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12-Month Roadmap to Becoming a Data Scientist or Data Engineer

Are you ready to embark on a data-driven career path? Whether you’re eyeing a role in Data Science or Data Engineering, breaking into these fields requires a blend of the right skills, tools, and dedication. This 12-month roadmap lays out a step-by-step guide for acquiring essential knowledge and tools, from Python, ML, and NLP for Data Scientists to SQL, Cloud Platforms, and Big Data for Data Engineers. Let’s break down each path –

Data Scientist Roadmap: From Basics to Machine Learning Mastery

Months 1-3: Foundations of Data Science

  • Python: Learn Python programming (libraries like Pandas, NumPy, Matplotlib).
  • Data Structures: Understand essential data structures like lists, dictionaries, sets, and practical algorithms such as sorting, searching.
  • Statistics & Probability: Grasp basic math concepts (Linear Algebra, Calculus) and stats concepts (mean, median, variance, distributions, hypothesis testing).
  • SQL: Learn to query databases, especially for data extraction and aggregation.

Months 4-6: Core Data Science Skills

  • Data Cleaning and Preparation: Learn techniques for handling missing data, outliers, and data normalization.
  • Exploratory Data Analysis (EDA): Learn data visualization with Matplotlib, Seaborn, and statistical analysis.
  • Machine Learning (ML): Study fundamental algorithms (regression, classification, clustering) using Scikit-learn. Explore Feature Engineering and different types of ML models such as Supervised, Unsupervised
  • Git/GitHub: Master version control for collaboration and code management.

Months 7-9: Advanced Concepts & Tools

  • Deep Learning (DL): Introduction to DL using TensorFlow or PyTorch (build basic neural networks).
  • Natural Language Processing (NLP): Learn basic NLP techniques (tokenization, sentiment analysis) using spaCy, NLTK, or Hugging Face Transformers.
  • Cloud Platforms: Familiarize with AWS Sagemaker, GCP AI Platform, or Azure ML for deploying ML models. Learn about cloud services like compute, storage, and databases across all major hyperscalers including Databricks, Snowflake. Understand concepts like data warehouse, data lake, data mesh & fabric architecture.

Months 10-12: Model Deployment & Specialization

  • Model Deployment: Learn about basics of MLOps and model deployment using Flask, FastAPI, and Docker.
  • Large Language Models (LLM): Explore how LLMs like GPT and BERT are used for NLP tasks.
  • Projects & Portfolio: Build a portfolio of projects, from simple ML models to more advanced topics like Recommendation Systems or Computer Vision.

Data Engineer Roadmap: From SQL Mastery to Cloud-Scale Data Pipelines

Months 1-3: Basics of Data Engineering

  • SQL & Database Systems: Learn relational databases (PostgreSQL, MySQL), NoSQL databases (MongoDB, Cassandra), data querying, and optimization.
  • Python & Bash Scripting: Gain basic proficiency in Python and scripting for automation.
  • Linux & Command Line: Understand Linux fundamentals and common commands for system management.

Months 4-6: Data Pipelines & ETL

  • ETL (Extract, Transform, Load): Study ETL processes and tools like Airflow, Talend, or Informatica.
  • Data Warehousing & Data Lake: Learn about data warehousing concepts and tools like Snowflake, Amazon Redshift, or Google BigQuery. Look up recent trends around Data Mesh & Data Fabric.
  • Data Modeling: Understand data modeling techniques and design databases for large-scale systems. Ex: Dimensional modeling, data vault modeling

Months 7-9: Big Data Technologies

  • Big Data Ecosystems: Get hands-on experience with Hadoop, Apache Spark, or Databricks for distributed data processing.
  • Cloud Data Services: Learn how to build pipelines on AWS (S3, Lambda, Glue), Azure (Data Factory, Synapse), or GCP (Dataflow, BigQuery) for real-time and batch processing.
  • Data Governance: Understand data quality, security, and compliance best practices.

Months 10-12: Data Flow & Advanced Tools

  • Streaming Data: Learn real-time data processing using Apache Kafka or AWS Kinesis.
  • DevOps for Data Engineers: Explore automation tools like Docker, Kubernetes, and Terraform for scalable pipeline deployment.
  • Projects & Portfolio: Build end-to-end data engineering projects showcasing data pipeline creation, storage, and real-time processing.

Conclusion

Whether you choose the path of a Data Scientist or a Data Engineer, this roadmap ensures you build a solid foundation and then progress into more advanced topics, using the hottest tools in the industry like AWS, Azure, Databricks, Snowflake, LLMs, and more.

Shiva by — October 2, 2024 in AI in Business 0
T-shaped vs V-shaped path in your Analytics career

We start with learning multiple disciplines in an industry and then niche down to a specific skill that we master over the period of time to get expertise and become an authority in that space.

Typically, many including me follow a T-shaped path in the career journey where horizontal bar ‘T’ refers for wide variety of generalized knowledge / skills whereas vertical bar ‘T’ refers to depth of knowledge in a specific skill. For instance, if you’re a Data Scientist, you still do minimal Data Pre-Processing steps before doing the Exploratory Data Analysis, Model Training / Experimentation and Selection based on evaluation metrics. Although a Data Engineer or a Data Analyst, primarily works on data extraction, processing and visualization, a Data Scientist might still need to be familiar in order to get the job done on time without depending on the other team members.

Data Scientist’s vertical bar ‘T’ refers to crafting the best models for the dataset and horizontal bar ‘T’ could refer to Data processing (cleaning, transformation etc.) and visualizing the KPIs in the form of insights for the business to take informed decisions.

Strategy & Leadership consultant and author, Jeroen, comes up with a V-shaped path which makes sense in our contemporary economic situation where layoffs news are on the buzz across many MNC companies.

In terms of similarities, the author, reiterates that both models address the fact that understanding one focus area deeply and having shallow knowledge across other areas. V-shaped model refers to having one deep knowledge and a lot of adjacent knowledge areas which are not deeper but not shallow either, somewhere in between. Jeroen describes as, “It is medium-deep, medium-broad, enabling us to be versatile and agile.”

For illustration, if the Data Scientist aspires to go above and beyond the expectations, he/she can technically collaborate with Data Engineers, performs AI/ML modeling stuffs, builds reports/dashboards, generate meaningful insights, and enable end-user adoption of insights. It has a combination of hard and soft skills! Soft skills such as storytelling, collaboration with peers, project management etc., Over the period of time, as one repeats this whole process, they can get better and better (develop deeper knowledge) with model development and management, and develop adjacent soft skills to excel at work.

In my view, I think, we start with a T-shaped path and eventually, it morphs into a V-shaped career path as we put our hard-work on one skill and also develop its associated adjacent skills. And, it applies to any field that you’re in.

How long do you think it would take this transformation to attain a V-shaped path? Will this take about 10,000 hours (~a decade) as per Gladwell’s book: “Outliers” to become an expert? Maybe, yes! Sooner, the better it is!!

I’ll leave you with a three-phase approach to becoming an expert according to the author Jeroen.

Image Credits: https://www.linkedin.com/in/jeroenkraaijenbrink/

Shiva by — March 26, 2023 in Career 0