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How to become a Data Scientist: Your Roadmap to Self-Taught Data Science (It’s not as scary as it sounds!)
Ever felt like you’re drowning in a sea of data, longing to transform it into shimmering insights? Well, fear not, intrepid explorer! The world of Data Science might seem like a tangled jungle, but with the right tools and a thirst for knowledge, you can be swinging from vine to vine like a pro in no time.
And guess what? You don’t need a fancy degree. How do I know it? I spent two years buying online courses, getting certificates, and competing on Kaggle. Now, I am a data scientist and have 5 years of experience. In this article, I’m going to share my path and give you some insights about ‘How to Become a Data Scientist?’.
We are going to talk about both my and your roadmap. But, firstly we need to clear out few things and answer the main questions:
What is Data?
What is Data Science?
What does a Data Scientist do?
What Qualifications Do Data Scientists Need?
What is the difference between Data Scientists and Data Analysts?
Is It Realistic to Become a Self-Taught Data Scientist?
What is Data?
Data can be in the form of numbers, text, images, or any format. It actually means unprocessed information and doesn’t have a meaning or context by itself. That’s where the artist – the Data Scientist – comes in. The artist collects, processes, organizes, interprets the data to make decisions, derive and communicate the insights.
Think of data as ingredients in a recipe. Each ingredient—flour, sugar, eggs—represents a piece of information. Individually, they might not make much sense, but when combined in the right proportions and order, they create a delicious cake. Similarly, data, in its raw form, may seem random or confusing, but when organized and analyzed, it turns into something meaningful. Never thought I would imagine myself as a cake chef.
What is Data Science?
Data Science represents the art and science of extracting valuable insights and knowledge from data by collecting, organizing, analyzing and interpreting it. It encompasses a wide array of techniques, including data analysis, machine learning, and data visualization, all geared toward solving intricate business problems and uncovering concealed patterns. Data scientists play a significant role in metamorphosing data into actionable insights that enhance business growth.The essence lies in the adept use of diverse tools and techniques to convert chaos into a structured order.
What Does a Data Scientist Do?
As a Data Scientist, your responsibilities span a diverse spectrum, encompassing tasks like data collection, data cleansing, exploratory data analysis, machine learning model development, and data visualization. You’ll be an integral part of cross-functional teams, collaborating to tackle real-world problems and making data-driven decisions that can substantially influence an organization’s success. You will translate the data (chaos) to actionable insights.
We can actually consider Data Scientists as detectives, and the dataset as the crime scene. There are pieces of evidence in your dataset waiting to be examined.
Rather than solving crimes, Data Scientists unravel the mysteries that lie underneath data. If you become a Data Scientist, you can actually call yourself Sherlock Holmes who uses analytical skills to derive patterns, solve problems, and uncover insights. The excitement of solving these kinds of problems, and the satisfaction of finding the answers still gives me goosebumps.
If you want to read main roles of a data scientist in detail, click here.
Data Scientist vs. Data Analyst
I really enjoyed using the Sherlock Holmes analogy for Data Scientists. Let’s continue with that.
In the world of Sherlock Holmes, Dr. Watson is the lifelong partner. But, in the world of Data Science, Dr. Watson is the Data Analyst since he observes and records the details, whilst assisting Holmes in solving immediate mysteries. The Data Analysts work with existing data, analyzing it closely, and providing insights that would support current decision-making.
Now, picture Sherlock Holmes as the Data Scientist. Holmes doesn’t just solve the case at hand; he is a master of deduction, predicting future crimes and understanding complex patterns. Likewise, a Data Scientist usually goes beyond the present while using advanced methods and tools to predict/forecast future trends. You can find our detailed post here.
What Qualifications Do Data Scientists Need?
Educational Background
The fact that I graduated from Management Information Systems gave me a good start. During my studies, I had courses about finance, accounting, e-commerce, programming, business, human resources and since data is everywhere, those courses gave me the basic domain knowledge and it was very helpful in the beginning of each project I worked on. Because everything starts with understanding the domain and understanding the data. If you know the field, you will know the terms, if you know the terms, you will have a good understanding of the columns in the dataset.
However, data scientists are coming from diverse degrees including Computer Science, Physics, Mathematics, Industrial Engineering, Statistics.
Technical Background
For beginners, the most critical programming languages are SQL, Python, R and increasingly Julia. The inevitable one is SQL. But typically, people are choosing in between Python, R, or Julia. In my opinion, you don’t have to choose in between. I think the visualization library of R (ggplot, ggplot2) is way more controllable and easy to use compared to visualization libraries of Python (matplotlib, seaborn). But for other things such as data manipulation, data cleaning, machine learning modeling, I prefer Python. You can choose whatever you feel comfortable with. But, Python is more commonly used than R and Julia. All of these languages will be explained below.
SQL
Imagine you have a vast library filled with books on various topics. To find the specific information you need, you wouldn’t just rummage through every book randomly. Instead, you would use the library’s cataloging system to locate the books relevant to your search. This cataloging system is similar to how SQL functions in the realm of data science.
SQL, or Structured Query Language, is the language used to interact with and manage data stored in relational databases. Just like a library catalog organizes books by author, genre, and other categories, SQL allows you to organize, manipulate, and retrieve data from databases efficiently.
R
R is a powerful programming language widely used in data science for statistical computing, data visualization, and developing machine learning algorithms. It provides a rich ecosystem of packages and tools that empower data scientists to explore, analyze, and transform data into meaningful insights.
Python
Python is a general-purpose programming language that has become a mainstay in the field of data science due to its versatility, ease of use, and extensive ecosystem of libraries and tools. It empowers data scientists to manipulate, analyze, and visualize data to extract meaningful insights and solve complex problems.
Julia
Julia is a high-level, general-purpose programming language designed for scientific computing. It is a relatively new language, but it has already gained popularity in the data science community due to its speed, performance, and ease of use. Julia is often compared to other popular data science languages like Python and R, but it has several advantages over these languages. Julia is faster than Python and R, and it has a more powerful syntax that makes it easier to write complex code.
Is It Realistic to Become a Self-Taught Data Scientist?
Absolutely! My journey started with my cousin recommending me to do some research about “Data Mining” (that’s what it was called back in the day) and I fell in love with the field immediately. I started with taking optional courses about Data Science and was lucky enough to have a Statistics course where we used R. Also wrote my bachelor’s thesis in Data Science. But, after graduation there weren’t too many opportunities for internships. That’s why I needed to start by myself. Of course, I started with Udemy. I started with the most popular course about Data Science which is called Machine Learning A-Z. It starts from the scratch and explains everything. Then implement it both in Python and R. My whole perspective about Data Science was changed immediately. After that, I kept getting certificates from both Udemy and IBM’s Cognitive Class. Then, I proceeded with the Kaggle competitions. So, it is absolutely possible and I think it is the best way. I believe learning by struggling is the best way. You can see my roadmap below:
My background in Management Information Systems (MIS) provided me with a solid foundation in statistics and data analysis, giving me a head start in my journey to become a data scientist. But don’t let that intimidate you! In this article, I’ll break down the essentials of data science in a way that’s easy to understand, even if you’re a complete beginner.
When I first started learning data science, I was overwhelmed by the sheer amount of information available. That’s why I highly recommend starting with IBM Cognitive Classes. Unlike other online courses, IBM Cognitive Classes focuses on the fundamental concepts of data science, giving you a comprehensive understanding of the field before you dive into the technical aspects.
Practice is the key to mastering any skill, and data science is no exception. Make sure you complete every assignment IBM throws your way – don’t skip a single one! Once you’ve got the basics down, it’s time to take your skills to the next level.
Kaggle is the go-to platform for data scientists to test their skills and learn from others. With a plethora of free competitions available, you’ll have ample opportunities to apply your knowledge and gain valuable experience. Don’t worry about winning or losing; the goal is to hone your skills and learn from different approaches. This will give you the opportunity for gaining real life experiences with the most used libraries of Python.
Participating in Kaggle competitions not only helps you improve your coding skills but also allows you to build a portfolio of your work, showcasing your expertise to potential employers. After tackling a few different datasets on Kaggle, you’ll gain a deeper understanding of the data science project lifecycle.
With a solid foundation and hands-on experience under your belt, you’re ready to tackle real-world competitions. By observing different coding approaches, you’ll continue to refine your skills and gain insights into industry best practices. Kaggle played a pivotal role in my journey, teaching me how to effectively utilize data science libraries in Python.
As you can see, it wasn’t easy. This is a very high-level explanation of my journey. There are probably hundreds of steps in between. The main reason behind this indecisiveness was not having a mentor. So, I was struggling to decide which way to go and what to do. That is the main reason I decided to start this blog. Helping beginners to understand what data science is and how to get into it. Now, let’s get started with your roadmap.
Step 1: Laying the Groundwork – Math & Stats for the Win!
Before we embark on our data safari, let’s pack some essential tools – a solid foundation in math and statistics! Now, don’t get intimidated by visions of Einstein scribbling on chalkboards. We’re not aiming to become math ninjas; we just need enough firepower to navigate the data jungle. I genuinely believe that the business side of things are as important as the technical side of the things. You might be amazing technically, but it doesn’t really matter if you cannot explain them. So, trying to keep the balance between both sides is the best approach, in my opinion.
Think of it like learning the language of the animals in this wild place. Numbers are their clicks and whistles, probability their roars and growls. Without understanding these basics, we’ll be lost in a cacophony of confusion.
Don’t get intimidated! I got you. We have plenty of resources that will get you conversationally fluent in a short period of time.
Khan Academy: Your friendly neighborhood online tutor, offering bite-sized lessons on everything from algebra to calculus. Think of it as a data bootcamp for beginners, delivered with humor and patience.
Coursera MOOCs: Want to dive deeper? These structured courses, like “Statistics for Data Science” or “Python for Everybody,” are like guided expeditions led by expert rangers. You’ll learn the lay of the land, encounter diverse data creatures, and return with a backpack full of knowledge.
“Naked Statistics” by Charles Wheelan: This book is your witty companion on the journey. Forget dusty textbooks – Charles makes stats fun and relatable, like a campfire chat with a wise old data guru. He’ll show you how these seemingly abstract concepts play out in everyday life, making you laugh while you learn.
Remember, the key here isn’t to become a formula-memorizing parrot. We’re after a deeper understanding, the “aha!” moments when a concept clicks and the data starts whispering its secrets to you. So, don’t be afraid to get your hands dirty, experiment, and practice. The more you play with numbers and data, the faster you’ll speak their language.
With this math and stats toolkit in your arsenal, you’ll be ready to face any data beast, from taming messy spreadsheets to uncovering hidden trends. It’s like having a built-in compass, guiding you through the jungle and towards valuable insights.
So, let’s get started! In the next step, we’ll meet your coding sidekicks, Python and R, who will help you wrangle this data menagerie with ease. Stay tuned, adventurer, the data jungle awaits!
Step 2: Coding Your Way to Insights – Python & R, Your New BFFs
Now that you’re speaking the language of data, it’s time to meet your coding comrades: Python and R! These two rockstars aren’t just any jungle guides – they’re your Swiss Army knives for wrangling, analyzing, and extracting those sweet, sweet insights from the data wilderness.
Don’t worry, they’re not scary, hairy beasts (unless you count their curly braces and squiggly symbols). They’re actually quite friendly once you get to know them. Think of Python as the laid-back, versatile adventurer, while R is the focused, stats-loving scholar. They both have their strengths, and together, they’re an unstoppable data-crunching duo! As I mentioned earlier, R is my way to go for Data Visualization. And, I complete other steps of the project in Python.
But where do you start? Buckle up, because we’re going on a whirlwind tour of free learning platforms:
Kaggle Learn: Your one-stop shop for interactive Python tutorials, perfect for beginners. Think of it as a jungle gym where you can learn by doing, tackling bite-sized challenges and conquering coding puzzles.
Codecademy: Learn Python the fun way, with gamified rewards and bite-sized coding adventures. It’s like playing a video game, but instead of saving princesses, you’re saving data from messy dungeons!
DataCamp: Feeling ready to graduate from Python bootcamp? DataCamp offers structured courses and guided projects, taking you from data wrangling basics to advanced topics like machine learning. It’s like having your own data guru mentor, leading you on a personalized learning expedition.
Start small, like making friends with variables and data structures (think of them as your jungle supplies). Then, gradually learn the superpowers of libraries like Pandas and NumPy. These guys are like your muscles, able to manipulate and analyze massive datasets with ease. Imagine cleaning messy spreadsheets in seconds, visualizing trends like a pro, and even building basic models to predict future events – all thanks to your coding comrades!
Here’s a sneak peek of what you can achieve:
Taming the data beast: No more drowning in spreadsheets! Python and R will help you organize, clean, and structure your data, transforming chaos into a well-maintained jungle trail.
Seeing the unseen: Unleash the power of data visualization! Create stunning charts and graphs that reveal hidden patterns and trends, like a cartographer mapping the secrets of the data landscape.
Building your own mini-jungle: Feeling adventurous? Learn how to build basic models that can predict customer behavior, analyze financial trends, or even recommend the next movie you’ll love. It’s like having your own data oracle, whispering insights into your ear!
Remember, coding isn’t just about memorizing lines of code – it’s about understanding the logic, the flow, the magic behind it. So, experiment, ask questions, and don’t be afraid to get creative with your data. The more you practice, the deeper you’ll connect with your coding companions, and the more powerful your data toolkit will become.
In the next step, we’ll unlock the secrets of SQL, your database decoder ring! Get ready to navigate the hidden vaults of information and extract the gems within. Stay tuned, fellow explorer!
Step 3: Taming the Data Beast – SQL & Databases
Imagine a vast jungle, teeming with life, but its secrets locked away in hidden vaults. That’s where databases come in, and SQL is your magical decoder ring, granting you access to the treasure trove of data within!
While Python and R are your wrangling and analysis rockstars, SQL is your database whisperer. It’s the language you use to talk directly to these data fortresses, asking them questions, filtering information, and unearthing the gems hidden inside.
But don’t be intimidated by the thought of learning a new language. Think of it like learning the dialect of the database guardians. It’s not about memorizing complex incantations, but understanding a few key phrases to get the job done.
Here are your friendly online tutors to get you started:
SQLBolt: Gamified platform that makes learning SQL interactive and addictive. Think of it as a data dungeon crawler, where you level up your SQL skills by solving puzzles and rescuing valuable information.
SQLZoo: Practice your SQL chops with bite-sized challenges, ranging from beginner-friendly queries to brain-bending puzzles. It’s like a data obstacle course, testing your understanding and pushing you to think outside the box.
HackerRank: Feeling competitive? Put your SQL skills to the test with real-world challenges and compete with other data enthusiasts. It’s like a data gladiator arena, where you hone your skills and emerge victorious with newfound knowledge.
Start with the basics – the “SELECT,” “JOIN,” and “WHERE” of the database world. These are like your essential jungle survival tools, allowing you to retrieve specific data, connect different tables, and filter out irrelevant information.
Once you’ve mastered these fundamentals, the jungle opens up! You can ask complex questions like:
“What are the top 10 products purchased by customers under 30?”
“Show me the correlation between website traffic and social media engagement.”
“Predict which customers are most likely to churn and suggest targeted promotions.”
With SQL, you’re not just a passive observer – you’re an active data chef, preparing your ingredients for analysis. You can clean, transform, and organize your data, making it easier for Python and R to work their magic.
Remember, the key is practice, experiment, and don’t be afraid to get creative with your queries. The more you explore the database jungle with SQL, the more insights you’ll uncover and the more powerful your data toolkit will become.
In the next step, we’ll delve into the fascinating world of machine learning, where algorithms learn from data and make predictions. Get ready to turn your data into a crystal ball, fellow explorer!
Step 4: From Cleaning to Conclusions – Machine Learning Demystified
We’ve wrangled the data, tamed the databases, and now we’re ready to embark on a truly magical journey: the world of Machine Learning (ML)! Imagine algorithms that learn from data, evolving like creatures in the jungle, uncovering hidden patterns and whispering predictions in your ear. Buckle up, because things are about to get fascinating! Still gives me goosebumps!
Let’s start with the basics. Supervised learning is like having a wise elder in the jungle, teaching the algorithms what to look for. We show them labeled examples (“This is a cat,” “This is a dog”), and they learn to identify similar features in new data, classifying emails as spam or predicting house prices based on past sales. Libraries like TensorFlow and scikit-learn are your friendly guides in this supervised world, offering tools and tutorials to build your own prediction machines.
But the jungle holds deeper secrets. Unsupervised learning is like exploring uncharted territory, where the algorithms are free to roam and discover hidden patterns on their own. Imagine them clustering customers into different groups or reducing complex data into manageable dimensions, revealing relationships and trends we never knew existed.
Remember, the goal isn’t to blindly memorize formulas or algorithms. We want to understand the “why” behind them, how they learn, and how they can be our partners in unlocking the true potential of data. Think of it like learning the language of these jungle creatures, not just mimicking their calls.
Now, let’s see this magic in action! Imagine:
Predicting customer churn before they even say goodbye. Your ML model, trained on past behavior, can identify customers at risk and you can tailor your marketing efforts to win them back.
Recommending products that customers will love, even before they know they want them. Your ML engine analyzes purchase history and preferences, creating a personalized shopping experience that feels like serendipity.
Personalizing education to fit each student’s needs. ML algorithms can identify learning styles and struggles, recommending targeted content and activities for optimal growth.
The possibilities are endless, fellow explorer! With ML, you’re not just a data observer – you’re a data alchemist, transforming raw materials into insights that can change the world.
In the next step, we’ll take your data skills from solo adventurer to collaborative hero. We’ll explore the power of online communities, personal projects, and the never-ending journey of learning. Stay tuned, the data jungle awaits your next adventure!
Step 5: Practice Makes Perfect – Your Data Playground Awaits!
Alright, adventurer, you’ve acquired the tools, tamed the beasts, and unlocked the secrets of the data jungle. Now, it’s time to unleash your inner Indiana Jones and embark on your own data expeditions!
Forget dusty textbooks and theoretical lectures – the real learning happens in the wild. It’s time to roll up your sleeves, grab your data machete, and dive headfirst into personal projects. Think of it like building your own data oasis in the jungle, a place to experiment, fail, and ultimately, succeed.
Where to start? Don’t worry, the data world is overflowing with playgrounds:
Public datasets are your jungle gym: Kaggle, the UCI Machine Learning Repository, and countless other platforms offer treasure troves of data, from weather patterns to movie ratings. Choose a topic that sparks your curiosity, be it predicting election results or analyzing social media trends.
Personal challenges are your hidden waterfalls: Want to track your fitness progress or optimize your budget? Use your data skills to tackle real-world problems that matter to you. Imagine building a tool that recommends healthy recipes based on your workout data or predicting your next big expense to avoid financial surprises.
Remember, there’s no “right” project. It’s all about learning by doing. Embrace the messiness, the roadblocks, and the inevitable stumbles – they’re your stepping stones to mastery.
But you don’t have to go it alone! The data jungle is teeming with friendly faces:
Online communities like Kaggle forums and Reddit’s r/datascience are your data campfire gatherings. Share your progress, ask questions, and get support from fellow explorers who understand the thrill and frustration of the data quest.
Local meetups are your hidden oases. Connect with data enthusiasts in your city, collaborate on projects, and learn from each other’s experiences. Imagine brainstorming ideas over pizza or sharing war stories about buggy code – the data camaraderie is real!
Most importantly, celebrate the small wins! Did you finally wrangle that messy dataset? Did your model make its first accurate prediction? Every step forward is a victory. Learn from your failures, analyze your mistakes, and keep iterating. Consistency is the key to unlocking your full data potential.
So, adventurer, go forth and conquer your data playground! Remember, the jungle holds endless possibilities, and the only limit is your imagination. Keep learning, keep exploring, and keep sharing your data love with the world. The future of data belongs to the curious, the collaborative, and the ones who never stop playing.
And who knows? Maybe one day, you’ll be the one writing the blog post, inspiring the next generation of data explorers to conquer their own data jungles! But, please give me credit 😀
Additional tips:
Develop strong domain expertise: While technical skills are crucial, data scientists also need a deep understanding of the specific domain or industry they intend to work in. This could involve studying relevant business concepts, understanding industry trends, and familiarizing yourself with the types of data commonly used in that domain.
Be curious and have a strong work ethic: The path to becoming a data scientist is not always easy. Be prepared to face setbacks, learn from mistakes, and keep pushing forward. Your dedication and perseverance will pay off.
Follow every possible page on LinkedIn and other social media platforms. This will help you to get familiar with Data Science vocabulary and see how people approach the problems. It is a highly competitive field and you need to keep yourself up-to-date to gain competitive advantage.
Practicing the communication skills: Since Data Science is highly related with the Business side, data scientists need to be able to communicate their findings in a way that is understandable to both technical and non-technical audiences. You might be highly skilled in a technical way, but you need to practice explaining complex concepts in a simple and engaging manner, and tailor your communication style to suit your audience.
Conclusion: Key Takeaways
The world of data science is rapidly evolving and there is a high demand for skilled data scientists. While there is no one-size-fits-all path to becoming a data scientist, the steps outlined in this article can provide a solid foundation for your journey. By developing the necessary skills and experience, you can position yourself for success in this exciting and rewarding field.
Data science is a multifaceted field that requires a combination of technical skills, business acumen, and communication expertise.
Start by getting familiar with the basics of data science, including statistics, SQL, and programming languages like Python, R, and Julia.
Practice your data science skills by participating in online competitions, contributing to open-source projects, and working on real-world datasets.
Develop domain expertise in the area where you want to apply your data science skills.
Be passionate about data and have a strong work ethic to navigate the challenges and opportunities of this dynamic field.
Remember, becoming a data scientist is a journey, not a destination. Continuous learning and self-improvement are essential to stay ahead of the curve and make significant contributions in this ever-changing field.
