Step-by-Step Guide to Become a Data Scientist in 2023

Let me begin with a quick story of two friends, Peter and Henry.

Two young boys who lived in a small village – shared a common dream of becoming successful musicians. Despite facing many challenges and setbacks, they never gave up on their dream. Eventually, their hard work and determination paid off, as they landed a record deal and became household names, inspiring people worldwide with their music.

Now my question is: have you heard of these two musicians: Peter & Henry? You may search and look on the internet for their inspiring story. Well, what if I told you that these two real-looking pictures of Peter and Henry are created by an AI text-to-image model called DALL-E?

And their inspiring story – of becoming successful musicians – is written by another AI language model called ChatGPT. Surprising, isn’t it?

It may feel hard to believe, but Smart AI Systems like Uber and Netflix and fun apps like – Talking Tom are now a decade-old technology. This is how far AI has come in the year 2022. Watch this video to get your roadmap of becoming a Data Scientist in 2023 with Kunal Jain.

Introduction

There hasn’t been a better time to get into Data Science and build your career. And in this article, I will give you a complete step-by-step roadmap to becoming a Data Scientist in 2023. To become a Data Scientist in 2023, these are the skills you need need to master:

• Programming
• Statistics & Mathematics for Data Science
• Storytelling with Data
• Machine Learning, including
  • Supervised & Unsupervised Algorithms
  • Deep Learning
  • NLP and CV
• Deploying ML Models

Apart from these technical skills, you also need to work on your soft skills:

• Structured Thinking,
• Analytical Skills &
• Communication Skills (further including Spoken & Presentation Skills)

Are you feeling overwhelmed? Don’t worry; for you, we have curated a 12-month roadmap to acquire all these skills. For simplification, we have further divided the roadmap into 4 quarters. This roadmap is created, presuming you shall study for a minimum of 4 hours per day – 5 days a week.

If you follow this plan diligently, you should be able to:

First Quarter Goal: Getting into Data Science Role

In the First quarter, put your complete focus on learning: Programming Skills, Statistics for Machine Learning & Storytelling with Data.

January: Data Science Toolkit and Python

Python is the most popular programming language for data science for several reasons, including a large and active community, an extensive standard library, a wealth of third-party libraries, ease of use, and strong support for data manipulation and visualisation, all of which make it the most popular programming language for data science. This month, you will be covering the following: 

1. Python Basics
2. Python for Data Science
3. Regular Expressions
4. SQL for Database Querying)
   • Used to manage and manipulate data stored in relational databases

Head on to this free Python course.

February:  Statistics & Mathematics for Data Science

Statistics is the study of collecting, analysing and interpreting data. It involves descriptive statistics, probability, hypothesis testing, and regression analysis. We’ll focus mainly on Probability & Statistics as applied to business analytics and study the below topics:

1. Descriptive & Inferential Statistics
2. Probability 
3. Exploratory Data Analysis
4. Linear Algebra Basics
5. Data Interpretation

Download this Ebook on Statistics 101, which contains tutorials on most of the above topics.

March: Data Visualization and Exploration

Storytelling with Data is essential for data scientists because it effectively communicates their findings and insights to a broader audience. Using data visualization and clear, concise language, data scientists can create a narrative that helps others understand and appreciate the significance of their work. You will learn the below skills and master the art of visualizing the data.

1. Power BI/ Tableau/ Qlik Sense
2. Build Interactive Dashboards 
3. Design Dashboards
4. Logical Thinking

You could take a few project ideas from here to further practice the topics learned this quarter.

Second Quarter Goal: Begin competing in Data Science Competitions (on Kaggle, DataHack)

Congratulations if you have reached here! It means you are not stopping just after analyzing & visualizing the data at hand but also want to create some predictive models for future predictions. This is where the knowledge of machine learning and deep learning algorithms comes in. I propose the following sequence of topics to study this quarter:

April: Machine Learning and Story Telling

When you have past data with outcomes (labels in machine learning terminology) and want to predict future outcomes, you would use Supervised Machine Learning algorithms. There are times when you don’t want to predict an Outcome exactly. You want to perform a segmentation or clustering. For example – a bank would want to segment its customers to understand their behavior. This is an Unsupervised Machine Learning problem, as we are not predicting any outcomes here. Here are some of the topics that we will be covering this month.

1. ML Basics
2. Start building ML Modelsl
3. Supervised and Unsupervised Machine Learning
4. Data Storytelling

May: Advanced Machine Learning

This is the month when you need to strengthen your core skills and learn advanced machine-learning concepts. One of the most exciting topics is Time series, wherein you will be doing data visualization & decomposition into level trend & seasonality, how to move Average Models, and understand the framework to evaluate & cross-validate Time Series Models. Here is the detailed plan for the month:

1. Ensemble Basics
2. Bagging and Boosting Algorithms
3. Time series 
4. Working on Real-life Projects

The Machine Learning Certification Course for Beginners has most of the above topics. Finally, follow this end-to-end project on Loan Prediction to make your learning about ML concrete.

June: Recommendation Engine and Deep Learning 

Frankly, recommendation engines have made our lives easier. I love when Netflix showcases some options for TV series and movies basis my favourite genre. Don’t you want to learn how to develop a recommendation engine? This month we will focus on the recommendation engine and deep learning.

Deep Learning is all about using artificial neural networks (ANNs) to solve machine learning problems, especially image and text data classification & clustering, where the data and problems are more complex. There are 2 most used frameworks for building & training neural networks, namely TensorFlow (Keras) and PyTorch.

Check out the brief plan for the month below:

1. Recommendation Engine
2. Dimensionality Reduction Techniques
3. Deep Learning Neural Network, Transfer Learning
4. Start participating in ML Competitions 

After learning the above modules, you should be able to create end-to-end machine learning and deep learning models and start participating in Data Science competitions on Kaggle and DataHack.

I also suggest the following projects for practice for this quarter:

Moreover, now you can build advanced Machine Learning models and be proficient in feature engineering. The next logical question is how do I deploy my ML projects, which brings us to quarter 3

Third Quarter Goal: Apply for Entry-level Data Science Role

The focus of this quarter will be to master Software Engineering Concepts and Learn ML deployment in Production.

July: Software Engineering Skills

Have you ever heard of version control? It is one of the essential concepts in a data scientist’s daily role – yet most newcomers and beginners haven’t even come across it! You need to understand how to navigate through Git and GitHub if you want to make it as a data science professional. While many folks know about these tools (having used them for cloning open-source code from Google Research and other top data science organisations), they never really understand their real purpose. Many problems we face in data science while working remotely and independently will be erased with a quick understanding of Git and GitHub. Yes, this concept is that important!

1. Git and GitHub
2. Python OOPs concepts
3. Linux Commands
4. AWS/GCP/Azure

Follow this course on Git and GitHub for Data Science Professionals and get started.

August: Machine Learning Operations

The following important skill in the pipeline is learning the fundamentals of DevOps for data science, commonly called MLOps. This deals with deploying your ML and DL models into production, maintaining different versions of the models, monitoring them periodically, and re-training them whenever needed seamlessly. Here is this month’s itinerary:

1. Deploying models using Flask
2. Docker, Containers & Images + Creating an app using Streamlit 
3. Deploying with Third-party Servers: Heroku or Firebase
4. Join Data Science Community

September: Structured Thinking

This month, you will develop a skill that everyone craves, ‘Structured Thinking’. Well, in this chaotic world, we need to know how essential it has become to plan and structure our thoughts and actions. Especially as a Data Scientist, you must be clear with your problem statement and how to work for its resolution. You will develop this skill by working on different case studies as well. Here is your agenda:

1. Practice Guesstimates & Case Studies
2. Mind Mapping
3. Digital Profile Building (GitHub/LinkedIn)
4. Share Knowledge with the community

Fourth Quarter Goal: Begin Applying for Full-fledged Data Science Roles

Now is the time to specialize in specific industry use cases/domains if you want to choose. I suggest the two most common specialisations as per the industry requirements in the past 10 years:

• Computer Vision
• Natural Language Processing

October: Computer Vision with Deep Learning

Do you find the world of computers fascinating, especially when we can create various visuals? Computer vision uses Artificial Intelligence (AI) to train computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects — and then react to what they “see”. Below is your month’s plan:

1. Object Detection
2. Image Segmentation
3. Image Generation
4. Transfer Learning – Pre-trained Models (YOLOv7, VGC-19, Retinanet)   

November: NLP with Deep Learning

NLP is a subfield of artificial intelligence that focuses on the interaction between computers and human (natural) languages. There are many applications of NLP in the industry that you will be studying this month.

1. RNN/LSTM
2. Tokenization & Embeddings
3. Transformer Models
4. Transfer learning – Pre-trained models (BERT)

December – End-to-end ML Projects and Interview Prep

1. Image Classification and Object Detection
2. Fake News Detection
3. Communication Skills – Mock Interviews with Peers and Mirror Technique

After learning the above modules, you should be able to start applying for Data Scientist roles/deep learning engineers, Image Processing Engineers. This blog will help you find various books to improve your communication skills.

Suggested Projects for this quarter:

Conclusion

The tools and technologies used in data science are expected to continue to evolve and improve, leading to new and more efficient ways of working with data. Data scientists in 2023 must stay up-to-date with these developments to remain competitive. In summary, the future outlook for a data scientist in 2023 is bright, with continued growth in the volume and variety of data, increased adoption of machine learning and artificial intelligence, a greater focus on data ethics and privacy, more collaboration between data scientists and domain experts, and the continued evolution of data science tools and technologies. I’m attaching an infographic with this article, which you can download and keep track of.

All the best for your data science journey, cheers!