science vs. Cyber Security

Fields within science, technology, engineering, and mathematics (STEM) continue to evolve, and among them, computer science and cybersecurity offer diverse career prospects. If you're considering pursuing a degree in either computer science or cybersecurity, understanding the differences between these disciplines is crucial in making an informed choice.

Computer science, a broad field, encompasses the study of computers and their functioning. Cybersecurity, a subset of computer science, focuses on safeguarding networks, systems, and software from digital threats. Cybersecurity specialists rely on their deep comprehension of computers and networks to execute their responsibilities.

Continue reading to explore the distinctions between cybersecurity and computer science, including educational prerequisites and potential career pathways.

Comparison of Coursework: Cybersecurity versus Computer Science Degrees

Computer Science Courses

If you're passionate about programming, pursuing a computer science degree might interest you. Here are some courses often found in computer science programs:

  • Introduction to Computer Systems: Teaches principles behind engineering computer hardware and software, covering topics like virtual memory and file systems.
  • Data Structures and Algorithms: Explores algorithm theory and its practical applications in computing problem-solving.
  • Software Engineering: Provides practical application opportunities to design and test software systems.
  • Data Analysis: Covers probability, statistics, and data modeling.

Cybersecurity Courses

Interested in a career in cybersecurity? Here are some courses you might encounter in a cybersecurity degree program:

  • Cyber Policy, Law, and Privacy Compliance: Explores legal requirements concerning cybersecurity and privacy while devising security structures for organizations.
  • Database Design and Security: Covers database design and its implications for security breaches.
  • Cryptography: Focuses on algorithms, authentication, and secure data transfer.
  • Network Security: Equips students with skills in developing secure networks, including protocols and access control.

Roles and Responsibilities with a Cybersecurity versus Computer Science Degree

Upon completion of a cybersecurity or computer science program, professionals in both fields rely on a blend of technical and soft skills. Here's a closer look at their similarities and differences:


Both computer scientists and cybersecurity professionals pay attention to detail, maintain regular project logs, and employ analytical skills to devise innovative solutions.


The primary distinction lies in their roles. Cybersecurity specialists audit security systems, set up firewalls, and analyze networks for potential data breaches. Conversely, computer science professionals focus on software feature development, database administration, or web development.

Career Options in Cybersecurity and Computer Science

Careers in either field offer various opportunities, and transitioning between cybersecurity and computer science careers can be feasible due to overlapping educational backgrounds. Below are career options based on 2020 U.S. Bureau of Labor Statistics data:

Computer Science Careers:

  • Computer Network Architects (Median annual wage: $116,780)
  • Computer Systems Analysts (Median annual wage: $93,730)
  • Computer and Information Research Scientists (Median annual wage: $126,830)
  • Database Administrators (Median annual wage: $98,860)
  • Software Developers (Median annual wage: $110,140)

Cybersecurity Careers:

  • Information Security Analysts (Median annual wage: $103,590)
  • Network and Computer System Administrators (Median annual wage: $84,810)

Difference between Computer Science & Computer Engineering


The terms "computer science" and "computer engineering" are sometimes used interchangeably, but they're actually two separate fields. Understanding the differences between computer science and computer engineering is essential for students looking to choose between these career paths.

Let's dig into the distinctions between these fields to help you decide whether a career in computer science or computer engineering aligns better with your interests.

Firstly, what's computer science all about? This field primarily involves coding that integrates data, algorithms, statistical models, and more in an efficient way. For instance, computer science experts often create algorithms to perform complex tasks, like mimicking the human brain or finding the best route for an upcoming Uber ride.

On the other hand, computer engineering combines elements of electrical engineering and computer science. It focuses on researching how to construct various computing systems, from smartphones to integrated circuits.

When exploring academic programs related to computing, the terminologies used by different schools can be confusing. Some programs even merge computer science and computer engineering into a single department or major.

Understanding Computer Science:

Computer science deals with computational theory, emphasizing the virtual aspects of computers rather than their physical components. Aligned closely with mathematics, it applies theoretical concepts to solve real-world problems.

Courses in a computer science program cover topics like algorithm analysis, operating system principles, computer architecture, and software engineering. A computer science degree provides a foundation in both hardware and software, including aspects of computer organization and architecture.

For those interested in this field, exploring online graduate programs in computer science might be a good idea.

Understanding Computer Engineering:

In contrast, computer engineering concentrates on hardware rather than software. It teaches students how to construct devices through a blend of physics, electrical engineering, and computer science. Individuals curious about processes and keen on applying that knowledge to create new computer structures might be drawn to a computer engineering program.

Computer engineers focus on understanding how to use principles of physics and electronics to build improved computer components. Unlike software engineers, computer engineers typically spend more time in labs than writing code. A computer engineering curriculum might include topics such as computer architecture, computer networks, and physics. However, some computer engineering programs also cover programming skills and software design.

There are various career paths for computer engineers, ranging from aerospace and life sciences to mobile devices and robotics. If computer engineering interests you, exploring online master's programs in computer engineering can provide more insights.

Similarities and Differences:

Are you still wondering about the differences between computer science and computer engineering? Let's take a closer look at their core curricula.

Computer Science Courses:

  • Design and Analysis of Algorithms: Students learn how to analyze algorithms' efficiency using concepts like big O notation. They also learn to write optimized code and assess its efficiency.
  • Introduction to Operating Systems: This course covers how operating systems manage and execute code to run software. Topics include process and thread management, concurrent programming, and resources management.
  • Software Engineering: Students learn to apply computation theory to practical problems by creating software solutions. Topics include effective design, testing, and team-based development.
  • Data Analysis: This course equips students with tools to apply statistical methods to large datasets using computational techniques. It also covers data creation, storage, access, processing, and presentation.

Computer Engineering Courses:

  • Mechanics: This class delves into the core principles of physics, exploring laws governing motion, energy, and waves.
  • Electricity and Magnetism: This introductory physics course aims to educate students on forecasting, describing, and comprehending various electromagnetic occurrences shaping our world.
  • Calculus: Covering multivariable calculus and vector fields, this course equips students with techniques to describe and anticipate numerous natural and physical phenomena.
  • General Physics: This course offers an overview of fundamental physical phenomena, encompassing particles, work, gravitational forces, and motion.
  • Electric Circuits: Providing foundational knowledge in electronic system design, this class teaches students to construct functional circuits, comprehend computer architecture, and integrated circuits.
  • Waves, Optics, Thermodynamics: By applying calculus, this course examines wave mechanics and related phenomena. Students learn to compute wave energy, apply thermodynamic laws, and comprehend physics experiments.
  • Digital Logic, Machine Design: Students explore how electrical circuits execute logical operations, focusing on designing and building circuits employed in computing systems.


While computer science and computer engineering are distinct fields, there's some overlap. Professionals in both fields use computers to solve problems, relying on data and human interaction. Both fields also share many prerequisites and coursework.

Computer scientists and computer engineers often collaborate to create products, as both hardware and software are vital for computing systems. Businesses often rely on skilled professionals from both areas to develop well-functioning devices.

Career Outlook:

Computer scientists typically work in software development, computer programming, network and database design, web design, and testing, often involving frequent coding. Job titles for computer science graduates might include software developer, database administrator, web developer, or project manager.

Computer engineers focus on developing, designing, and manufacturing microprocessors, integrated circuits, and other computing systems. Graduates might work in telecommunications, robotics, aerospace technology, and other areas, taking up roles such as embedded software engineer, electrical design engineer, hardware engineer, or network engineer.

Job salaries and outlook: In May 2020, computer scientists earned a median salary of $126,830, while computer engineers earned a median annual salary of $119,560. The employment of computer scientists is projected to increase by 22% from 2020 to 2030, while computer hardware engineering jobs are projected to increase by 2%, according to the U.S. Bureau of Labor Statistics (BLS).

Choosing Between Computer Science and Computer Engineering:

The decision between computer science and computer engineering depends on your passions, skills, and career goals. Both fields offer opportunities to solve real-world problems through innovative solutions.

Once you choose a path, pursuing a bachelor's degree in computer science online or a computer engineering degree can help you learn foundational concepts to translate theory into practice.

Ultimately, the choice between computer science and computer engineering is yours to make.

6 Technical & Soft Skills for Every Data Scientist

A data scientist should possess a diverse range of both technical and soft skills. Although the relevance of certain skills might vary based on the specific field a data scientist operates in, most skills remain beneficial irrespective of the position. Acquiring proficiency in these diverse skills can aid in pursuing a career as a data scientist or enhancing one's array of experiences.

The following are some of the more common technical and soft skills for a data scientist to have:

Technical Skills:

  • Data Visualization
  • Programming/Software
  • Statistics/Mathematics

Soft Skills:

  • Communication
  • Business Sense
  • Problem-Solving with Data

Data Science Technical Skills

A data scientist needs a bunch of skills, but their technical know-how sets them apart. They have to get comfy with lots of technical skills and special tools. Different companies use different tools and languages, but all data scientists need a core set of tech skills that work for many problems. These core skills are crucial for every data scientist.

Data scientists use programming to do cool stuff like machine learning, AI, and data mining. They should get the hang of the math and stats behind these tricks to know when to use them. Besides the basics, data scientists should know the popular programming languages and tools used for these jobs. They also need to understand software engineering to put these languages and tools together.

1. Data Visualization

Knowing how to show data visually is a big deal for a data scientist. People understand patterns better when they see them. Data visualization does two key things: helps data scientists spot patterns in data and lets them tell a story with data. Both these things are super important in data science.

Stuff like scatter plots and histograms are vital for exploring data. Without visualizing data, it's hard to know where to start. And making sense of data only matters if you can share that sense with others. To do that, you need to present data in nice and useful pictures. The skill of telling stories with data needs data scientists to use data visualization creatively to explain their ideas. Without these tools, data science might not work well in bringing about change.

There are many tools for data visualization: most programming languages offer ways to show data. For example, Python uses Matplotlib and pandas. JavaScript has the D3.js tool. R has ggplot2 and more. Tableau is a platform for making data look good from lots of different places.

2. Programming/Software

Data scientists use loads of programming languages and software to handle, clean, analyze, and show data in smart ways. Even though new tools keep popping up, a few are always useful in the ever-changing world of data science. Here are six important tools that new data scientists should learn to get good at programming and software:
  • R: R was mostly for academics, but now big companies like social networks, banks, and media firms use it for stats, showing data, and predicting things. It's free and has been around for a long time, so there are lots of extra things you can add to it (called CRAN) for different data jobs.
  • Python: Python wasn't made for data stuff at first. But now it has the pandas library that helps store and work with data quickly. Big shots like Bank of America and Facebook use Python for data science. Python is friendly, fast, and easy to learn because it's been around for a while for general coding.
  • Tableau: Tableau from Seattle helps out data science tools like R and Python. It might not be great for cleaning data or making big changes, but it's awesome for playing with and showing data in cool ways. Tableau makes it easy to look at data with fun and interactive screens.
  • Hadoop: Hadoop is a free software system that splits up big data for lots of computers to work on together. It's good because it's flexible, powerful, and won't mess up if a computer breaks. It's run by the Apache Software Foundation and has tools like the Hadoop Distributed File System and a way to use the MapReduce way of programming.
  • SQL: SQL is a language for managing data in databases. There are different kinds, like MySQL, SQLite, and PostgreSQL. You can do a lot of the same things with SQL that you can do with R, Python, or even Excel. But writing your own SQL code can be quicker and make scripts you can use again.
  • Apache Spark: Like Hadoop, Spark splits up big data among many computers. But Spark is faster because it keeps data in the computer's memory. It's better than Hadoop’s MapReduce way, but it still needs Hadoop's File System.

3. Statistics/Mathematics

Computers do most of the hard math in data science these days, but a data scientist still needs to know which math test to use and what the results mean. They should know some calculus and algebra, which are the basis for lots of data tricks. Knowing stats helps them see what a method can do, what it can't, and what it assumes. A data scientist needs to know what things have to be true for a test to work.

Data scientists don’t just use fancy stuff like neural networks to learn things. Even basic things like drawing a line on a chart and understanding it are important first steps in data science.
Math things like logs and exponents come up a lot in real-world data. Knowing the basics and more advanced stats helps data scientists understand data better.

Though computers do most of the math work, understanding how they do it is still important. Data scientists have to know what to ask computers and how to get answers. Computer stuff is a lot like math, so knowing math helps data scientists write better and accurate code.

Data Scientist Soft Skills

Data science needs a mix of skills. It's a mix of science, math, computer science, business, and talking. Data scientists need different skills to work with numbers and to change decisions with data.

Because data scientists work with data to change how people make choices, they need to explain numbers to people who don't get the tech stuff. They have to make data stories that people understand and find interesting.

You can look at data scientist skills in different ways. Mitchell Sanders wrote a blog about it on Data Science Central. Looking at this can help you understand what it takes to be a data scientist. But Dave Holtz on the Udacity blog says that the job of a “data scientist” can be very different, asking for different skills. Holtz's blog breaks down four types of data scientist jobs and which skills are most important for each.

1. Communication

One big skill for data scientists is talking to people. To be good at data science, people need to understand the data. Data scientists help turn hard data into things people get. Even though cleaning, working on, and understanding data is a big deal in data science, this work is useless without talking about it in an easy way.

Talking well needs a few things. It starts with showing data in a good way. People understand data better when it looks good. This is key for both looking at data and explaining it to others.

2. Business Sense

Data science can be used in lots of fields. Each field has its own goals, data, and rules. To do a good job, data scientists should understand the business they're working in.

Knowing the business is key for effective data science. Data scientists need to know the field they're working in before they can understand data. Though some things, like profit and goals, are the same in all fields, lots of important stuff is different. This special data makes up the industry’s business smarts, which helps know where a business is at and how it got there.

Each field’s goals, needs, and rules decide what a data scientist does. Without knowing how an industry works, it’s hard to find good info or suggest helpful stuff.

A data scientist is best when they really get the business they're helping. Even though data can show things, it doesn't show everything. Data scientists with the same job title can have very different tasks depending on the field they're in. To be good, a data scientist should know the industry they're in.

3. Problem-Solving with Data

Using data to solve problems is key in data science. By using a smart way to find and explain problems, decisions can be easier. In data science, there are tons of ways to look at data. Knowing how to pick the best one is a big job for data scientists. Data science helps find problems and uses data to fix them.

A data scientist knows how to fix problems smartly. They look at what matters, ask the right questions, and pick the best ways to fix things, getting help from other people at the right times. They also pick the right data science tricks to solve the problem.

A data scientist’s job is to use raw data and make sense of it. This needs more than just understanding stats and machines. They also need to know about the problem they're working on, the info they have, and what they want to do.

Data science isn't easy. There are lots of ways to look at stuff, and it's easy to get stuck. A smart way to solve data problems helps data scientists keep track of what they're doing. Things like Six Sigma are good tools to help data scientists and teams fix real data problems.

Best Programming Languages to Learn

When you're choosing which computer language to start learning, think about what you want to do with it. There are different types of languages—some are a bit harder but let you control more, while others are easier to learn but might limit your control over what you make.

Picking a language depends on what you want to achieve. If you want to create websites, you might begin with languages like JavaScript. If you're interested in hardware or graphics, more complex languages like C could be better. For computer science, it's good to know a few different languages.


Guido van Rossum created Python in 1991, but its ascension gained momentum recently, becoming the fastest-growing language, as per Stack Overflow data. Renowned for accessibility and adaptability, Python finds utility in data science, aiding data analysts in managing and processing substantial datasets. It also serves as a cornerstone for web development, software engineering, algorithmic learning, robotics, and financial computations. Notably, Python holds prominence in the financial technology (fintech) sector, outweighing Java in demand, according to HackerRank.

YouTube | Python for Beginners (Free Course)


Hatched at Google in 2009, Go is revered for efficiency akin to C and C++, yet it simplifies intricate syntax concerns. Ideal for systems programming, it finds applications in crafting servers, data pipelines, and machine learning packages.

YouTube | Learn Go Programming | Free


Java, celebrated for its compatibility and versatility, stands tall as a high-level language catering to app and software development. Notably, it serves as the default language for Android app development, boasting inherent security features. Its origins trace back to cell phone development, embracing cross-network data exchange. Java's static typing distinguishes it from dynamically typed languages like Ruby, Python, and JavaScript. Its robustness favors large-scale enterprise applications, finding favor in expansive corporate environments. However, its steep learning curve often necessitates professional guidance or formal education.

YouTube | Java Programming for Beginners | Free


Toppling the charts as the most prevalent language, JavaScript shares mere nomenclature with Java, sans any technical relationship. Functioning in tandem with CSS and HTML, it imparts interactivity to webpages and applications. Its prowess extends to crafting mobile and web apps, fabricating web servers, server applications, and even game development.

YouTube | Learn Javascript for Beginners | Free


Crafted exclusively for Apple and Linux ecosystems, Swift—birthed in 2014—holds sway in app development, specifically tailored for iOS and macOS platforms. Valuing safety and expressive syntax, Swift stands as a swift and secure alternative programming language choice.

YouTube | Learn Swift for Beginners | Free

C and C++:

C, a programming veteran since 1972, forms the cornerstone for many successor languages, including Java, JavaScript, and C++. Akin to C with additional features, C++ serves as an extension. Favored in computer science and performance-centric arenas like gaming, their understanding necessitates comprehension of computer memory mechanisms. While C forms a bedrock for several programming languages, some opt for modern alternatives, deeming it archaic. Comparatively, C++ garners recognition for being more beginner-friendly.

YouTube | C++ Tutorial for Beginners | Free


Functioning as a statistical modeling tool, R creates an environment conducive to applying statistical techniques. Boasting prowess in statistical computing, modeling, and graphical representation of data findings, R finds favor in data science, machine learning, and statistical computations, paralleling Python as a foundational data science skill.

YouTube | Learn R Basics | Free


Renowned as a scripting language, Ruby—through its framework Ruby on Rails—forges pathways in website design. Notably, platforms like Airbnb deploy Ruby on Rails in specific iterations. Renowned for simplicity, Ruby finds support within a vast learning community and transcends website development to encompass prototyping and data analysis.

YouTube | Learn Ruby Full Course | Free


Structured Query Language (SQL), conceived in 1979 by IBM, stands as a specialized language catering to data management within databases. Its forte lies in structuring data across varying datasets, facilitating swift data insertion, modification, updating, deletion, and efficient querying—an invaluable asset for data scientists.

YouTube | Learn SQL | Free


As a general-purpose scripting language, PHP predominantly thrives in web development, synergizing effectively with HTML. Easing HTML inputs, PHP—a recursive acronym for PHP: Hypertext Preprocessor—exhibits simplicity for novices while harboring complex features for seasoned programmers. Emulating syntax from C, Java, and Perl, PHP executes code on servers, generating HTML, diverging from JavaScript, which executes in browsers through direct HTML embedding.

YouTube | Learn PHP | Free

5 Powerful Questions to Ask an Interviewer in a Job Interview

As the end of every job interview draws near, the interviewer will likely ask "Do you have any questions for me?" It's surprising how many job candidates respond with a simple "No." After all, you are considering spending a significant amount of time at this company, it makes sense to have some questions. The issue is that some people believe that this invitation to ask questions is a trap, another way for the interviewer to evaluate them. They're concerned that their inquiries may seem overly demanding or unprofessional, or that they need to choose questions that will make them appear as the best candidate. In some cases, they simply don't know how to gracefully ask the questions they really want to know, such as "What are you like as a boss?" or "Is everyone here unhappy?"

So, what should you ask during the interview when it's your turn to evaluate the interviewer? Here are ten powerful questions that will give you valuable insights into whether the job is right for you.

1. What challenges do you expect the person in this role to face?

This inquiry can uncover information that wouldn't be revealed by the job description - like the existence of complicated interdepartmental dynamics, compatibility issues with a key colleague, or strict budget restrictions for the program.

Additionally, it provides an opportunity for you to share your past experiences in handling similar difficulties, which can give assurance to the interviewer. Don't merely ask questions as a means of promoting yourself - this can come off as annoying and transparent. However, if the discussion of challenges leads to an honest exchange on how you would tackle them, it can be beneficial for both parties.

2. How will the success of the person in this role be determined?

This brings you straight to the core of what is essential to understand about the job: what constitutes success, and what do you need to accomplish for the manager to be satisfied with your performance?

You may assume that the job description provides this information, however, it is not uncommon for a job description to be outdated, even if the job has changed significantly over time. Companies often use generic language from HR for their job postings, while the actual manager may have different priorities for the role. Furthermore, many employers simply struggle with writing effective job descriptions, resulting in language that sounds impersonal and robotic. To get a clearer understanding of the role, it's beneficial to have a direct conversation with the manager about what the job truly entails. You may discover that while the job posting lists numerous responsibilities, your success is dependent on only a few of them, or that the posting undervalues the significance of one key responsibility, or that the manager is still negotiating with their own supervisor on the expectations for the role, or even that the manager is uncertain about what success in the job would look like (in which case it would be wise to proceed with caution).

3. How long did the previous person in this role stay and what has been the general turnover rate in the role?

It is crucial to ask this question as a pattern of high turnover in the position could indicate negative factors such as a difficult manager or unrealistic job expectations, which could potentially lead to an unpleasant work experience for you as well. If only one person has left the position quickly, it may not be a cause for concern, but if multiple individuals have departed in a short period, it is important to inquire with your interviewer about the reasons behind this trend. If the position is a newly created role, it is not possible to ask about the turnover rate. In such cases, inquiring about the overall turnover rate for the team can be an alternative.

4. Can you describe a typical day or week in the role?

It's crucial to ask about the allocation of your responsibilities in the job, specifically if the job description mentions a combination of administrative and program work. You need to find out whether the majority of your time will be devoted to administrative tasks or if it is more balanced. Additionally, you should also inquire about the frequency of the responsibilities that you are most interested in. Knowing this information can give you a clearer idea of what it would be like to work in the job on a day-to-day basis.

A helpful tip: If the interviewer replies with "Every day is different," try asking for a description of the last month's workload of the current employee in the role. What took up most of their time?

However, if you are unable to get a clear understanding of how your time will be spent, it may indicate that the job is disorganized or that expectations are not well-defined, which could be a warning sign.

5. What sets apart the individuals who excelled at this role compared to those who simply performed well?

This question is important in getting to the core of what the hiring manager is seeking in a candidate. They want someone who will stand out and excel, not just perform averagely. By asking this question, you show that you are also passionate about making a positive impact and being exceptional in the role. Your keen interest and drive are important qualities that hiring managers look for in candidates. Additionally, the answer to this question can provide a more detailed understanding of what it takes to excel in the job, allowing you to evaluate if it aligns with your strengths and abilities.