Data Science Engineer Roadmap
Key Competencies and Knowledge
for a Successful AI Engineering Career
Trusted by
You Know
SQL
Watch Now
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Neural Networks
- Deep Learning
- Evaluation Metrics
- Model Optimization
- Feature Engineering
- Bias-Variance Tradeoff
- Model Deployment
- 9h 15m
Github
Watch Now
- Introduction to Git
- Git Setup
- Basic Commands
- Branching and Merging
- Working with Remotes
- Conflict Resolution
- Git Log and History
- Stashing Changes
- Tagging
- Best Practices
- 9h 15m
Python
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- Variables and basic data types
- Loops, and functions
- Error handling and exceptions
- File input/output operations
- Using Python modules and libraries
- Python classes and objects
- Python virtual environments
- Basic debugging techniques in Python
- 9h 15m
Advanced Python
(Pandas & NumPy)
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- Data Manipulation With Pandas
- HPC With Numpy
- Using Pandas For Time Series
- Complex Data Transformations
- Memory Management In Python
- Optimization For Code Performance
- Integrating Python With Databases
- Matplotlib And Seaborn
- Scripting For Automation
- Advanced Error Handling Techniques
- 9h 15m
Statistics & Analytics Techniques
Watch Now
- Descriptive Statistics
- Probability Theories And Applications
- What is Big Data
- Inferential Statistics For Decision Making
- Understanding Mean, Median, And Mode
- Measures Of Spread
- Shape Of The Distribution
- Data Visualization
- 9h 15m
Power BI
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- Desktop Navigation
- Data Connectivity
- Transforming Data
- DAX Fundamentals
- Report Building
- Visualization Tools
- Service Publishing
- Collaboration Sharing
- Mobile Applications
- DAX Advancement
- 9h 15m
Azure
PySpark
Watch Now
- Introduction to PySpark on Azure
- PySpark DataFrames
- What is Big Data
- Spark’s Basic Architecture
- Spark Toolset
- Spark Components
- Working with datasets using PySpark
- RDDs and transformations in PySpark
- DataFrames in PySpark
- Manipulating DataFrames
- 9h 15m
Azure Databricks
Watch Now
- Databricks Fundamentals
- Data Import and Export
- Cluster Management
- Notebooks in Databricks
- Data Engineering Pipelines
- Databricks SQL Analytics
- Stream Processing
- Machine Learning and AI
- Integrating with Azure Storage
- Security and Compliance
- 9h 15m
SQL
Watch Now
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Neural Networks
- Deep Learning
- Evaluation Metrics
- Model Optimization
- Feature Engineering
- Bias-Variance Tradeoff
- Model Deployment
- 9h 15m
Github
Watch Now
- Introduction to Git
- Git Setup
- Basic Commands
- Branching and Merging
- Working with Remotes
- Conflict Resolution
- Git Log and History
- Stashing Changes
- Tagging
- Best Practices
- 9h 15m
Statistics & Analytics Techniques
Watch Now
- Descriptive Statistics
- Probability Theories And Applications
- What is Big Data
- Inferential Statistics For Decision Making
- Understanding Mean, Median, And Mode
- Measures Of Spread
- Shape Of The Distribution
- Data Visualization
- 9h 15m
Power BI
Watch Now
- Desktop Navigation
- Data Connectivity
- Transforming Data
- DAX Fundamentals
- Report Building
- Visualization Tools
- Service Publishing
- Collaboration Sharing
- Mobile Applications
- DAX Advancement
- 9h 15m
Azure PySpark
Watch Now
- Introduction to PySpark on Azure
- PySpark DataFrames
- What is Big Data
- Spark’s Basic Architecture
- Spark Toolset
- Spark Components
- Working with datasets using PySpark
- RDDs and transformations in PySpark
- DataFrames in PySpark
- Manipulating DataFrames
- 9h 15m
Azure Databricks
Watch Now
- Databricks Fundamentals
- Data Import and Export
- Cluster Management
- Notebooks in Databricks
- Data Engineering Pipelines
- Databricks SQL Analytics
- Stream Processing
- Machine Learning and AI
- Integrating with Azure Storage
- Security and Compliance
- 9h 15m
Github
Watch Now
- Introduction to Git
- Git Setup
- Basic Commands
- Branching and Merging
- Working with Remotes
- Conflict Resolution
- Git Log and History
- Stashing Changes
- Tagging
- Best Practices
- 9h 15m
Statistics & Analytics Techniques
Watch Now
- Descriptive Statistics
- Probability Theories And Applications
- What is Big Data
- Inferential Statistics For Decision Making
- Understanding Mean, Median, And Mode
- Measures Of Spread
- Shape Of The Distribution
- Data Visualization
- 9h 15m
Power BI
Watch Now
- Desktop Navigation
- Data Connectivity
- Transforming Data
- DAX Fundamentals
- Report Building
- Visualization Tools
- Service Publishing
- Collaboration Sharing
- Mobile Applications
- DAX Advancement
- 9h 15m
Azure PySpark
Watch Now
- Introduction to PySpark on Azure
- PySpark DataFrames
- What is Big Data
- Spark’s Basic Architecture
- Spark Toolset
- Spark Components
- Working with datasets using PySpark
- RDDs and transformations in PySpark
- DataFrames in PySpark
- Manipulating DataFrames
- 9h 15m
Azure Databricks
Watch Now
- Databricks Fundamentals
- Data Import and Export
- Cluster Management
- Notebooks in Databricks
- Data Engineering Pipelines
- Databricks SQL Analytics
- Stream Processing
- Machine Learning and AI
- Integrating with Azure Storage
- Security and Compliance
- 9h 15m
Data Science Engineer Level Achieved!
Data Science Engineer
Average Salary
$1,62,000 year
Concept BASED Learning
Frequently Asked Questions
The Data Science Engineer Roadmap is a comprehensive learning program designed to help learners master the skills required for data analysis, data engineering, and machine learning. It covers data manipulation, statistical modeling, and deploying scalable data pipelines for real-world applications.
This program is ideal for:
- Data analysts and software developers seeking to advance their data engineering skills
- IT professionals aiming to transition into data-driven roles
- Students, freelancers, and beginners passionate about entering the field of data science
- Business professionals who want to leverage data for decision-making.
Basic knowledge of Python programming and a foundational understanding of data structures and statistics are recommended. The program starts with the fundamentals, making it suitable for beginners.
You’ll gain expertise in:
- Data manipulation with SQL, Pandas, and NumPy
- Data visualization using tools like Matplotlib and Seaborn
- Machine learning fundamentals
- Deploying data pipelines and models using cloud platforms
- Big Data tools such as Azure Databricks and PySpark.
The roadmap is designed to be completed in 3 to 5 months, depending on your learning pace and schedule.
Yes, the learning path includes several practical projects that challenge you to solve real-world problems using the data science skills you've acquired, enhancing both your understanding and your portfolio.
You’ll have access to:
- Industry expert mentorship sessions
- A peer community for collaboration
- Weekly technical Q&A sessions
- Regular guest lectures and webinars to stay updated with industry trends.
The program is delivered online with:
- Live interactive sessions
- On-demand video tutorials and downloadable materials
- Real-time mentoring and project support for an engaging learning experience.
Upon completion, you’ll receive an industry-recognized certification from BotCampus AI, validating your skills in data science and boosting your career prospects.
Visit the Data Science Engineer Roadmap page, select your plan, and enroll to start mastering data science today!