Kickstart Your Data Science Journey —  A Guide for Aspiring Data Scientists

Kickstart Your Data Science Journey — A Guide for Aspiring Data Scientists

Key Technical Skills You Need to Kick-start Your Career in Data Science

Are you curious about data science? Does math and artificial intelligence excite you? Do you want to explore data science and plan to pursue a data science career? Whether you’re unsure where to begin or just taking your first steps into data science, you’ve come to the right place. Trust me, this guide will help you take your first steps with confidence!

Data science is one of the most exciting fields in which to work. It’s a multidisciplinary field that combines various techniques and tools to analyze complex datasets, build predictive models, and guide decision-making in businesses, research, and technology.

Data science is applied in various industries such as finance, healthcare, social media, travel, e-commerce, robotics, military, and espionage.

Myths and Truths about Data Science

The Internet has abundant information about how to start with data science, leading to myths and misconceptions about data science. The two most important misconceptions are —

1. Learn Maths or not?— Many online courses and boot camps advertise that you can become a data scientist in 50 days! These courses are often misleading. They focus on advanced machine learning (ML) topics, a few quick coding tutorials using ML frameworks, and tell you not to get into the nitty-gritty mathematical details. It’s not okay not to worry about the mathematics behind it. Maths is important. Importing libraries, treating models like a black box, and relying on high-level APIs isn’t really data science, especially in product-based companies.
2. Is Data Science equal to Large Language Models/Generative AI?— No. Data science is not synonymous with large language models (LLMs)/Generative AI. Data science spans far beyond LLMs and encompasses a variety of tools and algorithms. LLMs are groundbreaking but are not suited for solving every academic, research, or business problem. LLMs are one tool among many and shouldn’t define an entire skill set.

Data scientists require a strong grasp of mathematics. It’s important for someone starting their data science journey to focus on mathematics and fundamentals before diving into fancy stuff like LLMs. I’ve stressed the importance of fundamentals throughout this article. The knowledge of basic concepts will help you stand out from the crowd of data science aspirants. It will help you ace this career and stay updated with the developments in this rapidly growing field. Think of it as laying a building’s foundation. It takes the maximum time and effort. It’s essential to support everything that follows. Once the base is solid, you can start building upwards, floor by floor, expanding your knowledge and skills.

What is expected from you?

1. Patience — Becoming a data scientist is a long, challenging, and tedious journey. Patience is key. Be ready to deal with a few struggles.
2. Passion — Passion drives success. Your curiosity and enthusiasm for data and problem-solving will fuel your progress.
3. Growth Mindset — Data science is a vast and rapidly evolving field. Embrace the mindset of continuous learning. Always seek to improve and stay updated.
4. Think from first principles — Thinking from first principles is the thumb rule in any profession. It helps you solve problems by breaking them down to the basics and building solutions from the base.
5. Consistency — Consistent efforts compound into grand success. Take small steps constantly.

Knowing where to start might seem overwhelming if you’re a beginner. With so many tools, concepts, and techniques to learn, it’s easy to feel lost. But don’t worry!

In this article —

• I will explore the role of a data scientist within an organization and highlight their key responsibilities and contributions.
• I’ll discuss the most fundamental technical skills you need to kick-start your career in data science.
• I’ll explain why these skills are important.
• I’ll share valuable resources to help you learn and develop these skills.

Let’s get started!

Job Description of a Data Scientist

• Define the Problem Statement — A data scientist’s role starts by identifying and solving business challenges using data-driven methods and predictive modeling. The first step involves collaborating with product managers and subject matter experts to define a clear and precise problem statement.
• Exploratory Data Analysis and Training Models — Data scientists perform data analysis to identify the underlying issues. Once the problem is defined, the next step is to gather and explore the appropriate data required to train an ML model. This is where they apply their primary data science skills and judgment to obtain a robust model.
• Model Evaluation — Data scientists play a crucial role in developing and tracking evaluation metrics to quantify the success of ML models. For instance, in an e-commerce recommendation system, these metrics could measure the model’s impact on sales, user engagement, or revenue growth. Defining the right metrics ensures that the model aligns with business objectives and can deliver meaningful value to the business.
• Model Deployment and A/B Testing — Once the model is ready, you work closely with engineers to deploy it into production. They conduct A/B testing to validate the model’s effectiveness and scale the model for larger use. They monitor its performance over time.
• Research and Experiment— Data scientists continuously experiment with innovative ideas to improve their models. Staying up to date with the latest research is essential. Reading research papers provides insights into new methodologies, algorithms, and breakthroughs.

The following technical skills are necessary.

1. Mathematics — Linear Algebra, Probability, Statistics, and Calculus
2. Machine Learning Fundamentals
3. Coding — Python and SQL

1. Mathematics

Mathematics is everywhere. No doubt it’s the backbone and the core of data science. A good data scientist must have a deep and concise understanding of mathematics. Mastering mathematics will help you

• Correctly explore, analyze, and interpret large noisy industrial datasets.
• Extract meaningful conclusions from data.
• Grasp the foundational principle behind any ML model you want to use.
• Tweak the model (model hyperparameters, neural network architecture, loss function) based on your requirements.
• Choose the appropriate ML and business metrics to evaluate the models you built.
• Generate a feedback loop to detect possible scenarios where the model might fail.
• Perform error/root cause analysis to understand model flaws.

Without mathematical understanding, you’ll have difficulty unboxing the black box. The following topics are super important.

1.1. Linear Algebra

Linear Algebra is a beautiful and elegant branch of mathematics that deals with vectors, matrices, and linear transformations. Linear Algebra concepts are fundamental for solving systems of linear equations and manipulating high-dimensional data.

Why is it required?

• In industry, data at scale is inherently high-dimensional. Linear algebra provides the mathematical foundation to represent, store, and efficiently manipulate this data using vectors and matrices. Data transformations, projections, and optimizations can be performed easily by leveraging linear algebra concepts like linear transformations, determinants, orthogonality, and rank.
• For instance, dimensionality reduction techniques like principal component analysis (PCA) rely on concepts like singular value decomposition to extract meaningful, lower-dimensional representations of large datasets.
• Linear algebra is deeply embedded in the core of many ML algorithms. Neural networks and LLMs depend on efficient matrix operations, like matrix multiplications, to handle the massive computational demands of training and inference.

Nvidia folks are getting richer daily because they produce and sell the hardware (GPUs) and write open-source optimized software (Cuda) to perform efficient matrix operations!

Where to learn Linear Algebra?

• Professor Gilbert Strang’s MIT lectures — . You can use Sheldon Axler’s book as a reference book and practice exercises.
• 3Blue1Brown YouTube Channel —  on .
• Stanford University’s Probability for Computer Scientists lectures — . His videos are very engaging. You can follow his YouTube channel to learn other data science concepts. This channel is useful, especially for learning/revising statistics concepts.
• Sheldon Ross’s Book — . I undertook this course in 2019. It remains the best course for understanding basic ML. You can audit this course for free! This specialization doesn’t cover the math deeply but gives you an intuitive understanding of ML.
• Cornell Tech’s Applied Machine Learning Lectures —  on  and and is the best place to combine your ML and coding skills to solve ML problems. It’s important to get hands-on experience. Pick up any contest. Play with the dataset and apply the skills you learn from the lectures.
• Implementing ML algorithms without using special ML libraries like scikit-learn or PyTorch is a great self-learning exercise. Writing code from scratch for basic algorithms like PCA, gradient descent, and linear/logistic regression can help you enhance your understanding and coding skills.

During my Master’s in AI course at the Indian Institute of Science, Bengaluru, we had coding assignments where we implemented algorithms in C! Yes C! One of these assignments was about training a deep neural network for MNIST digits classification.

I built a deep neural network from scratch in C. I created a custom data structure for storing weights and wrote algorithms for gradient descent and backpropagation. I felt immense satisfaction when the C code ran successfully on my laptop’s CPU. My friend mocked me for doing this “impractical” exercise and argued that we have highly efficient libraries for such a task. Although my code was inefficient, writing the code from scratch deepened my understanding of the internal mechanics of deep neural networks.

You’ll eventually use libraries for your projects in academia and industry. However, as a beginner, jumping straight into libraries can prevent you from fully understanding the fundamentals.

Final Notes

Congratulations on making it this far in the article! We’ve covered the core skills necessary to become a data scientist. By now, I hope you have a solid understanding of why the basics are so important.

A Master’s degree from a reputed institution can provide structured learning on mathematics and ML concepts. It also offers opportunities to work on projects and gain practical experience. However, if pursuing a formal degree isn’t an option, don’t worry. You can follow the YouTube playlists and reference books mentioned earlier to self-learn.

Every expert was once a beginner. The key is to start small. Take it one step at a time, and gradually build your knowledge. Make sure not to skip any steps — start by mastering the math before moving on to applying it. Don’t rush the process. Focus on truly understanding each concept. Developing a strong foundation and thinking from first principles should always be your mantra. Over time, everything will begin to fall into place. With the right mindset, you’ll excel in this journey.

I highly recommend becoming a Medium member if you haven’t done so. You’ll unlock unlimited access to invaluable resources. Trust me, it’s a goldmine of knowledge! You’ll find insightful articles written by data science professionals and experts.

I hope you find my article interesting. Thank you for reading, and good luck in your data science journey!

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